CTNF 18/352,579 CTNF 84736 Detailed Action 1. Claims 1-16 are pending in this Application. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Election /Restriction 08-25-01 AIA 3 . Applicant’s election without traverse of Group I claims 1-16 in the reply filed on 03/31/2006 is acknowledged. 08-06 AIA 4 . Claim s17-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention , there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/31/2006 . Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 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. 07-07-aia AIA 07-07 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 - 07-08-aia AIA (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. 07-15 AIA 5 . Claim s1,3 6-9,11 and 14-16 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by A. Mitra et al. (hereafter Mitra), “ iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities”, IEEE ACCESE, pub. July 1, 2022 As to claim 1, Mitra teaches A processor comprising: one more circuits ( Fig.9, section 3, The attack detection module is the next module in the digital ID system pipeline, includes a convolutional networks c ircuit )), receive a first prompt indicating at least one feature for a first output and at least one characteristic of the at least one feature (Figs. 8-9; page 7179, section 3 , The deepfake detection (DD) module is used to detect if there is a deepfake attack on the system. A verification unit (represented by a diamond shape polygon shown in Fig.9A) receive face image and check if the face image is real image or feck image, wherein the deepfake detection (DD) module has been trained to detect deepfakes, generated by face swapping techniques ); determine the first output using a neural network and based at least on the at least one feature and the at least one characteristic ( as discussed above the verification unit associated to the deepfake detection (DD) module receive trained face image and determine if the face image is real image or feck image ); maintain a representation of the first output in a storage element (Figs. 8-9; page 7179, section 3, If the face image is real the face image, the image is saved into the presentation the verification model for farther verification, if the image is fake the verification unit reject the image) ; cause, using at least one of a display device or an audio output device, a presentation of the first output ( Fig.9, page 7179 section 3, the verification unit display “Yes” or “No” single that indicate the image is real image or fake image) ); receive subsequent to the presentation of the first output, a second prompt ( Fig.9, page 7179 section 3, the liveness detection uni t (represented by a diamond shape polygon shown in Fig.9A) receive the real image after proceeds by presentation attack detection model ); and determine, based at least on the representation of the first output and the second prompt, a second output ( Fig.9, page 7179 section 3, the liveness detection uni t determine the real image, after proceeds by presentation attack detection model, whether the real image is also a live image or not. The liveness detection unit output “Yes” or “No” single that indicate the image is real image or fake image) ); character or sentence As to claim 3, Mitra teaches the first output comprises at least one of image data, audio data, text data, music data, speech data, or video data (Fig.9A A verification unit (represented by a diamond shape polygon shown in Fig.9A) receive face image and check if the face image is real image or feck image ). As to claim 6, Mitra teaches the neural network is updated using a first database of first training data and a second database of second training data, the first training data comprising photographic image data, the second training data comprising a plurality of artistic images, each artistic image of the plurality of artistic images assigned at least one of an identifier of an artist of the artistic image or an identifier of a style class of the artistic image (Fig.9, page 7179 section 3, Fig.9 is designed to classify images as real images or deepfake, where deepfakes are AI generated fake images or videos that do not exist and can easily fool human eyes. The deepfake detection (DD) module is used to detect if there is a deepfake attack on the system; wherein the deepfake detection (DD) module has been trained to detect deepfakes, generated by face swapping techniques). As to claim 7, Mitra teaches the first prompt comprises content of at least one modality of a plurality of modalities, the plurality of modalities comprising at least one of a text modality (Fig.9, page 7179 section 3, the text signal “Yes” or “ No” that indicate the image is a real image or fake image) , a speech modality , an image modality or a video modality (the text signal “Yes” or “ No” that indicate the real image is an alive image (video) not ) ; and the first output comprises content of at least one output modality of the plurality of modalities different from the at least one modality of the first prompt (Fig.9, page 7179 section 3, the verification unit output “Yes” or “No” single that indicate the image is real image or fake image respectively. In addition in case of the image is real image, the verification unit output the real image data and send to the presentation attack detection model for further verification process, where the verification process determine if the real image is a live image or not, and display if the real image is also a live image) As to claim 8, Mitra teaches the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine (Figs.1 and 3); a perception system for an autonomous or