DETAILED ACTION
Information Disclosure Statement
The references cited in the Information Disclosure Statement (IDS) filed on 11/11/24, 5/23/25 and 2/3/26 has/have been considered. However, based on the extensive number and the length of references cited, only a cursory review was made. Applicant is advised to provide which of the cited references and/or contents thereof are most pertinent to the instant application, if a detailed consideration is desired.
The information disclosure statement (IDS) submitted on 11/11/24, 5/23/25 and 2/3/26 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. However, it is noted that All Non-Patent Literature (NPL) citations need at least a month and year of publication: MPEP 609.04(a): The date of publication supplied must include at least the month and year of publication, except that the year of publication (without the month) will be accepted if the applicant points out in the information disclosure statement that the year of publication is sufficiently earlier than the effective U.S. filing date and any foreign priority date so that the particular month of publication is not in issue. NPL cited without at least the month and year of publication has been labeled with “no date available”.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this 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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
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Claim 1 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US Patent 12,154,313 B2. Although the claims at issue are not identical, they are not patentably distinct from each other.
Claim 1 of the instant application is anticipated by US Patent 12,154,313 B2 claim 1 in that claim 1 of the US Patent 12,154,313 B2 contain all the limitations of claim 1 of the instant application. Claim 1 of the instant application therefore is not patently distinct from the US Patent 12,154,313 B2 claim 1 and as such is unpatentable for obvious-type double patenting.
Claims of Instant Application 18/942,693
Claims of US PATENT 12,154,313 B2
1. A computing system comprising a generative neural network that is configured to:
receive a plurality of input videos, wherein each input video comprises a face defining an identity and wherein each input video comprises a behaviour, each input video comprising similar behaviour to each of the other input videos, and a different identity to each of the other input videos; and
synthesize an output video from the input videos, wherein the output video comprises a synthetic face defining a synthetic identity, wherein the generative neural network has been trained, with a loss function, to preserve the behaviour of the input videos while generating the synthetic identity so that the synthetic identity is different from each identity of the input videos.
1. A computing system comprising a generative neural network that is configured to:
receive a plurality of input videos, wherein each input video comprises a face defining an identity and a behavior, wherein the behavior is the same in each input video and the identity is different in each input video;
synthesize an output video from the input videos, wherein the output video comprises a synthetic face defining a synthetic identity, wherein the generative neural network has been trained, with a loss function, to preserve the behavior of the input videos while generating the synthetic identity so that the synthetic identity is different from each identity of the input videos;
wherein a term in the loss function is determined using a loss function neural network; wherein: i) a similarity term in the loss function is determined using a face recognizing neural network and optionally using a voice recognizing neural network; ii) a behavior term in the loss function is determined using a behavior estimating neural network; iii) a face term in the loss function is determined using a face detecting neural network and optionally a voice detecting neural network; iv) a consistency term in the loss function is determined using a face recognizing neural network and optionally a voice recognizing neural network.
Claim Interpretation
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: generative neural network in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof:
Referring to the specifications as filed, the generative neural network corresponds to Fig. 2-4 generative neural network 100. Furthermore, Fig. 4 shows the structure of a computing system, including an encoding neural network 4, an encryption module 6, a decryption module 8, a store 30 comprising a plurality of stored face videos (optionally comprising audio data of a voice), a decoding neural network 40, a generative neural network 100, and a statistical machine learner 50. Furthermore, the system includes client-side processor 1 and server-side processor 2.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites “wherein each input video comprises behaviour, each input video comprising similar behaviour …”. It is unclear as to what has a behaviour. For example, it is unclear if the video encompasses a behaviour, or if the face in the video encompasses a behaviour.
Claim 1 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. These claims recites “each video comprising similar behaviour” which raises the question as to what behaviours are considered “similar”. The claims are considered indefinite, since the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). Note the explanation given by the Board of Patent Appeals and Interferences in Ex parte Wu, 10 USPQ2d 2031, 2033 (Bd. Pat. App. & Inter. 1989), as to where broad language is followed by "such as" and then narrow language. The Board stated that this can render a claim indefinite by raising a question or doubt as to whether the feature introduced by such language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. Note also, for example, the decisions of Ex parte Steigewald, 131 USPQ 74 (Bd. App. 1961); Ex parte Hall, 83 USPQ 38 (Bd. App. 1948); and Ex parte Hasche, 86 USPQ 481 (Bd. App. 1949).
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meden et al (NPL: k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification; see IDS) in view of Yao et al (US 20210201078).
Regarding claim 1, Meden discloses a computing system comprising a generative neural network (page 11 4.1 Datasets: train the generative network needed for k-Same-Net) that is configured to:
receive a plurality of input videos, wherein each input video comprises a face defining an identity and wherein each input video comprises a behaviour, each input video comprising similar behaviour to each of the other input videos, and a different identity to each of the other input videos (page 11 4.1 Datasets: we use the RaFD dataset [54], which contains high-quality images of 67 subjects with eight different facial expression (i.e., anger, disgust, fear, happiness, sadness, surprise, contempt and neutral) per subject; page 12 We use the third dataset, CK+ [56,57], to demonstrate the data-utility-preservation capabilities of k-Same-Net. Specifically, we show how information on facial expressions can be preserved despite deidentification, thus enabling expression recognition on the deidentified data. The CK+ dataset comprises video sequences of 123 subjects expressing posed and non-posed (or natural) facial expressions/emotions, i.e., anger, disgust, fear, happiness, sadness, surprise and contempt.); and
synthesize an output video from the input videos, wherein the output video comprises a synthetic face defining a synthetic identity, wherein the generative neural network has been trained, with a loss function, to preserve the behaviour of the input videos while generating the synthetic identity so that the synthetic identity is different from each identity of the input videos (page 12 4.2 Network Training: Once the network is trained, it is able to output realistic, natural-looking facial images of size 640 x 512 corresponding to the identities from the training data or artificial, non-existing identities and displaying various facial expressions as seen in the examples in Figure 6).
While Meden implies that the generative neural network has been trained with a loss function, Meden does not specifically teach this.
Yao teaches wherein the generative neural network has been trained with a loss function (¶160 Once the neural network is structured, a learning model can be applied to the network to train the network to perform specific tasks. The learning model describes how to adjust the weights within the model to reduce the output error of the network. Backpropagation of errors is a common method used to train neural networks. An input vector is presented to the network for processing. The output of the network is compared to the desired output using a loss function and an error value is calculated for each of the neurons in the output layer.; ¶169 In addition to the basic CNN and RNN networks described, variations on those networks may be enabled; e.g. A deep belief network (DBN) is a generative neural network).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the generative neural network has been trained with a loss function from Yao into the system as disclosed by Meden. The motivation for doing this is to improve training of neural networks.
Conclusion
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/KEVIN KY/Primary Examiner, Art Unit 2671
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