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 .
Response to Amendment
The amendment filed 04/01/2026 has been fully considered and entered into record. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant's arguments filed 04/01/2026 have been fully considered but they are not persuasive for the reasons set forth below. A new search was conducted as a result of Applicant’s amendments, and the following references were discovered and are newly cited of record.
Applicant’s amendments to Claims 1-3, 5, 9, 11-13, 15 and 19 introduced new limitations not previously before the examiner, necessitating a new search and the citation of additional art references, specifically:
The vulnerable segment determination being a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list.
Modifying the vulnerable segment by overlaying an avatar associated with the user on the at least one vulnerable segment
Merging one or more encrypted frames with a video stream permission metadata.
These new limitations necessitated a new prior art search. The following references are newly cited in direct response to Applicant’s amendment.
Kwitek et al (US 11297021 B2) Predictive Privacy Screening And Editing Of Online Content
Allen et al (US 20180113587 A1) generating and displaying customized avatars in media overlays
Applicant argues that Randall fails to teach or otherwise disclose utilizing machine learning to output predictions of vulnerable segments of media content, and further fails to teach classifying the vulnerable segments for the purpose of overlaying an avatar over the vulnerable segments.
Applicant concludes that claims 1 and 11 are therefore novel over the art of record and in condition for allowance. Applicant’s argument is acknowledged.
Examiner agrees that Randall does not explicitly teach a machine learning model outputting predictions identifying a topic or activity associated with a user necessary to be added to a list. Accordingly, the 102 rejection of claims 1 and 11 over Randall alone is withdrawn.
However, Applicant’s argument does not overcome the newly presented 103 rejection over Randall in view of Kwitek et al (US 11297021 B2) and Allen et al (US 20180113587 A1) . Applicant cannot argue novelty over a combination of references under 35 U.S.C. § 103. Kwitek explicitly discloses a machine learning model trained to classify and predict topics and activities in media content items associated with specific users.
Examiner agrees that Randall does not explicitly disclose avatar overlay. However, this argument is directed solely at Randall and does not address the newly cited Allen et al (US 20180113587 A1) reference, which explicitly teaches generating customized avatars associated with specific users and overlaying those avatars on media content items. Avatar overlaying is a functionally equivalent privacy-preserving substitution for Randall’s redaction or blurring of sensitive content.
The § 112(b) rejection of claims 9 and 19 are withdrawn in view of Applicant’s amendments resolving the antecedent basis issue.
The rejections of dependent claims 4, 8, 14 and 18 under 35 U.S.C. § 102 over Randall are not maintained.
The rejections of dependent claims 6, 7, 10, 16, 17 and 20 under 35 U.S.C. § 103 over Randall in view of Owens are also not maintained because claims from which they depend are rejected under the new combination of references.
The rejections of amended dependent claims 2, 3, 5, 9, 12, 13, 15 and 19 under 35 U.S.C. § 103 as previously set forth in the Non-Final Office Action are not maintained. Therefore, new grounds of rejections for these amended dependent claims are set forth below, incorporating the newly cited references.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8, 11-15, and 18 are rejected under 35 U.S.C. §103 as being unpatentable over Randall et al. (US20230269422A1) [hereinafter “Randall”] in view of Kwitek et al (US 11297021 B2) [hereinafter “Kwitek”] and in view of Allen et al. (US 20180113587 A1) [hereinafter “Allen”].
As per claim 1, Randall discloses a computer-implemented method for privatizing media feeds comprising: receiving, by a computing device, a plurality of media data;( [ Randall, para [0051]” At a second block 204, the data processing system 100 selects portions of the image data on the basis of first data 306 indicating locations within the image data……The first data 306 may be implemented in a number of ways, for example, the first data 306 may include a copy of portions of the image data which are to be selected, pointers to the portions of the image data which are to be selected….”).
