DETAILED ACTON
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/2/2026 has been entered.
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 Arguments
Applicant’s arguments, see Remarks pg. 12, filed 7/2/2026, with respect to the status of the claims is hereby acknowledged.
Applicant’s arguments, see Remarks pg. 12-14, filed 7/2/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 have been fully considered. The examiner notes that the applicant’s arguments are directed to the newly amended limitations not previously recited and are narrower in scope. Therefore, the applicant’s arguments are persuasive, however, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 5-19 are rejected under 35 U.S.C. 103 as being unpatentable over Zavesky; Eric US 20170186241 A1 (hereafter Zavesky) and in further view of Johnson; Neil Edward et al. US 20240323478 A1 (hereafter Johnson) and in further view of Crabtree; Jason et al. US 20250258685 A1 (hereafter Crabtree) and in further view of Baughman; Aaron K. et al. US 20200092610 A1 (hereafter Baughman).
Regarding claim 1, “a system for generating customized video content, the system comprising: one or more processors; and one or more computer-readable media storing computer-usable instructions that, when executed by the one or more processors, cause the one or more processors to: receive a request for streaming video content, wherein the request for streaming video content comprises content selection data and user customization data; obtain content data that corresponds to the content selection data, the content data comprising video content data and extracted content element data that indicates modifiable elements of the streaming video content; using the user customization data and extracted content element data as prompt inputs to a video generation model comprising a generative artificial intelligence (AI) machine learning model, identify for modification one or more target features that represent features of the video content data based on the user customization data; and generate an altered version of the streaming video content as customized video content data within which the one or more target features are modified in the customized video content data based on the user customization data; and cause user equipment (UE) to present video content on a display comprising the altered version of the streaming video content in response to the request for streaming video content” Zavesky para 10, 21, 34 systems and methods to digitally replace objects in images or video streams for final output to be displayed by client devices 170 based on context rules that correspond to user customization data wherein object matching rule may specify a threshold number of matching features of the first object 112 and the second object 162, a user defined set of matching rules, one or more context rules, or any combination thereof. The disclosed systems and methods may enable dynamic and seamless replacement of targeted objects (e.g., consumer products) in an image or video stream; during post-production of video content, the disclosed systems and methods may be utilized to automatically validate that all instances of a product (e.g., a soda can) abide by a rule established by a user and corresponds to content selection data established by a user data for customization. With respect to a video generation model Zavesky para 16-17 teaches three-dimensional (3D) model generator 120 coupled to a database 130. The 3D model generator 120 may be configured to generate a 3D model of an object (e.g., representative first object 112) depicted in video content 110 and to store the generated 3D model 132 of the first object 112 in the database 130. a modification module 150. The modification module 150 may be coupled to, integral with, or may be in communication with a user device 170. Zavesky para 21, 29-32 teaches target features which identify objects; object matching rule may specify a threshold number of matching features of the first object 112 and the second object 162, a user defined set of matching rules, one or more context rules, or any combination thereof. Zavesky does not reference a generative artificial intelligence (AI) machine learning model as claimed.
In an analogous art, Johnson provides a motivation for modifying the embodiments of Zavesky for performing the modification based on a user request comprising user preferences and the context and visual features of the targeted object wherein Johnson para 28-34, 54-56, 63-64, 79-80 teaches real-time insertion of objects into content during playback of the content, and more particularly to inserting personalized and localized objects in real-time into one or more frames of content during playback of the content based on identifiers of the one or more frames of a streamed video. Johnson does not reference a generative artificial intelligence (AI) machine learning model as claimed.
In an analogous art, Crabtree teaches a generative artificial intelligence (AI) machine learning model wherein learned or inferred preferences may be based on the analysis of user behavior, interactions/sessions, and historical data and further teaches that by analyzing the types of content a user consumes regularly (e.g., articles, videos, podcasts), the platform can infer the user's preferences and recommend similar content (para 93, 121-124, 250, 283). See also Crabtree para 81 enabling natural language interactions between the user and the AI. It may utilize natural language processing (NLP) and natural language generation (NLG) techniques to understand user inputs, maintain context, and provide intelligent and personalized responses.