semi-autonomous machine (Figs.1 and 3); a system for performing simulation operations ( Figs. 1 and 4); a system for performing digital twin operations ( Figs.4-5 and 9); a system for performing light transport simulation (Figs.1 and 3); a system for performing deep learning operations (Figs.5 and 9) ; a system implemented using an edge device ( Figs.1,5 and 9); a system implemented using a robot Figs.1 and 3); a system for generating synthetic data ( Figs.4-5 and 8-9); a system incorporating one or more virtual machines (VMs) (Fig.1); a system implemented at least partially in a data center( Figs.4-5 and 8-9); or a system implemented at least partially using cloud computing resources ( Figs.4-5 and 8-9). Claim 9 is rejected the same as claim 1 except claim 9 is directed to a system claim. All the limitations of claim 9 are addressed in claim 1. Thus, argument analogous to that presented above for claim 1 is also applicable to claim 9. Claim 11 is rejected the same as claim 3 except claim 11 is directed to a system claim. All the limitations of claim 11 are addressed in claim 3. Thus, argument analogous to that presented above for claim 3 is also applicable to claim 11. Claim 14 is rejected the same as claim 6 except claim 14 is directed to a system claim. All the limitations of claim 14 are addressed in claim 6. Thus, argument analogous to that presented above for claim 6 is also applicable to claim 14. Claim 15 is rejected the same as claim7 except claim 15 is directed to a system claim. All the limitations of claim 15 are addressed in claim 7. Thus, argument analogous to that presented above for claim 7 is also applicable to claim 15. Claim 16 is rejected the same as claim 8 except claim 16 is directed to a system claim. All the limitations of claim 16 are addressed in claim 8. Thus, argument analogous to that presented above for claim 8 is also applicable to claim 16 . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA 6. Claim s 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra, “iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities’” in in view of Gupta et al. ,( hereafter Gupta), US 20220392453 A1, pub. 12/08/ 2022. As to claim 4, Mitra, teaches one or more circuits are to iteratively modify the first according to a plurality of prompts (Figs. 3-4 and 8-9). However , it is noted that Mitra does not specifically teach “a conversational interface” On the other hand Gupta teaches a conversational interface ( [0096] [0147] In some circumstances, the identification server 102 may require additional voice sample audio data from the user. In these circumstances, the identity app presents a user interface prompting the user to provide additional voice samples (e.g., asked to speak additional sentences to collect more voice samples). In some embodiments, the identification server 102 or the provider server 106 employs a stepped approach to authentication). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate conversational interface methodology taught by Gupta into Mitra. The suggestion/motivation for doing so would have been Integrating the conversational interface introduced by Gupta enables Mitra's users to achieve intuitive interaction, autonomous iterative refinement, and increased system accessibility. Claim 12 is rejected the same as claim 4 except claim 12 is directed to a system claim. All the limitations of claim 12 are addressed in claim 4. Thus, argument analogous to that presented above for claim4 is also applicable to claim 12 . 07-21-aia AIA 7 . Claim s 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra, “iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities’” in in view of Diego Gragnaniello ( hereafter Diego), “Detection of AI-Generated Synthetic Faces”, Handbook of Digital Face Manipulation and Detection, pub., 2022 As to claim 5, Mitra, teaches the one or more circuits are to determine the second output by providing a concatenation of the first output and the second prompt as input ( Figs. 3-4 and 9). However, it is noted that Mitra, does not specifically teach “a denoising network of the neural network.” On the other hand Diego teaches a denoising network of the neural network (Fig. 9.4, page 195, 2 nd col., More specifically, for a generic image Xi generated by a given GAN a high-pass filter, i.e., a denoiser, is used to remove the semantic image content:). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a well- known high-pass filter, i.e., a denoiser, taught by taught by Diego into Mitra. The suggestion/motivation for doing so would have been to allows user Mitra to eliminate unwanted digital grain, sensor noise, and visual artifacts from images. Claim 13 is rejected the same as claim 5 except claim 13 is directed to a system claim. All the limitations of claim 13 are addressed in claim 5. Thus, argument analogous to that presented above for claim 5 is also applicable to claim 13 . 