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
1. 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 1-20 of U.S. Patent No. 12111880. Although the claims at issue are not identical, they are not patentably distinct from each other because claims features/limitations of instant application, is recited by patent claims.
For Example:
Instant Application claims.
Patent Application claims.
1. A computer-implemented method for changing a face within an image, the method comprising: receiving a first image including a face associated with a first facial identity; generating, via a machine learning model, at least a first texture map and a first position map based on the first image, wherein the machine learning model comprises a plurality of decoders, wherein each decoder included in the plurality of decoders is trained on images corresponding to a different facial identity; and rendering a second image including a face associated with a second facial identity based on the first texture map and the first position map, wherein the second facial identity is different from the first facial identity.
3. The method of claim 1, wherein the first texture map represents an average texture map corresponding to the second facial identity, and the second texture map represents one or more adjustments to the average texture map.
2. The method of claim 1, wherein the machine learning model generates a second position map that represents one or more adjustments to the first position map, and wherein the second image is further rendered based on the second position map.
3. The method of claim 1, wherein the first texture map represents an average texture map corresponding to the second facial identity, and the second texture map represents one or more adjustments to the average texture map.
4. The method of claim 1, wherein rendering the second image comprises generating a composite texture map based on the first texture map and the second texture map.
5. The method of claim 1, further comprising training the machine learning model based on a plurality of training input images, wherein the plurality of training input images includes one or more neutral input images associated with the first facial identity, one or more neutral input images associated with the second facial identity, one or more non-neutral input images associated with the first facial identity, and one or more non-neutral input images associated with the second facial identity.
6. The method of claim 5, wherein training the machine learning model comprises, for each training input image included in the plurality of training input images: generating, using the machine learning model, training output corresponding to the training input image; computing one or more of a reconstruction loss, a silhouette loss, or a smoothing loss based on the training output; and refining the machine learning model based on the one or more of the reconstruction loss, the silhouette loss, or the smoothing loss.
7. The method of claim 1, wherein the method further comprises training each decoder included in the plurality of decoders based on a different set of training images associated with the corresponding facial identity.
8. The method of claim 1, wherein rendering the second image comprises generating a 3D mesh based on the first position map, wherein rendering the second image is further based on the 3D mesh.
9. The method of claim 1, further comprising generating a vertex displacement map based on the first image, wherein generating the first texture map and the first position map is further based on the vertex displacement map.
10. One or more computer-readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: receiving a first image including a face associated with a first facial identity; generating, via a machine learning model, at least a first texture map and a first position map based on the first image, wherein the machine learning model comprises a plurality of decoders, wherein each decoder included in the plurality of decoders is trained on images corresponding to a different facial identity; and rendering a second image including a face associated with a second facial identity based on the first texture map and the first position map, wherein the second facial identity is different from the first facial identity.
12. The one or more computer readable media of claim 10, wherein the first texture map represents an average texture map corresponding to the second facial identity, and the second texture map represents one or more adjustments to the average texture map.
13. The one or more computer readable media of claim 10, wherein rendering the second image comprises generating a composite texture map based on the first texture map and the second texture map.
14. The one or more computer readable media of claim 10, further comprising training the machine learning model based on a plurality of training input images, wherein the plurality of training input images includes one or more neutral input images associated with the first facial identity, one or more neutral input images associated with the second facial identity, one or more non-neutral input images associated with the first facial identity, and one or more non-neutral input images associated with the second facial identity.
15. The one or more computer readable media of claim 14, wherein training the machine learning model comprises, for each training input image included in the plurality of training input images: generating, using the machine learning model, training output corresponding to the training input image; computing one or more of a reconstruction loss, a silhouette loss, or a smoothing loss based on the training output; and refining the machine learning model based on the one or more of the reconstruction loss, the silhouette loss, or the smoothing loss.
16. The one or more computer readable media of claim 10, wherein the machine learning model comprises a plurality of decoders, wherein each decoder included in the plurality of decoders corresponds to a different facial identity, the method further comprises training each decoder included in the plurality of decoders based on a different set of training images associated with the corresponding facial identity.
