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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/28/2026 has been entered.
Claim Status
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8, 11-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Karakotsios et al (US 10541000 B1) hereafter Karakotsios in view of Dolan et al (US 20230381665 A1) hereafter Dolan
Regarding claim 1, Karakotsios teaches a computer-implemented method, comprising: receiving data indicating a request for a plurality of media assets that comprise multiple media modalities (Column 7 lines 47-49, the first summarization parameters 130 may be provided by the user 104 that is requesting that the first video summarization 134 be generated); receiving one or more control signals (Column 5 line 30, The servers 110(1)-(P) may include the processor(s) 124); determining, using a machine-learned performance estimation model, one or more generated assets, wherein the machine-learned performance estimation model is configured to identify asset characteristics associated with historical performance data (Column 23 lines 51-56, The machine learning techniques described herein may learn characteristics of those styles/techniques 412 over time and generate specific summarization parameters and/or rules to subsequently generate video summarizations based on the styles/techniques); generating, using a machine-learned media asset generation pipeline, the plurality of media assets based on the one or more control signals by instructing a machine-learned asset generation model to generate media assets that align with the one or more control signals and by generating, using the machine-learned performance estimation model, an augmented input for input to the machine-learned media asset generation pipeline to induce the asset characteristics associated with the historical performance data (Column 29 lines 48-51, The remote computing resources 108 may utilize the video summarization preferences when generating subsequent video summarizations for the user 104 and/or other users 104); and sending, based on receiving data indicating selection of one or more of the plurality of media assets, the one or more of the plurality of media assets to a content item generation system for generating content items using the one or more of the plurality of media assets (Column 21 lines 62-65, the remote computing resources 108 may provide the first video summarization 134 to the user device 106 associated with the user).
Karakotsios does not appear to explicitly teach obtaining a data resource locator indicating a data resource; parsing the data resource to obtain pre-existing media assets; generating an augmented input to a machine learning model by changing a prompt input to the machine-learned asset generation model.
In analogous art, Dolan teaches obtaining a data resource locator indicating a data resource (Para 0031, The fine-tuning engine 114 and models 116 are operable to interact with the user actions via the user device 102, the instantiated agent(s) 108, and game training model(s) 124 in order to process the feedback data received form the various sources); parsing the data resource to obtain pre-existing media assets (Para 0031, The fine-tuning engine 114 and models 116 are operable to interact with the user actions via the user device 102, the instantiated agent(s) 108, and game training model(s) 124 in order to process the feedback data received form the various sources); generating an augmented input to a machine learning model by changing a prompt input to the machine-learned asset generation model (Para 0032, The output from the one or more models 116 is provided to the fine-tuning model 114 which can use the output to modify the prompts generated by prompt generator 112 in order to further personalize the instructions generated for the user based upon the agent's past interactions with the user). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Karakotsios to include the teaching of Dolan. One of ordinary skill in the art would be motivated to implement this modification in order to provide personalized media content, as taught by Dolan (Abs, Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users).
Regarding claim 2, Karakotsios in view of Dolan teaches the method of claim 1, wherein generating, using the machine-learned media asset generation pipeline, the plurality of media assets based on the one or more control signals comprises, for each respective modality of the multiple media modalities: instructing a respective machine-learned asset generation model associated with the respective modality to generate respective media assets that align with the one or more control signals (Karakotsios, Para 93, The remote computing resources 108 may use one or more machine learning techniques to determine characteristics of previously generated video summarizations and analyze data that represents user feedback relating to such video summarizations).
Regarding claim 3, Karakotsios in view of Dolan teaches the method of claim 1, wherein the multiple media modalities include two or more modalities selected from: text, image, or audio (Karakotsios, Para 39, the cameras 114 may include any type of camera 114 that is capable of capturing video and/or images).
Regarding claim 4, Karakotsios in view of Dolan teaches the method of claim 1, wherein the request is associated with a client account, and wherein the client account is associated with an account profile storing inputs to the machine-learned media asset generation pipeline (Karakotsios, Para 32, the first summarization parameters 134 may be determined based on a user profile associated with the user 104).