monitoring, by the computing device, the plurality of media data;( [Randall, para [0052]]” The selected portions of the image data represent objects in the frames of image data which are to be modified, for example, to redact or obscure information in the image data. [The image masks (the first data 306), may be generated automatically using object detection algorithms performed on the image data]. For example, image masks can be generated by processing the plurality of frames of the image data 304a to 304h using computer vision techniques to identify, (or detect), at least one predetermined class of object represented by the image data”)
determining, by the computing device, at least one vulnerable segment of the plurality of media data, [Randall, para [0052]] “The selected portions of the image data represent objects in the frames of image data which are to be modified, for example, to redact or obscure information in the image data. The image masks (the first data 306), may be generated automatically using object detection algorithms performed on the image data. For example, image masks can be generated by processing the plurality of frames of the image data 304a to 304h using computer vision techniques to [identify, (or detect), at least one predetermined class of object] represented by the image data.”). and modifying, by the computing device, the at least one vulnerable segment based on the determination([ Randall, para [0052]] “The selected portions of the image data represent objects in the frames of image data which are to be modified, for example, [to redact or obscure information in the image data]. The image masks (the first data 306), may be generated automatically using object detection algorithms performed on the image data. For example, image masks can be generated by processing the plurality of frames of the image data 304a to 304h using computer vision techniques to identify, (or detect), at least one predetermined class of object represented by the image data.”]).
Randall does not disclose wherein the at least one vulnerable segment is a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list; by overlaying an avatar associated with the user on the at least one vulnerable segment.
However, Kwitek in the same field of endeavor discloses wherein the at least one vulnerable segment is a prediction of a machine learning model, ( [Kwitek, (11)]” The machine learning process 140 learns from the results of the Autonomous rules, and the results of the human screening, to learn from the human screening more about which autonomous rules are properly written, which autonomous rules are suspect, and also to create new autonomous rules for scanning the new content.”) the prediction indicating( [Kwitek, (19)]” The software would recognize patterns and ultimately have predictive behavior elements”) the at least one vulnerable segment is a topic( [Kwitek, (8)]” scan posted materials for improper elements (nudity, curse words, hate speech, fake news and others)”) or activity ( [Kwitek, (15)]” illegal activities or trending events”) associated with a user ( [Kwitek, (14)]” The software…is able to list users or people potentially impacted by the postings or attempted postings”) necessary to be added to a list( [Kwitek, (13),(14)]” The system uses all of this to identify improper content. Once confirmed, the software maps the improper image or set of words at 145 and searches it out on the entire system at 150” and “This can also be used for identification and alert of stakeholders at 160. The software (with human support) is able to list users or people potentially impacted by the postings or attempted postings. This then allows for these people to be contacted”).
Therefore it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Randall to include wherein the at least one vulnerable segment is a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list as suggested by Kwitek. One of ordinary skill in the art would have been motivated to do so because incorporating Kwitek’s ML-based topic and activity classification into Randall’s vulnerable segment determination would predictably improve the accuracy and automation of identifying privacy-sensitive content in media.
The combination of Randall and Kwitek does not disclose overlaying an avatar associated with the user on the at least one vulnerable segment.
However, Allen in the same field of endeavor discloses overlaying( [Allen, [0045]]” applying the media overlay to the media content item (410)”) an avatar( [Allen, [0049]]” an “avatar” of a user is any visual representation of user. The avatar of a user may be based on characteristics derived from images of the user in conjunction with the avatar characteristics…”) associated with the user ( [Allen, [0048]]” avatar characteristics (404) for a user”)on the at least one vulnerable segment. ( [Allen, [0050]]” the media overlay in conjunction with a media content item”).
Therefore it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Randall to include wherein the at least one vulnerable segment is a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list as suggested by Kwitek to further include overlaying an avatar associated with the user on the at least one vulnerable segment as taught by Allen. One of ordinary skill in the art would have been motivated to do so because incorporating Allen’s avatar overlay technique into Randall’s media modification framework would predictably provide a privacy-preserving visual substitution for vulnerable content.
As per claim 2, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 1. Kwitek further discloses wherein determining the at least one vulnerable segment comprises: utilizing the machine learning model to classify one or more frames of the plurality of media data as vulnerable; ( [Kwitek, (8),(11)]” A computer 100, e.g. either server or client computer, first receives user content being posted online 105, from many different social media users 110. This run through an autonomous screening filter 115, which includes software to scan posted materials for improper elements (nudity, curse words, hate speech, fake news and others). The filter 115 can be updated from time to time, to learn different categories at different times” and “The results are passed through a Machine learning process 140. The machine learning process 140 learns from the results of the Autonomous rules, and the results of the human screening, to learn from the human screening more about which autonomous rules are properly written, which autonomous rules are suspect, and also to create new autonomous rules for scanning the new content.”).
