DETAILED ACTION
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).
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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. 11514342. Although the claims at issue are not identical, they are not patentably distinct from each other because Claims of patent application contain every element of claims above instant application or vice versa, and as such they anticipate or anticipated by Instant Application. As to Claims 1, 10, 19, of the Pat. *342 anticipates the claims of the instant application. By way of illustration, consider the respective claim 1 from each disclosure:
Claim 1 of the instant application
Claim 1 of the ‘342 Patent
1. A computer-implemented method comprising: receiving, by a first computing device and from a second computing device, a model that is configured to receive given media content and output data indicating whether the given media content includes deepfake content; receiving, by the first computing device, media content; determining, by the first computing device and using the model, whether the media content likely includes deepfake content; and providing, to a display of the first computing device, data indicating whether the media content likely includes deepfake content.
1. A computer-implemented method comprising: receiving, by a computing device, media data that represents an item of media content detected by a receiving device and location data that indicates a location of the receiving device; providing, by the computing device, the media data that represents the item of media content and the location data that indicates the location of the receiving device as an input to a model that is configured to determine whether the item of media content likely includes deepfake content; receiving, by the computing device and from the model, data indicating whether the item of media content likely includes deepfake content; and based on the data indicating whether the item of media content likely includes deepfake content, determining, by the computing device, whether the item of media content likely includes deepfake content.
Independent claims 1, 11, 20 of the instant application are substantially similar to independent claims 1, 10, 19, of the Pat. *342 and are rejected for substantially similar reasons as discussed supra. Likewise, dependent claims 2-10, 12-19 of the instant application are substantially similar to dependent claims 2-9, 11-18, 20 (respectively) of the Pat. *342 and are rejected for substantially similar reasons as discussed supra.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6, 7, 8, 10-14, 16-18, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mathews et al (Pub. No. US 2021/0097382).
As per claim 1, Mathews discloses a computer-implemented method comprising: receiving, by a first computing device and from a second computing device, a model that is configured to receive given media content and output data indicating whether the given media content includes deepfake content (…component interface obtains a media file from the AI trainer (or from the processing device)…the component interface inputs the media file into the deepfake classification model…the deployed deepfake classification model is implemented by deepfake analyzer…the deepfake analyzer obtains an input media file and generates an output identifying whether the input media file is “real” or “deepfake.”…see par. 26, 53-54); receiving, by the first computing device, media content (see par. 53); determining, by the first computing device and using the model, whether the media content likely includes deepfake content (…the deepfake classification model generates a classification score for the input media file…the classification score corresponding to whether the media file is real or a deepfake based on dataset used to train the deepfake classification model…see par. 53-55); and providing, to a display of the first computing device, data indicating whether the media content likely includes deepfake content (see the output device of the deepfake analyzer can be implemented by display device…the deepfake analyzer generates a report which includes the explainability mapping…see par. 26, 74).
As per claim 11, Mathews discloses a system, comprising: one or more processors; and memory including a plurality of computer-executable components that are executable by the one or more processors to perform a plurality of acts (fig.8), the plurality of acts comprising: receiving, by the system and from a second computing device, a model that is configured to receive given media content and output data indicating whether the given media content includes deepfake content (…component interface obtains a media file from the AI trainer (or from the processing device)…the component interface inputs the media file into the deepfake classification model…the deployed deepfake classification model is implemented by deepfake analyzer…the deepfake analyzer obtains an input media file and generates an output identifying whether the input media file is “real” or “deepfake.”…see par. 26, 53-54); receiving, by the system, media content (see par. 53); determining, by the system and using the model, whether the media content likely includes deepfake content (…the deepfake classification model generates a classification score for the input media file…the classification score corresponding to whether the media file is real or a deepfake based on dataset used to train the deepfake classification model…see par. 53-55); and providing, to a display of the system, data indicating whether the media content likely includes deepfake content (see the output device of the deepfake analyzer can be implemented by display device…the deepfake analyzer generates a report which includes the explainability mapping…see par. 26, 74).