18. The one or more computer readable media of claim 10, further comprising generating a vertex displacement map based on the first image, wherein generating the first texture map and the first position map is further based on the vertex displacement map.
1. A computer-implemented method for changing a face within an image, the method comprising: receiving a first image including a face associated with a first facial identity; generating, via a machine learning model, at least a first texture map, a second texture map, and a first position map based on the first image, wherein the second texture map represents one or more adjustments to the first texture map; and rendering a second image including a face associated with a second facial identity based on the first texture map, the first position map, and the second texture map, wherein the second facial identity is different from the first facial identity.
2. The method of claim 1, wherein the machine learning model comprises an encoder and a plurality of decoders, wherein each decoder included in the plurality of decoders is associated with a different facial identity, and wherein generating the first texture map and the first position map uses a first decoder included in the plurality of decoders that is associated with the second facial identity.
15. The non-transitory computer-readable media of claim 11, wherein the first position map represents an average geometry corresponding to the second facial identity, wherein the instructions, when executed by the one or more processors further cause the one or more processors to perform the step of generating a second position map that represents one or more adjustments to the average geometry, and wherein rendering the second image is further based on the second position map.
4. The method of claim 1, wherein the first texture map represents an average texture map corresponding to the second facial identity, and the second texture map represents one or more adjustments to the average texture map.
5. The method of claim 1, wherein rendering the second image comprises generating a composite texture map based on the first texture map and the second texture map.
6. The method of claim 1, further comprising training the machine learning model based on a plurality of training input images, wherein the plurality of training input images includes one or more neutral input images associated with the first facial identity, one or more neutral input images associated with the second facial identity, one or more non-neutral input images associated with the first facial identity, and one or more non-neutral input images associated with the second facial identity.
7. The method of claim 6, wherein training the machine learning model comprises, for each training input image included in the plurality of training input images: generating, using the machine learning model, training output corresponding to the training input image; computing one or more of a reconstruction loss, a silhouette loss, or a smoothing loss based on the training output; and refining the machine learning model based on the one or more of the reconstruction loss, the silhouette loss, or the smoothing loss.
8. The method of claim 1, wherein the machine learning model comprises a plurality of decoders, wherein each decoder included in the plurality of decoders corresponds to a different facial identity, the method further comprises training each decoder included in the plurality of decoders based on a different set of training images associated with the corresponding facial identity.
9. The method of claim 1, wherein rendering the second image comprises generating a 3D mesh based on the first position map, wherein rendering the second image is further based on the 3D mesh.
10. The method of claim 1, further comprising generating a vertex displacement map based on the first image, wherein generating the first texture map and the first position map is further based on the vertex displacement map.
11. One or more non-transitory computer-readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: receiving a first image including a face associated with a first facial identity; generating, via a machine learning model, at least a first texture map, a second texture map, and a first position map based on the first image, wherein the second texture map comprises one or more adjustments to the first texture map; and rendering a second image including a face associated with a second facial identity based on the first texture map, the first position map, and the second texture map, wherein the second facial identity is different from the first facial identity.
12. The non-transitory computer-readable media of claim 11, wherein the machine learning model comprises an encoder and a plurality of decoders, wherein each decoder included in the plurality of decoders is associated with a different facial identity, and wherein generating the first texture map and the first position map uses a first decoder included in the plurality of decoders that is associated with the second facial identity.
13. The non-transitory computer-readable media of claim 11, wherein the first texture map represents an average texture map corresponding to the second facial identity, and the second texture map represents one or more adjustments to the average texture map.
14. The non-transitory computer-readable media of claim 11, wherein rendering the second image comprises generating a composite texture map based on the first texture map and the second texture map.
6. The method of claim 1, further comprising training the machine learning model based on a plurality of training input images, wherein the plurality of training input images includes one or more neutral input images associated with the first facial identity, one or more neutral input images associated with the second facial identity, one or more non-neutral input images associated with the first facial identity, and one or more non-neutral input images associated with the second facial identity.