Kwitek does not disclose encrypting and redacting, by the computing device, the one or more frames based on the prediction.
However, Randall discloses encrypting[Randall, [0112]]“ modifying the selected portions of the image data comprises encrypting the selected portions of the image data.”) and redacting, [Randall, [0112]]“ the modification of the selected portions of the image data by blurring, replacing, and/or redacting the selected portions of the image data, other examples are also envisaged.”) by the computing device, the one or more frames[Randall, [0003]]“ A video stream may generally be considered to be a sequence of digital signals, or packets of data, representing a plurality of frames of image data”) based on the prediction. Claim 2 is rejected under the same rationale as claim 1.
As per claim 3, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 2. Randall further discloses wherein at least one vulnerable material is determined within the one or more frames[Randall, [0094],[0052]]“ However, in other examples, it may be sufficient to modify a subset of the plurality of frames of the image data that include the sensitive information” and “ selected portions of the image data represent objects in the frames of image data which are to be modified”).
Randall fails to disclose by comparing, by the computing device, a plurality of user expressions to a pre-defined expressions list.
However, Kwitek discloses by comparing, by the computing device, a plurality of user expressions to a pre-defined expressions list. [Kwitek, claim1, (8)]“ operating to carry out a first autonomous screening of the content to use rules to determine whether the content meets a posting criteria, and categorizing the content as to whether the autonomous screening indicates that the content has met the posting criteria” and “autonomous screening filter 115, which includes software to scan posted materials for improper elements (nudity, curse words, hate speech, fake news and others). ”). Claim 3 is rejected under the same rationale as claim 2 above.
As per claim 4, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 1. Randall further discloses wherein modifying the at least one vulnerable segment comprises altering, by the computing device, a video stream element of the at least one vulnerable segment preventing it from being emitted in an original state. ([Randall, para. [0057], [0094], [0070]]:“ The data processing system 100 [modifies, at a third block 206], the selected portions of the image data to generate modified portions…”[0057]… “In the examples described above with respect to FIGS. 4 and 5, the selected portions of the image data which are processed to create the modified portions of the image data 308a to 308c are selected for every frame of the image data in which the [sensitive information to be removed is present]. However, in other examples, it may be sufficient to modify a subset of the plurality of frames of the image data that include the sensitive information which is to be removed. That is to say, the selected portions of the image data may correspond to a subset of the plurality of frames of image data 304a to 304h in the video stream 302 even when more frames of the image data in the video stream 302 contain the sensitive information…”[0094]….” Encrypting the second data 310 may, in some instances, include encrypting the second data 310 according to a single-use encryption key. A single-use encryption key allows the second data 310 to be decrypted once so that it can be used to recover the selected portions. Thereafter, the second data 310 is no longer able to be decrypted by a user to whom it is provided. This allows the selected portions of the image data to be recovered for a single limited and specific circumstance while maintaining the general security of the information represented by the second data 310. T[his is of particular use where the method 200 is implemented for a video stream 302 which contains highly sensitive information…]”[0070]. The examiner interprets the “ altering” of the vulnerable segment as including encryption of the selected portions of the image.
As per claim 5, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 3. Allen further discloses wherein modifying the at least one vulnerable segment comprises: generating, by the computing device, the avatar configured to emulate the at least one vulnerable material; [Allen, [0049]]“ As used herein, an “avatar” of a user is any visual representation of user.”)and overlaying, by the computing device, the avatar[Allen, claim 1]“ a processor; a user interface coupled to the processor, the user interface comprising an input device and a display screen; and memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to perform operations comprising: receiving a media content item; retrieving, from the memory, avatar characteristics associated with a user of the system; generating a media overlay, based on the user's avatar characteristics, that includes an avatar of the user; and displaying the media overlay with the media content item on the display screen of the user interface”).