As per claim 20, Mathews discloses one or more non-transitory computer-readable media of a first computing device storing computer-executable instructions that upon execution cause one or more processors to perform acts comprising: receiving, by the first computing device and from a second computing device, a model that is configured to receive given media content and output data indicating whether the given media content includes deepfake content (…component interface obtains a media file from the AI trainer (or from the processing device)…the component interface inputs the media file into the deepfake classification model…the deployed deepfake classification model is implemented by deepfake analyzer…the deepfake analyzer obtains an input media file and generates an output identifying whether the input media file is “real” or “deepfake.”…see par. 26, 53-54); receiving, by the first computing device, a first portion of media content (see par. 53); providing, by the first computing device, the first portion of the media content as a first input to the model (input the media file (media file from a training dataset during test) into deepfake classification model…see par. 53-54); receiving, by the first computing device and from the model, data indicating whether the first portion of the media content likely includes deepfake content (see par. 54-55); generating, by the first computing device, a user interface that indicates whether the first portion of the media content likely includes deepfake content (…the deepfake classification model generates a classification score for the input media file…the classification score corresponding to whether the media file is real or a deepfake based on dataset used to train the deepfake classification model…see par. 53-55); providing, to a display of the first computing device, the user interface (see the output device of the deepfake analyzer can be implemented by display device…the deepfake analyzer generates a report which includes the explainability mapping…see par. 26, 74); while providing, to the display of the first computing device, the user interface, receiving, by the first computing device, a second portion of the media content (the component interface may transmit a partially trained deepfake classification model to the deepfake analyzer…the deepfake analyzer can use a second portion of the dataset to test the accuracy of the partially trained deepfake classification model…see par. 61); providing, by the first computing device, the second portion of the media content as a second input to the model (see par. 63); receiving, by the first computing device and from the model, data indicating whether the first portion of the media content or the second portion of the media content likely includes deepfake content (…the deepfake classification model generates a classification score for the input media file…the classification score corresponding to whether the media file is real or a deepfake based on dataset used to train the deepfake classification model…see par. 53-55); and updating, by the first computing device, the user interface to include the data indicating whether the first portion of the media content or the second portion of the media content likely includes deepfake content (see par. 65-66).
As per claims 2, 12, Mathews discloses receiving, by the first computing device, data indicating whether the media content includes deepfake content; and providing, by the first computing device and to the second computing device, the media content, data indicating whether the media content includes deepfake content, and the data indicating whether the media content likely includes deepfake content (see par. 53-55).
As per claims 3, 13, Mathews discloses receiving, by the first computing device and from the second computing device, an additional model that is configured to receive the given media content and output additional data indicating whether the given media content includes deepfake content; receiving, by the first computing device, additional media content; determining, by the first computing device and using the additional model, whether the additional media content likely includes deepfake content; and providing, to the display of the first computing device, data indicating whether the additional media content likely includes deepfake content (see par. 37-38).
As per claims 4, 14, Mathews discloses wherein determining whether the media content likely includes deepfake content comprises: providing the media content as an input to the model; and receiving, from the model, the data indicating whether the media content likely includes deepfake content (see par. 53-55).
As per claims 6, 16, Mathews discloses receiving, by the first computing device and from the second computing device, sensor data (see audio sensor, par. 73) that reflects an attribute of a receiving device while the receiving device detected the media content or while the receiving device outputted the data that represents the media content, wherein determining whether the media content likely includes deepfake content is further based on the sensor data that reflects the attribute of the receiving device while the receiving device detected the media content or while the receiving device outputted the data that represents the media content (see par. 54-55).
As per claims 7, 17, Mathews discloses providing, to the display of the first computing device, a selectable option that provides the ability to input whether the media content includes deepfake content (see par. 26-27).
As per claims 8, 18, Mathews discloses determining, by the first computing device and using the model, a validation score that reflects a likelihood that the media content includes deepfake content; and providing, to the display of the first computing device, data that reflects the validation score (see par. 55).
As per claim 10, Mathews discloses receiving, by the first computing device and from the second computing device, an additional model that is configured to receive given additional media content and output data indicating whether the given additional media content includes deepfake content; and in response to receiving the media content, selecting, by the first computing device, the model from among the model, the additional model, and other models based on the model being configured to receive the given media content that is a same type of media content as the media content (see par. 32, 37-38).
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.
