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 .
DETAILED ACTION
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/826,834 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the present invention are similar in scope and that that covered by that of the claims of copending Application No. 18/826,834 with different in wording variations.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 102
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.
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)(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.
(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 1-3, 9, 11-12 and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nandwana et al (2024/0087596).
Consider claims 1, 11 and 16, Nandwana et al teach a system, computer-implemented method and computer program product including a non-transitory computer-readable medium for sensitive data leakage prevention, the system and method comprising: a computing platform including: a memory; at least one computing processor device in communication with the memory (par. 0113); an image capturing device in communication with one or more of the at least one computing processor device (par. 0025; “The user device 115 may be a computing device that includes a memory, a hardware processor, and a camera. For example, the user device 115 may include a mobile device, a tablet computer, a mobile telephone, a wearable device, a head-mounted display, a mobile email device, a portable game player, a portable music player, a reader device, or another electronic device capable of accessing a network 105 and capturing images with a camera”); and a video call application in communication with the image-capturing device (par. 0026; “User device 115a includes metaverse application 104a and user device 115n includes metaverse application 104b. In some embodiments, the user device 115a is a sender device and user device 115n is a receiver device. In some embodiments, the user 125a generates a communication, such as an audio stream or a video stream, using the metaverse application 104a on the sender device and the communication is transmitted to metaverse engine 103”) and including Artificial Intelligence (AI), the video call application is stored in the memory, executable by one or more of the at least one computing processor device (par. 0022; 0024; 0074; “the database 199 may store data associated with the metaverse engine 103, such as training data sets for the trained machine-learning model, a history and metadata associated with each user 125, etc.”) and configured to: initiate a video call amongst a plurality of call participants (par. 0022; 0026; “User device 115a includes metaverse application 104a and user device 115n includes metaverse application 104b. In some embodiments, the user device 115a is a sender device and user device 115n is a receiver device. In some embodiments, the user 125a generates a communication, such as an audio stream or a video stream, using the metaverse application 104a on the sender device and the communication is transmitted to metaverse engine 103. Once the communication has been approved for transmission, the metaverse engine 103 transmits the communication to the metaverse application 104b on the receiver device for the user 125n to access”; “the training data used for the machine-learning model also includes video streams. The training dataset may be labelled to include examples of video streams with no offensive actions and video streams with one or more offensive actions, which enables the machine-learning module 210 to train the machine-learning model to classify input video streams as including offensive actions or no offensive actions, using the distinctions between offensive and non-offensive actions as labels during a supervised learning process”), implement the AI to detect sensitive data in at least one of a video feed or an audio feed being transmitted by a first call participant from amongst the plurality of call participants (par. 0021; 0042; 0074; “the metaverse engine 103 receives an audio stream from a user device 115a that is intended for a user device 115n. The metaverse engine 103 provides, as input to a trained machine-learning model, the audio stream and a speech analysis score, information about one or more voice emotion parameters, and one or more voice emotion scores for the user 115a associated with the user device 115a. The trained machine-learning model is iteratively applied to a respective portion of the audio stream, such as a few seconds of the audio stream as it is received”), and in response to detecting the sensitive data in at least one of the video feed or the audio feed being transmitted by the first call participant, perform one or more actions that prevent one or more other call participants participating in the video call from at least one of viewing or hearing the sensitive data in the video feed or the audio feed (par. 0022; 0084; 0089; 0101; “In some embodiments, the metaverse engine 103 uses the time delay to identify an instance of toxicity and replaces the instance of toxicity with a noise or silence before transmitting the audio stream to the one or more other user devices 115n”; “Because the graphic 305 draws attention to the offensive action, other mitigating actions are possible such as adding blur to the mouth or even replacing the mouth with pixels that match the background”; “In some embodiments, the level of toxicity may be so high that the toxicity module 212 mutes the user. For example, if the user uses expletives in every-other word, it might be easier to simply mute the audio stream until the user's tirade is complete”).
Consider claims 2-3, 12 and 17, Nandwana et al teach wherein the video call application including the AI further comprises the AI including computer vision and optical character recognition (OCR) techniques and wherein the video call application is further configured to implement the AI including the computer vision and the OCR techniques to detect the sensitive data (par. 0089-0090; “In some embodiments, the toxicity module 212 analyzes a video stream for instances of toxicity. Responsive to the toxicity module 212 identifying an instance of toxicity in the audio stream, the toxicity module 212 may analyze the video stream for offensive actions that occur within a predetermined time period of the instance of toxicity. For example, turning to FIG. 3, an example user interface 300 of a video stream is illustrated where an instance of toxicity in the audio stream is identified by the toxicity module 212. The toxicity module 212 performs image recognition on the video stream of a user's avatar and identifies a location within the video stream where the speaker's mouth moves to form the words that correspond to the instance of toxicity in the audio stream”), wherein the sensitive data is text indicia displayed in a background of the video feed (par. 0064; “the text module 208 compares the text to a list of toxic words and identifies instances of toxicity in the text. The text module 208 may generate a text score based on the text messages that indicates a toxicity rating for the text. The text module 208 may generate the text score periodically and transmit the text score to the machine-learning module 210”); and wherein the video call application is further configured to perform the one or more actions that prevent the one or more other call participants participating in the video call from at least one of viewing or hearing the sensitive data in the video feed or the audio feed, wherein the one or more actions includes obfuscating a region within the video feed where the text indicia appears (par. 0090; “the toxicity module 212 performs motion detection and/or object detection on the video to identify an offensive action. Responsive to identifying the offensive action, the toxicity module 212 blurs the offensive action in the video stream”).