Allen does not disclose over at least one of the one or more frames comprising the at least one vulnerable segment.
However, Randall discloses over at least one of the one or more frames comprising the at least one vulnerable segment. [Randall, [0094],[0052]]“ However, in other examples, it may be sufficient to modify a subset of the plurality of frames of the image data that include the sensitive information” and “ selected portions of the image data represent objects in the frames of image data which are to be modified”). Claim 5 is rejected under the same rationale as claimed 3.
As per claim 8, the combination of Randall, Kwitek and Allen discloses, the computer-implemented method of claim 1. Randall further discloses wherein the plurality of media data comprises one or more of video data, audio data, LIDAR data, sonar data, temperature data, and infrared data. ([Randall, para. [0003]]: “Video streams may also comprise other data, such as audio data, and metadata…” and further teaches that “Image sensors may operate in the visible light spectrum, but may additionally, or alternatively, include sensors which operate outside of the visible light spectrum, for example, the infrared spectrum.”[0038].
The examiner interprets this as disclosing that the “plurality of media data’ includes any data captured from an environment, and therefore reasonably encompasses video, audio, LIDAR, sonar, infrared, and temperature data under the broadest reasonable interpretation.
As per claim 11, Randall discloses a computer system for privatizing media feeds, the computer system comprising: one or more processors, one or more computer-readable memories, ;([Randall, para.[0045], [0046]]” The processor(s) 102 comprises any suitable combination of various processing units including a central processing unit (CPU), a graphics processing unit (GPU), an Image Signal Processor (ISP), a neural processing unit (NPU), and others. The at least one processor 102 may include other specialist processing units, such as application specific integrated circuits (ASICs), digital signal processors (DSPs), or field programmable gate arrays (FPGAs). For example, the processor(s) 102 may include a CPU and a GPU which are communicatively coupled over a bus. In other examples, the at least one processor 102 may comprise a CPU only”[0045]….” The storage 104 is embodied as any suitable combination of non-volatile and/or volatile storage. For example, the storage 104 may include one or more solid-state drives (SSDs), along with non-volatile random-access memory (NVRAM), and/or volatile random-access memory (RAM), for example, static random-access memory (SRAM) and dynamic random-access memory (DRAM) Other types of memory can be included, such as removable storage synchronous DRAM, and so on. The computer-executable instructions 106 included on the storage 104, when executed by the at last one processor 102, cause the processor(s) 102 to perform a computer-implemented method for processing a video stream as described herein. and program instructions stored on at least one of the one or more computer-readable memories for execution by at least one of the one or more processors to cause the computer system to: program instructions to receive a plurality of media data; program instructions to monitor the plurality of media data; program instructions to determine at least one vulnerable segment of the plurality of media data, ( [ Randall, para [0051]” At a second block 204, the data processing system 100 selects portions of the image data on the basis of first data 306 indicating locations within the image data……The first data 306 may be implemented in a number of ways, for example, the first data 306 may include a copy of portions of the image data which are to be selected, pointers to the portions of the image data which are to be selected….”).
monitoring, by the computing device, the plurality of media data;( [Randall, para [0052]]” The selected portions of the image data represent objects in the frames of image data which are to be modified, for example, to redact or obscure information in the image data. [The image masks (the first data 306), may be generated automatically using object detection algorithms performed on the image data]. For example, image masks can be generated by processing the plurality of frames of the image data 304a to 304h using computer vision techniques to identify, (or detect), at least one predetermined class of object represented by the image data”)
determining, by the computing device, at least one vulnerable segment of the plurality of media data, [Randall, para [0052]] “The selected portions of the image data represent objects in the frames of image data which are to be modified, for example, to redact or obscure information in the image data. The image masks (the first data 306), may be generated automatically using object detection algorithms performed on the image data. For example, image masks can be generated by processing the plurality of frames of the image data 304a to 304h using computer vision techniques to [identify, (or detect), at least one predetermined class of object] represented by the image data.”). and modifying, by the computing device, the at least one vulnerable segment based on the determination([ Randall, para [0052]] “The selected portions of the image data represent objects in the frames of image data which are to be modified, for example, [to redact or obscure information in the image data]. The image masks (the first data 306), may be generated automatically using object detection algorithms performed on the image data. For example, image masks can be generated by processing the plurality of frames of the image data 304a to 304h using computer vision techniques to identify, (or detect), at least one predetermined class of object represented by the image data.”]).