Regarding claim 5, Karakotsios in view of Dolan teaches the method of claim 4, wherein the account profile was retrieved from a database, and wherein the account profile was previously generated prior to the request (Karakotsios, Para 32, the first summarization parameters 134 may be determined based on a user profile associated with the user 104).
Regarding claim 6, Karakotsios in view of Dolan teaches the method of claim 1, comprising: parsing a web resource to extract visual style data associated with a client account, the visual style comprising color information, layout information, or typography information (Karakotsios, Para 33, The first video summarization 134 may be sent via a website, an application residing on the user device 106, an e-mail message, a text message, and so on).
Regarding claim 7, Karakotsios in view of Dolan teaches the method of claim 1, comprising: parsing a web resource to extract textual style data associated with a client account, the textual style data comprising an intonation or inflection of copy on the web resource (Karakotsios, Para 33, The first video summarization 134 may be sent via a website, an application residing on the user device 106, an e-mail message, a text message, and so on).
Regarding claim 8, Karakotsios in view of Dolan teaches the method of claim 1, comprising: parsing a web resource to extract landing page data associated a client account, wherein the landing page data comprises URLs to web pages associated with the plurality of media assets (Karakotsios, Para 33, The first video summarization 134 may be sent via a website, an application residing on the user device 106, an e-mail message, a text message, and so on).
Regarding claim 11, Karakotsios in view of Dolan teaches the method of claim 1, comprising: inputting, to a machine-learned media asset generation model, data from an account profile and a request for generated assets consistent with the data from the profile (Karakotsios, Para 32, the first summarization parameters 130 may be provided by the user 104 that is requesting that the first video summarization 134 be generated).
Regarding claim 12, Karakotsios in view of Dolan teaches the method of claim 1, comprising: and ranking, using the machine-learned performance estimation model, the generated assets from the machine-learned media asset generation model by using a machine-learned ranking model to rank assets based on an estimated performance of the asset (Karakotsios, Para 71, The video segment ranking module 316 may rank the video segments).
Regarding claim 13, Karakotsios in view of Dolan teaches the method of claim 1, comprising: presenting, on a user interface accessible by a client account, one or more generated media assets for review; receiving, via the user interface, inputs providing corrections to the one or more generated media assets; and re-generating, using the machine-learned media asset generation pipeline, the one or more generated media assets based on the received inputs (Karakotsios, Para 93, The remote computing resources 108 may use one or more machine learning techniques to determine characteristics of previously generated video summarizations and analyze data that represents user feedback relating to such video summarizations).
Regarding claim 14, Karakotsios in view of Dolan teaches the method of claim 1, wherein a media asset profile is based on at one or more features of the following features, the one or more features being associated with a client account: a machine-learned model, images, sitemap, logo, social media accounts, asset library, performance data, past sets of media assets, past sets of generated media assets (Para 33, The first video summarization 134 may be sent via a website, an application residing on the user device 106, an e-mail message, a text message, and so on).
Regarding claim 15, Karakotsios in view of Dolan teaches the method of claim 1, wherein the machine-learned media asset generation pipeline comprises a plurality of machine-learned media generators, a machine- learned optimizer, and a machine-learned ranker (Karakotsios, Para 93, The remote computing resources 108 may use one or more machine learning techniques to determine characteristics of previously generated video summarizations and analyze data that represents user feedback relating to such video summarizations).
Regarding claim 16, Karakotsios in view of Dolan teaches the method of claim 1, wherein the machine-learned media asset generation pipeline receives, via an asset feedback layer, inputs from a user to guide updates to or regeneration of at least one of the plurality of media assets (Karakotsios, Para 0031, The fine-tuning engine 114 and models 116 are operable to interact with the user actions via the user device 102, the instantiated agent(s) 108, and game training model(s) 124 in order to process the feedback data received form the various sources).