07-21-aia AIA 8 . Claim s 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra, “iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities’” in in view of LI Wan-Yu(hereafter LI), CN 112489652 A, pub. 03/12/2021. As to claim 2, Mitra, teaches the one or more circuits are to determine the first output by: determining, using a text completion model and based at least on the prompt, text data representative of the first prompt, ( Fig.9, page 7179 section 3, the verification unit output “Yes” or “No” text single that indicate the image is real image or fake image respectively. In addition in case of the image is real image, the verification unit output the real image data and send to the presentation attack detection model for further verification process, where the verification process determine if the real image is a live image or not, and display if the real image is also a live image). However , it is noted that Mitra, does not specifically teach “, text data representative of the first prompt, the text data having at least one of a greater length or a greater amount of information than the prompt, the text completion model updated using training data comprising text elements associated with completion elements longer than the text elements; and determining the first output, using the neural network, based at least on the text data.” On the other hand LI teaches text data representative of the first prompt, the text data having at least one of a greater length or a greater amount of information than the prompt, the text completion model updated using training data comprising text elements associated with completion elements longer than the text elements; and determining the first output, using the neural network, based at least on the text data ( page 4 last par., Encoder (encoder) -Decoder (decoder) structure, input is a sequence (Sequence), output is also a sequence; in Encoder, converting the sequence of variable length into the vector expression of fixed length, Decoder converts the vector of the fixed length into the signal sequence of the target of variable length, so as to realize the input of unfixed length to the output of unfixed length, for example, when the Chinese character is translated into Arabic numerals, The length of the Arabic numerals (output) may be shorter than the Chinese character (input), and may be longer than the Chinese character, and the length of the output is not determined. sequence to the sequence model may include a plurality of types, for example, based on the cyclic neural network (Recurrent Neural Network, RNN) seq2seq model and based on convolution operation (Convolution; CONV) neural network seq2seq model and so on; Optionally, in the present disclosure, the type of the neural network used by the sequence to the sequence model is not specifically limited. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate end-to-end voice processing innovation taught by taught by LI into Mitra. The suggestion/motivation for doing so would have been to allows user Mitra to dictate text rapidly, completing inputs much faster than typing on a physical or on-screen keyboard Claim 10 is rejected the same as claim 2except claim 10 is directed to a system claim. All the limitations of claim 10 are addressed in claim 2. Thus, argument analogous to that presented above for claim2 is also applicable to claim 10. Prior art not used in rejections but pertinent to the claims or disclosure . “ Preventing DeepFake Attacks on Speaker Authentication by Dynamic Lip Movement Analysis IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 16, 2021, to Chen-Zhao et al., disclosed: In this paper, a lip-based visual speaker authentication method is proposed to defend against both human imposters and DeepFake attacks. The proposed approach can differentiate DeepFake attacks without any prior knowledge of the video-generation method based on the assumption that the attackers only have limited information about the client (a small number of photos, a few talking videos, etc.) and cannot exactly reproduce the client’s talking habit when uttering random prompt texts. A new deep neural network, called SA-DTH-Net, is designed to extract information about the client’s unique talking habit in order to differentiate the client’s lip image sequence against human imposters and DeepFake forgeries. The final authentication result for a speaker uttering a random prompt text can be obtained by integrating all word-level authentication results derived from SA-DTH-Net. Experimental results demonstrated that the proposed approach can successfully reject most DeepFake attacks produced using different manipulation methods. Our approach can therefore provide a feasible solution for universal DeepFake detection in VSA systems (see .Figs.1,2 5, section V page 1852) Contact Information Any inquiry concerning this communication or earlier communication from the examiner should be directed to Mekonen Bekele whose telephone number is (469) 295-9077.The examiner can normally be reached on Monday -Friday from 9:00AM to 6:50 PM Eastern Time. If attempt to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Eng, George can be reached on (571) 272-7495.The fax phone number for the organization where the application or proceeding is assigned is 571-237-8300. Information regarding the status of an application may be obtained from the patent Application Information Retrieval (PAIR) system. Status information for published application may be obtained from either Private PAIR or Public PAIR. Status information for unpublished application is available through Privet PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have question on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866. /MEKONEN T BEKELE/ Primary Examiner, Art Unit 2699 Application/Control Number: 18/352,579 Page 2 Art Unit: 2699 Application/Control Number: 18/352,579 Page 3 Art Unit: 2699 Application/Control Number: 18/352,579 Page 4 Art Unit: 2699 Application/Control Number: 18/352,579 Page 5 Art Unit: 2699 Application/Control Number: 18/352,579 Page 6 Art Unit: 2699 Application/Control Number: 18/352,579 Page 7 Art Unit: 2699 Application/Control Number: 18/352,579 Page 8 Art Unit: 2699 Application/Control Number: 18/352,579 Page 9 Art Unit: 2699 Application/Control Number: 18/352,579 Page 10 Art Unit: 2699 Application/Control Number: 18/352,579 Page 11 Art Unit: 2699
Prosecution Insights