Randall does not disclose wherein the at least one vulnerable segment is a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list; by overlaying an avatar associated with the user on the at least one vulnerable segment.
However, Kwitek in the same field of endeavor discloses wherein the at least one vulnerable segment is a prediction of a machine learning model, ( [Kwitek, (11)]” The machine learning process 140 learns from the results of the Autonomous rules, and the results of the human screening, to learn from the human screening more about which autonomous rules are properly written, which autonomous rules are suspect, and also to create new autonomous rules for scanning the new content.”) the prediction indicating( [Kwitek, (19)]” The software would recognize patterns and ultimately have predictive behavior elements”) the at least one vulnerable segment is a topic( [Kwitek, (8)]” scan posted materials for improper elements (nudity, curse words, hate speech, fake news and others)”) or activity ( [Kwitek, (15)]” illegal activities or trending events”) associated with a user ( [Kwitek, (14)]” The software…is able to list users or people potentially impacted by the postings or attempted postings”) necessary to be added to a list( [Kwitek, (13),(14)]” The system uses all of this to identify improper content. Once confirmed, the software maps the improper image or set of words at 145 and searches it out on the entire system at 150” and “This can also be used for identification and alert of stakeholders at 160. The software (with human support) is able to list users or people potentially impacted by the postings or attempted postings. This then allows for these people to be contacted”).
Therefore it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Randall to include wherein the at least one vulnerable segment is a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list as suggested by Kwitek. One of ordinary skill in the art would have been motivated to do so because incorporating Kwitek’s ML-based topic and activity classification into Randall’s vulnerable segment determination would predictably improve the accuracy and automation of identifying privacy-sensitive content in media.
The combination of Randall and Kwitek does not disclose overlaying an avatar associated with the user on the at least one vulnerable segment.
However, Allen in the same field of endeavor discloses overlaying( [Allen, [0045]]” applying the media overlay to the media content item (410)”) an avatar( [Allen, [0049]]” an “avatar” of a user is any visual representation of user. The avatar of a user may be based on characteristics derived from images of the user in conjunction with the avatar characteristics…”) associated with the user ( [Allen, [0048]]” avatar characteristics (404) for a user”)on the at least one vulnerable segment. ( [Allen, [0050]]” the media overlay in conjunction with a media content item”).
Therefore it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Randall to include wherein the at least one vulnerable segment is a prediction of a machine learning model, the prediction indicating the at least one vulnerable segment is a topic or activity associated with a user necessary to be added to a list as suggested by Kwitek to further include overlaying an avatar associated with the user on the at least one vulnerable segment as taught by Allen. One of ordinary skill in the art would have been motivated to do so because incorporating Allen’s avatar overlay technique into Randall’s media modification framework would predictably provide a privacy-preserving visual substitution for vulnerable content.
As per claim 12, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 14, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 8. Accordingly, this claim is rejected under the same rationale.
Claims 7, 17 are rejected under 35 U.S.C. §103 as being unpatentable over Randall et al. (US20230269422A1) [hereinafter “Randall”] in view of Kwitek et al (US 11297021 B2) [hereinafter “Kwitek”] and in view of Allen et al. (US 20180113587 A1) [hereinafter “Allen”] as applied to claim 1 and further in view of Dufaux et al. (Scrambling for Privacy Protection in Video Surveillance Systems) [hereinafter (“Dufaux”)].
As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale.
As per claim 7, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 1. The combination fails to disclose determining, by the computing device, a receiving party for the plurality of media data including the modified vulnerable segment; and selecting, by the computing device, a variation of the plurality of media data based on determination.