As discussed above, with respect to “obtain content data that corresponds to the content selection data, the content data comprising video content data and extracted content element data that indicates modifiable elements of the streaming video content” and “identify for modification one or more target features that represent features of the video content data based on the user customization data; and generate an altered version of the streaming video content as customized video content data within which the one or more target features are modified in the customized video content data based on the user customization data; and cause user equipment (UE) to present video content on a display comprising the altered version of the streaming video content in response to the request for streaming video content” Baughman teaches [0083] In embodiments, methods of the present invention comprises: monitoring user behavioral responses (e.g., facial expressions, social media posts) to train portions of one or more audiovisual streams; generating, based on the responses, a training user response vector for each training portion; training, using the training response vectors, an artificial neural network to detect unwanted portions of content; monitoring user behavioral response to real-time portions of the real-time audiovisual stream; generating, based on the responses; a real-time user response vector for each real-time portion; analyzing, using the trained artificial neural network, the real-time user response vectors; detecting, based on the analysis, an unwanted real-time portion of content; and censoring the unwanted real-time portion of content by splicing a replacement portion of content in place of the unwanted real-time audiovisual stream. In aspects, the unwanted real-time portion of content is sound that is too muffled to be heard properly, wherein the replacement portion of content is a pre-recorded version of the sound that is clearer, and therein a director of the video stream is notified of the censoring action so that action can be taken to correct the issue going forward in the audiovisual stream (e.g. by fixing the microphone that made the muffled sound).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill to modify the teachings of Zavesky systems and methods to digitally replace objects in images or video for final output to be displayed by client devices based on context rules that correspond to user customization data wherein object matching rule may specify a threshold number of matching features of the objects, a user defined set of matching rules, one or more context rules because the prior art to Johnson teaches the benefit of utilizing a machine learning model for real-time insertion of objects into content during playback of the content, and more particularly to inserting personalized and localized objects in real-time into one or more frames of content during playback of the content based on identifiers of the one or more frames of a streamed video because the prior art to Crabtree teaches a generative artificial intelligence (AI) machine learning model wherein learned or inferred preferences may be based on the analysis of user behavior, interactions/sessions, and historical data and further teaches that by analyzing the types of content a user consumes regularly (e.g., articles, videos, podcasts), the platform can infer the user's preferences and recommend similar content. The combination of Zavesky, Johnson, and 20250258685 would be further modified by the teachings of Baughman for utilizing artificial intelligence for tailoring video streams to individual viewers by identifying portions of the video stream that are modifiable and modifying said portions based on viewer preferences.
Regarding claim 2, “the one or more processors further to instruct a content server to stream content data based on the content selection data, wherein the content data comprises at least the video content data” is further rejected on obviousness grounds as discussed in the rejection of claim 1 wherein Zavesky para 10, 21, 34 systems and methods to digitally replace objects in images or video streams for final output to be displayed by client devices 170 based on context rules that correspond to user customization data wherein object matching rule may specify a threshold number of matching features of the; see also Johnson para 28-34, 54-56, 63-64, 79-80 teaches real-time insertion of objects into content during playback of the content, and more particularly to inserting personalized and localized objects in real-time into one or more frames of content during playback of the content based on identifiers of the one or more frames of a streamed video.
Regarding claim 3, “wherein the video generation model comprises at least one of: a machine learning model, a generative artificial intelligence (GAI) model, a deep neural network (DNN), a generative adversarial network (GAN), or a variational autoencoder (VAE)” is further rejected on obviousness grounds as discussed in the rejection of claim 1-2 wherein Johnson para 28-34, 54-56, 63-64, 79-80 teaches machine learning model for real-time insertion of objects into content during playback of the content, and more particularly to inserting personalized and localized objects in real-time into one or more frames of content during playback of the content based on identifiers of the one or more frames of a streamed video.
Regarding claim 5, “wherein the one or more processors are further to: acquire the extracted content element data as data comprising a first plurality of content elements determined from the video content data; identify the one or more target features from the first plurality of content elements based on the user customization data; select a second plurality of content elements from a content element library based on the one or more target features; and using the video generation model, generate the customized video content data from the video content data based on applying the modification to the one or more target features based at least on the second plurality of content elements” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3 wherein Zavesky para 16-17 teaches three-dimensional (3D) model generator 120 coupled to a database 130. The 3D model generator 120 may be configured to generate a 3D model of an object (e.g., representative first object 112) depicted in video content 110 and to store the generated 3D model 132 of the first object 112 in the database 130. a modification module 150. The modification module 150 may be coupled to, integral with, or may be in communication with a user device 170. Zavesky para 21, 29-32 teaches target features which identify objects; object matching rule may specify a threshold number of matching features of the first object 112 and the second object 162, a user defined set of matching rules, one or more context rules, or any combination thereof.
Regarding claim 6, “wherein the one or more processors apply the video content data to a machine learning model to generate the extracted content element data” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3, 5 wherein Zavesky para 16-19, 29, 36 teaches three-dimensional (3D) model generator 120 coupled to a database 130. The 3D model generator 120 may be configured to generate a 3D model of an object (e.g., representative first object 112) depicted in video content 110 and to store the generated 3D model 132 of the first object 112 in the database 130. a modification module 150. The modification module 150 may be coupled to, integral with, or may be in communication with a user device 170. Zavesky para 21, 29-32 teaches target features which identify objects; object matching rule may specify a threshold number of matching features of the first object 112 and the second object 162, a user defined set of matching rules, one or more context rules, or any combination thereof. See also Johnson para 28-34, 54-56, 63-64, 79-80 teaches machine learning model for real-time insertion of objects into content during playback of the content, and more particularly to inserting personalized and localized objects in real-time into one or more frames of content during playback of the content based on identifiers of the one or more frames of a streamed video.