Claims 5, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mathews et al (Pub. No. US 2021/0097382) in view of Cheng et al (Pub. No. US 2020/0175260).
As per claims 5, 15, Mathews does not explicitly disclose receiving, by the first computing device and from the second computing device, biometric data that reflects an attribute of a user while a receiving device detected the media content or while the receiving device outputted the data that represents the media content, wherein determining whether the media content likely includes deepfake content is further based on the biometric data that reflects the attribute of the user while the receiving device detected the media content or while the receiving device outputted the data that represents the media content. However Cheng discloses receiving, by the first computing device and from the second computing device, biometric data that reflects an attribute of a user while a receiving device detected the media content or while the receiving device outputted the data that represents the media content, wherein determining whether the media content likely includes deepfake content is further based on the biometric data that reflects the attribute of the user while the receiving device detected the media content or while the receiving device outputted the data that represents the media content (…the image processing manager may identify a face in a first image based on identifying one or more biometric features of the face…determining a mapping function to determine whether a face in an image belongs to a real person or a fake-face image…see par. 76-77, 105). Therefore one ordinary skill in the art would have found it obvious before the effective filling date of the claimed invention to use Cheng in Mathews for including the above limitations because one ordinary skill in the art would recognize it would further improve anti-spoofing techniques by using machine learning to recognize images of actual faces and faked or spoofed faces…see Cheng, par. 4-5.
Claims 9, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mathews et al (Pub. No. US 2021/0097382) in view of Oliver et al (Pat. No. US 11101995).
As per claims 9, 19, Mathews does not explicitly disclose receiving, by the first computing device and from the second computing device, first location data that reflects a location of a receiving device while the receiving device detected the media content or while the receiving device outputted the data that represents the media content; and generating, by the first computing device, second location data that reflects the location of the first computing device; wherein determining whether the media content likely includes deepfake content is further based on the first location data or the second location data. However Oliver discloses receiving, by the first computing device and from the second computing device, first location data that reflects a location of a receiving device while the receiving device detected the media content or while the receiving device outputted the data that represents the media content; and generating, by the first computing device, second location data that reflects the location of the first computing device; wherein determining whether the media content likely includes deepfake content is further based on the first location data or the second location data (…the camera node requests the network access node for a location proof and an identifier of the network access node…the camera node receives, from the network access node, the location proof and the identifier of the network access node…the camera node generates video data, which comprises the video content, the hash of the video content, the identifier of the camera node, and time of recording of the video content…the network access node signs the location proof signed by the camera node, adds the double signed location proof to the video data as part of the recording metadata of the video content, and forwards the video data to the display node and to the verification node…see col.7 line 65-col.8 line 25…the display node may compare the recording metadata indicated in the video data to retrieved recording metadata to verify the video content…the display node may display the video content and/or display a success message (e.g., “VIDEO IS AUTHENTIC”) when the video content passes verification…otherwise, in response to the video content failing verification, the display node may stop/block displaying of the video content…see col.9 lines 31-45. Therefore one ordinary skill in the art would have found it obvious before the effective filling date of the claimed invention to use Oliver in Mathews for including the above limitations because one ordinary skill in the art would recognize it would further enhance the security of video content transmission in order to prevent cybercriminal activities using fake videos to access legitimate organization’s network, see Oliver, col.2 lines 20-30.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see PTO-form 892).
The following Patents and Papers are cited to further show the state of the art at the time of Applicant’s invention with respect to deepfake media content that is designed to deceive the user consuming the media content.
Ye (Pub. No. US 2021/0117650); “Fake Video Detection”;
-Teaches determining whether a video is genuine or is a fake generated by machine learning…see par. 2.
Brown et al (Pub. No. US 2021/0377205); “Methods, Systems, Apparatuses, and Devices for Facilitating Managing Digital Content Captured Using Multiple Content Capturing Devices”;
-Teaches facilitating managing digital content captured using multiple content capturing devices for capturing digital content simultaneously from two or more of point of views…see par. 8-9.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHAZAL B SHEHNI whose telephone number is (571)270-7479. The examiner can normally be reached Mon-Fri 9am-5pm PCT.
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/GHAZAL B SHEHNI/Primary Examiner, Art Unit 2499