However, Dufaux in the same field of endeavor discloses determining, by the computing device, a receiving party for the plurality of media data including the modified vulnerable segment; and selecting, by the computing device, a variation of the plurality of media data based on determination. ([Dufaux, pp. 1169-1170]” The same scrambled codestream is transmitted to all terminals independently from their access rights. Unauthorized clients, who do not possess the secret key, can only view a distorted version of the content where privacy-sensitive data is concealed. Conversely, authorized clients, e.g., law-enforcement authorities, can unscramble the codestream and recover the truthful scene”)
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify Randall to include determining, by the computing device, a receiving party for the plurality of media data including the modified vulnerable segment; and selecting, by the computing device, a variation of the plurality of media data based on determination as suggested by Dufaux. One of ordinary skill in the art would have been motivated to do so because Randall provides redacted content while Dufaux teaches a well-established privacy-preserving approach in which different viewing parties receive different visual variations of the same media based on authorization level. Incorporating Dufaux’s selective-access model into Randall would allow Randall’s redacted outputs to be tailored to specific viewer privileges enhancing the privacy-preservation system by aligning the degree of redaction with the viewer’s authorization rights.
Claims 6, 16 are rejected under 35 U.S.C. §103 as being unpatentable over Randall et al. (US20230269422A1) [hereinafter “Randall”] in view of Kwitek et al (US 11297021 B2) [hereinafter “Kwitek”] and in view of Allen et al. (US 20180113587 A1) [hereinafter “Allen”] as applied to claim 1 and further in view of Genay et al. (A. Genay, A. Lécuyer and M. Hachet, "Being an Avatar “for Real”: A Survey on Virtual Embodiment in Augmented Reality," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, pp. 5071-5090, 1 Dec. 2022, doi: 10.1109/TVCG.2021.3099290.) [hereinafter (“Genay”)] .
As per claim 6, the references as combined above disclose the computer-implemented method of claim 5. The combination of Randall, Kwitek and Allen does not explicitly disclose wherein the avatar is configured to be embedded in at least one of a virtual reality environment, an augmented reality environment, or a mixed reality environment.
However, Genay discloses wherein the avatar is configured to be embedded in at least one of a virtual reality environment, an augmented reality environment, or a mixed reality environment. ([Genay, abstract]” Virtual self-avatars have been increasingly used in Augmented Reality (AR) where one can see virtual content embedded into physical space.” Therefore, it would have been obvious before the effective filling date of the claimed invention for one of the ordinary skill in the art to modify Randall to further include the avatar is configured to be embedded in at least one of a virtual reality environment, an augmented reality environment, or a mixed reality environment as suggested by Genay. One of the ordinary skill in the art would have been motivated to do so because Randall teaches redacting or modifying vulnerable material, while Lui teaches generating avatars as replacements, and Genay teaches the use of avatars embedded into virtual, augmented, or mixed-reality environments for presenting privacy-preserving representations of individuals. Incorporating these teachings would have predictably improved Randall by enabling avatar-based privacy protection across additional digital environments such as VR/AR.
As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale.
Claims 9, 19 are rejected under 35 U.S.C. §103 as being unpatentable over Randall et al. (US20230269422A1) [hereinafter “Randall”] in view of Kwitek et al (US 11297021 B2) [hereinafter “Kwitek”] and in view of Allen et al. (US 20180113587 A1) [hereinafter “Allen”] as applied to claim 2 and further in view in view of Owens et al. (US 20160321260 A1) [hereinafter “Owens”].
As per claim 9, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 2. wherein modifying the at least one vulnerable segment comprises: merging, by the computing device, the plurality of media data one or more encrypted[Randall, [0094],[0070]]“ Encrypting the second data 310 may, in some instances, include encrypting the second data 310 according to a single-use encryption key. A single-use encryption key allows the second data 310 to be decrypted once so that it can be used to recover the selected portions”)frames[Randall, [0094]]“ the selected portions of the image data may correspond to a subset of the plurality of frames of image data 304a to 304h in the video stream 302 even when more frames of the image data in the video stream 302 contain the sensitive information”).
Randall does not disclose and the plurality of media data in a video stream comprising permission metadata; wherein the video stream is transmitted based on the permission metadata.