Regarding claim 7, “wherein the one or more processors modify the video content data, using the video generation model, further based on the extracted content element data representing individual features determined from the video content data” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3, 5-6 wherein Zavesky para 16-19, 21, 28-29, 36 teaches features of three-dimensional (3D) model generator 120 coupled to a database 130. The 3D model generator 120 may be configured to generate a 3D model of an object (e.g., representative first object 112) depicted in video content 110 and to store the generated 3D model 132 of the first object 112 in the database 130. a modification module 150. The modification module 150 may be coupled to, integral with, or may be in communication with a user device 170. Zavesky para 21, 28-32 teaches target features which identify objects; object matching rule may specify a threshold number of matching features of the first object 112 and the second object 162, a user defined set of matching rules, one or more context rules, or any combination thereof.
Regarding claim 8, “wherein the video generation model identifies the one or more target features of the video content data to modify based on the extracted content element data; and wherein the one or more target features are modified based at least in part on a matching of the one or more target features with content elements from a content library, based on a similarity” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3, 5-7 wherein Zavesky para 16-19, 21, 28-29, 36 teaches features of three-dimensional (3D) model generator 120 coupled to a database 130. The 3D model generator 120 may be configured to generate a 3D model of an object (e.g., representative first object 112) depicted in video content 110 and to store the generated 3D model 132 of the first object 112 in the database 130. a modification module 150. The modification module 150 may be coupled to, integral with, or may be in communication with a user device 170. Zavesky para 21, 28-32 teaches target features which identify objects; object matching rule may specify a threshold number of matching features of the first object 112 and the second object 162, a user defined set of matching rules, one or more context rules, or any combination thereof. See also Johnson para 28-34, 54-56, 63-64, 79-80 teaches machine learning model for real-time insertion of objects into content during playback of the content based on identified object features, and more particularly to inserting personalized and localized objects in real-time into one or more frames of content during playback of the content based on identifiers of the one or more frames of a streamed video.
Regarding claim 9, “wherein the one or more processors cause the UE to present the video content based on streaming the customized video content data to the UE via a network connection” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3, 5-8 wherein Johnson Fig. 1 element 118.
Regarding claim 10, “wherein the one or more target features may represent at least one of: objects, actors, characters, character behaviors, spoken content, sung content, character voice characteristics, languages, dialects, phrases, music, background settings, background sounds, and animals” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3, 5-9 wherein Zavesky para 24, 34 teaches actors.
Regarding claim 11, “wherein the video content data includes a combination of one or more video channels and one or more audio channels” is further rejected on obviousness grounds as discussed in the rejection of claim 1-3, 5-9 wherein Johnson para 38, 44-49, 47 teaches channels comprising audio and video.
Regarding the network claims 12-17 and the method claims 18-19 are grouped and rejected with the system claims 1-3, 5-11 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of system claims 1-3, 5-11 and because the elements of the system are easily converted into elements of computer implemented methods or network device claims by one of ordinary skill in the art. With respect to the element of claim 13-15, the prior art also discloses an edge server, and a worker node cluster as claimed. See Zavesky para 52 computer system 600 may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a distributed peer-to-peer or network environment. See also prior art discussed below to Iyer Fig. 6 disclosing an edge cloud configuration.
Claim(s) 4, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zavesky; Eric US 20170186241 A1 (hereafter Zavesky) and in further view of Johnson; Neil Edward et al. US 20240323478 A1 (hereafter Johnson) and in further view of Crabtree; Jason et al. US 20250258685 A1 (hereafter Crabtree) and in further view of BAUGHMAN; Aaron K. et al. US 20200092610 A1 (hereafter Baughman) and in further view of Iyer; Ravishankar et al. US 20200228880 A1 (hereafter Iyer).
Regarding claim 4, whereas Zavesky and Johnson disclose infer the user customization data, Zavesky and Johnson do not disclose “applying the request to a natural language processor. In an analogous art, Iyer teaches the deficiency in para 49, 51, 65-69 – utilizing artificial intelligence for object identification and replacement comprising natural language). Crabtree para 81 discloses enabling natural language interactions between the user and the AI. It may utilize natural language processing (NLP) and natural language generation (NLG) techniques to understand user inputs, maintain context, and provide intelligent and personalized responses. See also Baughman para 67-71 disclosing NLP.
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill to modify the teachings of Zavesky, Johnson, Crabtree, and Baughman for digitally replacing objects in images or video for final output to be displayed by client devices based on context rules that correspond to user customization data wherein object matching rule may specify a threshold number of matching features of the objects, a user defined set of matching rules, one or more context rules because the prior art to Iyer teaches utilizing artificial intelligence for object identification and replacement comprising natural language and the combination would apply known methods of analogous subject matter according to known techniques resulting in a high likelihood of success.
Regarding the method claims 20 is grouped and rejected with the system claim 4 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of system claim 1-11 and because the elements of the system are easily converted into elements of computer implemented methods or network device claims by one of ordinary skill in the art.
CONCLUSION
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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