Regarding claim 17, Karakotsios in view of Dolan teaches The method of claim 1, wherein the machine-learned media asset generation pipeline receives, via a control layer, initial inputs from a user to guide generation of the plurality of media assets (Karakotsios, Para 0031, The fine-tuning engine 114 and models 116 are operable to interact with the user actions via the user device 102, the instantiated agent(s) 108, and game training model(s) 124 in order to process the feedback data received form the various sources).
Claim 19 is the media claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 20 is the system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Karakotsios in view of Dolan in view of Smith (US 20140168055 A1) hereafter Smith
Regarding claim 9, Karakotsios in view of Dolan teaches the method of claim 1, as shown above. However, Karakotsios in view of Dolan does not appear to explicitly teach generating at least one of the plurality of media assets by editing a pre-existing image asset using at least one of the following editing operations: crop, rotate, infill, recolor, defocus, deblur, denoise, relight; and wherein the editing operations are optionally implemented with machine-learned image editing tools.
In analogous art, Smith teaches generating at least one of the plurality of media assets by editing a pre-existing image asset using at least one of the following editing operations: crop, rotate, infill, recolor, defocus, deblur, denoise, relight; and wherein the editing operations are optionally implemented with machine-learned image editing tools (Para 0018, A virtual edit may be one or more or any combination of the following: add caption/text, alter, blend, brighten, change color exposure focus and or saturation, combine, contrast, crop, darken, delete, enhance, filter, manipulate, mask, modify, overlay, resize, rotate, scale, sharpen, soften, transform, translate, and or make some other virtual edit to the image display). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Karakotsios in view of Dolan to include the teaching of Smith. One of ordinary skill in the art would be motivated to implement this modification in order to perform image editing, as taught by Smith (Abs, Embodiments for a method and system for the display of virtual image edits are disclosed).
Regarding claim 10, Karakotsios in view of Dolan in view of Smith teaches the method of claim 9, wherein the pre-existing image asset is edited based on historical performance data associated with related image assets, and wherein the pre-existing image asset is edited based on a set of content item guidelines for generating content items using the pre-existing image asset (Para 0018, A virtual edit may be one or more or any combination of the following: add caption/text, alter, blend, brighten, change color exposure focus and or saturation, combine, contrast, crop, darken, delete, enhance, filter, manipulate, mask, modify, overlay, resize, rotate, scale, sharpen, soften, transform, translate, and or make some other virtual edit to the image display). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Karakotsios in view of Dolan to include the teaching of Smith. One of ordinary skill in the art would be motivated to implement this modification in order to perform image editing, as taught by Smith (Abs, Embodiments for a method and system for the display of virtual image edits are disclosed).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Karakotsios in view of Dolan in view of Davis et al (US 20080120294 A1) hereafter Davis
Regarding claim 18, Karakotsios in view of Dolan teaches the method of claim 1, as shown above. However, Karakotsios in view of Dolan does not appear to explicitly teach comprising: updating an account profile based on:(i) user inputs from a control layer;(ii) user feedback from an asset feedback layer, including asset selections, rejections/removals, manual edits/adjustments, corrections, and other inputs;(iii) pre-existing assets parsed from the data resource; or (iv) features generated from any one or combinations of (i)-(iii), including brand personality features, theme features, style features.
In analogous art, Davis teaches comprising: updating an account profile based on:(i) user inputs from a control layer;(ii) user feedback from an asset feedback layer, including asset selections, rejections/removals, manual edits/adjustments, corrections, and other inputs;(iii) pre-existing assets parsed from the data resource; or (iv) features generated from any one or combinations of (i)-(iii), including brand personality features, theme features, style features (Davis, Para 0028, the asset searching software system 110 can be configured to receive search input from a user for searching a plurality of media digital assets stored in a data store. The software system 110 determines an asset's relevance with respect to the received search input). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Karakotsios in view of Dolan to include the teaching of Davis. One of ordinary skill in the art would be motivated to implement this modification in order to handle media assets, as taught by Davis (Abs, Computer-implemented systems and methods for handling media assets in a networked environment).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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