However, Owens in the same field of endeavor discloses the plurality of media data in a video stream comprising permission metadata; ( [Owens, [0029], [0030]])”The content analysis module 104 can analyze content items based on signals indicative of objectionable material in the content items. For example, the signals indicative of objectionable material can include a domain signal relating to a number of links to websites that identify the objectionable material as false, the links provided in response to content items published by a domain associated with a publisher of the content items. As another example, the signals indicative of objectionable material can include a keyword signal relating to a number of responses including one or more keywords indicative of objectionable material to a content item.” [0029] “Based on one or more of the signals indicative of objectionable material, a content item that likely contains objectionable material can be identified.” [0030]. wherein the video stream is transmitted based on the permission metadata([Owens, [0047]]” … The signal analysis module 206 in this regard can prioritize an affirmative decision of a user to receive content items from a domain over any contrary determinations regarding the presence of objectionable material in content items from the domain. In various embodiments, when a content item…… objectionable material”).
Therefore it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to modify the system created by the combination of Randall, Kwitek and Allen to further include the plurality of media data in a video stream comprising permission metadata; wherein the video stream is transmitted based on the permission metadata as suggested by Owens. One of the ordinary skill in the art would have been motivated to do so because incorporating Owens’s metadata-based modification and selective transmission, making the combination a predictable improvement for controlling privacy-conditioned media distribution.
As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 9. Accordingly, this claim is rejected under the same rationale.
Claims 10, 20 are rejected under 35 U.S.C. §103 as being unpatentable over Randall et al. (US20230269422A1) [hereinafter “Randall”] in view of Kwitek et al (US 11297021 B2) [hereinafter “Kwitek”] and in view of Allen et al. (US 20180113587 A1) [hereinafter “Allen”] as applied to claim 1 and claim 11 and further in view of Miller et al. (US 202000162236 A1) [hereinafter (“Miller”)].
As per claim 10, the combination of Randall, Kwitek and Allen discloses the computer-implemented method of claim 1. The combination does not explicitly disclose wherein the plurality of media data are maintained on a blockchain comprising a plurality of cryptographically linked blocks.
However, Miller in the same field of endeavor discloses wherein the plurality of media data are maintained on a blockchain comprising a plurality of cryptographically linked blocks.([Miller, para. [[0095], [0111], [0116], [0120]]” In various embodiments, and in response to a user request, the reference link to the copy of the document stored in the document repository may be accessed via the block. The accessed reference link may be employed, via document repository synchronizer 328, to retrieve and/or recall the stored copy of the document.” [0095]” That is, the current block may be added to a [distributed blockchain-like ledger]. The current block may include at least one of the current fingerprint of the image, a reference to a previous block included in the distributed ledger, or a fingerprint of the previous block. The previous block may encode a previous transaction that includes a previous fingerprint of the image that corresponds to the previous state of the image” [0111]. “Metadata associated with the document may be stored in the blockchain”. [0116]. “In various embodiments directed towards maintaining a distributed ledger across a plurality of nodes, ……. component is configured to run only when the image editing application is currently in use.” [0120]). ”
It would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to integrate into Randall the blockchain comprising a plurality of cryptographically linked blocks as suggested by Miller. One of ordinary skill in the art would have been motivated to do so because both references deal with managing sensitive visual data. Using a blockchain as taught by Miller would provide predictable benefits such as immutability, tamper detection, provenance tracking, and secure audit logs enhancements directly applicable to Randall’s output media management. Thus, applying Miller’s secure ledger to Randall’s media storage would represent a straightforward substitution of one known storage technique with another known secure alternative. Incorporating Miller’s blockchain storage into Randall’s would thus enhance integrity and traceability of redacted media data using a known secure-ledger approach.
As per claim 20, the substance of the claimed invention is identical or substantially similar to that of claim 10. Accordingly, this claim is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Modica et al., (US 20170358204 A1) discloses method, apparatus, and computer product for processing sensor data.
Tan et al., (US 20240054246 A1) discloses anonymizing caller identity based on voice print match.
Schoen et al., (US 20130151346 A1) discloses redacting portions of advertisements delivered to underage users.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Komi N. AMEVIGBE whose telephone number is (571)272-3381. The examiner can normally be reached Monday-Friday 2pm-10pm.
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/K.N.A./
Examiner, Art Unit 2493
/CARL G COLIN/Supervisory Patent Examiner, Art Unit 2493
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