7. The method of claim 6, wherein training the machine learning model comprises, for each training input image included in the plurality of training input images: generating, using the machine learning model, training output corresponding to the training input image; computing one or more of a reconstruction loss, a silhouette loss, or a smoothing loss based on the training output; and refining the machine learning model based on the one or more of the reconstruction loss, the silhouette loss, or the smoothing loss.
12. The non-transitory computer-readable media of claim 11, wherein the machine learning model comprises an encoder and a plurality of decoders, wherein each decoder included in the plurality of decoders is associated with a different facial identity, and wherein generating the first texture map and the first position map uses a first decoder included in the plurality of decoders that is associated with the second facial identity.
17. The non-transitory computer-readable media of claim 16, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to perform the step of generating a vertex displacement map based on the first image, and wherein generating the composite position map is further based on the vertex displacement map.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 10 is drawn to a computer readable- medium (also called machine readable medium or storage medium and other such variations) typically covers forms of non- transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, even though the specification does show, "non- transitory medium". Therefore claim 10 may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 US.C. § 101 by adding the limitation "non- transitory" to computer readable medium. Please see the memo regarding Eligibility of Computer Readable Media. (1351 OG 212 February 23, 2010). Examiner suggestion “One or more non-transitory computer-readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:”.
Allowable Subject Matter
3. The following is a statement of reasons for the indication of allowable subject matter: claims 1-20 would be in allowable condition, if applicant overcome the above rejections.
Reasons for Allowance
4. The following is an examiner’s statement of reasons for allowance: the closest prior art of Lui et al. US 20210390789 provide for, receiving a first image including a face associated with a first facial identity (see [0070], see "In the example shown in FIG. 2, the image processing system 100 can receive an input image 202 including a face, and can perform face detection to detect the face in the input image 202 (and/or a face region in the input image 202 including the face or a portion of the face), generating via a machine learning model (see [0072], see "In some cases, the UV position map prediction model 206 can include a machine learning system (e.g., a CNN) and can be implemented by the machine learning engine 122"), at least a first texture map and a first position map based on the first image (see Fig.2, [0074], see "As another example, the UV face position map 208 can include a 2D texture map that maps texture coordinates in the 2D texture map to position coordinates in 3D space"); and rendering a second image including a face associated with a second facial identity based on the first texture map and the first position map, wherein the second facial identity is different from the first facial identity (Fig.2 see output image 216, see [0082], see " For instance, the output image 216 can include a person from the input image 202 with a different (e.g., rotated) head pose. In one example, the output image 216 can depict the person in the input image 202 with the person's face rotated a certain amount, direction, angle").
Lui failed to teach or suggest for wherein the machine learning model comprises a plurality of decoders, wherein each decoder included in the plurality of decoders is trained on images corresponding to a different facial identity. As cited in independent claims 1,10 and 19.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Craft US 20210133160, is cited because the reference teaches “[0120] In another implementation, facial recognition may be applied to all faces in a selected scene or to a selected face in a scene. A copy of the scene or a copy of the image of the selected face may be communicated from the device (e.g., tablet 135, smartphone 120, computer 140 or smart television 175) to a server (e.g., server 155) for facial recognition processing using facial recognition software”.
Vaidya US 20190138795, is cited because the reference teaches “[0048] Face recognition information 116, which may comprise a name of one or more people recognized in captured video 112 is then transmitted by system for facial recognition 124, through network 110, to a user device 118, at which point it can be viewed by a user. In some embodiments, additional information may be transmitted with face recognition information 116, such as a copy of the face image from the captured video 112, and/or other information regarding the facial recognition”.
Wang et al. US 20190102608, is cited because the reference teaches “ Similarly, a fraudulent user trying to spoof the facial-recognition system may arrange the camera and a paper or electronic copy of the real user's face (e.g., a photograph of the real user's face or a computer screen displaying the real user's face) in such a way that the camera is ready to capture images of the displayed face of the real user”, in [0042].
Contact Information
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/ALI BAYAT/Primary Examiner, Art Unit 2677
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