Consider claim 9, Nandwana et al teach wherein the video call application is further configured to, in response to detecting sensitive data in at least one of the video feed or the audio feed being transmitted by the first call participant and prior to performing the one or more actions, receive permission from the first call participant to perform the one or more actions (par. 0042; “In some embodiments, after obtaining user permission, the history module 202 stores information about each communication session in the metaverse associated with a user and metadata associated with a user. The communication session may include audio streams, video streams, text communications, etc. After obtaining user permission, the history module 202 may store information about instances of toxicity associated with the user. For example, the history module 202 may identify when a user participated in instances of toxicity, what toxic behavior they performed (e.g., spoke a swear word, made an offensive action, bullied another user, threatened another user, etc.), identify a particular user that was targeted by the instance of toxicity, etc.”).
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 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.
Claims 4-8, 13-14 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nandwana et al (2024/0087596) in view of Varerkar et al (2019/0147175).
Consider claims 4-5, 13 and 18, Nandwana et al did not explicitly suggest wherein the video call application including the AI further comprises the AI including computer vision and facial recognition techniques and wherein the video call application is further configured to implement the AI including the computer vision and the facial recognition techniques to detect the sensitive data, wherein the sensitive data is at least one of (i) an actual individual in the video feed other than the first call participant and (ii) an image of individual in the video feed other than the first call participant, wherein the video call application is further configured to perform the one or more actions that prevent the one or more other call participants participating in the video call from at least one of viewing or hearing the sensitive data in the video feed or the audio feed, wherein the one or more actions includes obfuscating a region within the video feed where the actual individual or the image of the individual appears. In the same field of endeavor, Varerkar et al teach a system and method for detecting presence of an intruder (individual other than participant) during the audio/video conference using facial detection and automatically apply privacy filters such as deactivated, distorted or blurred out the detected intruder in the video stream (abstract; par. 0030; 0033; 0059). Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date to incorporate the teaching of Varerkar et al into view of Nandwana et al in order to protect video and audio data and enhance privacy.
Consider claims 6-8, 14 and 19, Nandwana et al did not explicitly suggest wherein the video call application including the AI further comprises the AI including computer vision and voice recognition techniques and wherein the video call application is further configured to implement the AI and the voice recognition techniques to detect the sensitive data, wherein detection includes identifying one or more secondary voices in the audio feed other than a voice of the first call participant and wherein the sensitive data is any audio coming from the identified one or more secondary voices, wherein the video call application further includes Natural Language Processing (NLP) and wherein the video call application is further configured to implement the AI, the voice recognition techniques and the NLP to detect the sensitive data, wherein detection further includes implementing the NLP to determine that the audio coming from the identified one or more secondary voices includes sensitive data, and wherein the video call application is further configured to perform the one or more actions that prevent the one or more other call participants participating in the video call from at least one of viewing or hearing the sensitive data in the video feed or the audio feed, wherein the one or more actions includes implementing noise reduction techniques to mute the audio coming from the identified one or more secondary voices. In the same field of endeavor, Varerkar et al teach a system and method for detecting presence of an intruder/intrusion during the audio/video conference using voice recognition to identify a number of locations of sources of user audio or a number of user voices (secondary voices) and automatically apply privacy filters such as deactivating a microphone, deactivating a speaker, deactivating a camera, deactivating a display, deactivating an application window, or deactivating any other sensor used in a device (abstract; par. 0030; 0033; 0049; 0060). Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date to incorporate the teaching of Varerkar et al into view of Nandwana et al in order to protect video and audio data and enhance privacy.
8. Claims 10, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nandwana et al (2024/0087596) in view of Qin et al (2020/0349953).
Consider claims 10, 15 and 20, Nandwana et al did not explicitly suggest wherein the video call application is further configured to: detect that the first call participant is not in the video feed or not a primary subject in the video feed, and in response to detecting that the that the first call participant is not in the video feed or not a primary subject in the video feed, pause or stop at least one of (i) capture of video by the image capture device or (ii) transmission of the video feed of the first call participant to the other call participants. In the same field of endeavor, Qin et al teach the system and method for managing audio-visual meetings. Upon detecting that a participant leaving or has left the conference, the server remove the audio/video stream from the meeting (par. 0055-0056). Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date to incorporate the teaching of Qin et al into view of Nandwana et al in order better manage of meeting as well as protecting the meeting from unauthorized access.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/Quoc D Tran/
Primary Examiner, Art Unit 2691
June 5, 2026
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