DETAILED ACTON
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. 14, filed 1/5/2026, with respect to the status of the claims is hereby acknowledged.
Applicant’s arguments, see Remarks pg. 14, filed 1/5/2026, with respect to the amendments to the specification are hereby acknowledged. The applicant’s amendments do not change the scope of the invention and are not considered new matter.
Applicant’s arguments, see Remarks pg. 14, filed 1/5/2026, 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/5/2026 is in compliance with the provisions of 37 CFR 1.97 and are being considered by the examiner.
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).
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; using video content data that corresponds to the content selection data, identify one or more target features that represent features of the video content data based on the user customization data; 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, generate customized video content data from the video content data based at least on applying a modification to one or more target features identified from the extracted content element data; and cause user equipment (UE) to present video content on a display based on the customized video content data, 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.
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.
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 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
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, and Crabtree 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
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 ALFONSO CASTRO whose telephone number is (571)270-3950. The examiner can normally be reached on Monday to Friday from 10am to 6pm.
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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