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 .
Examiner’s Note
It is noted that non-functional descriptive material does not patentably distinguish over prior art that otherwise renders the claims unpatentable. See for example, MPEP 2111.05, MPEP 2112.01(III). See also In re Ngai, 367 F.3d 1336, 1339 (Fed. Cir. 2004); Exparte Nehls, 88 USPQ2d 1883, 1887-90 (BPAI 2008) (precedential) (discussing cases pertaining to non-functional descriptive material) see also BPAI’s decision in Appeal 2009-010851 (for Ser. No. 10/622,876) or BPAI’s decision in Appeal 2011-011929 (for Ser. No. 11/709,170), pages 6-7. In this case, a particular type of information such as “cut images”, “text” “narration generation-based information”, “narration information”….could be considered as non-functional descriptive material and are not required to give patentable weight because these particular types of data do not functionally change the structure or operation of a system of analyzing different types of data/information on the digital content to generate video content according a selection result. The limitations “cut images” “text corresponding to cut images” are only given patentable weight of different types of data/information.
Although non-functional descriptive material are not required to be considered, all claim limitations including non-functional descriptive material are known by prior art as discussed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-4, 6-7, 9-12, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over D’Auria (US 20240232937) in view of Ambrozic et al. (US 20210312318).
Note: it is noted that all documents that are directly or indirectly incorporated by references in their entireties in D’Auria (see paragraph 0001) or Ambrozic (see paragraphs 0032, 0033, 0035, 0036) are respectively treated as part of the specification of D’Auria or Ambrozic (see for example, MPEP 2163.07 b).
Regarding claim 1, D’Auria discloses an electronic device (video optimization system – see for example, figure 1) for generating video content using digital content based on a generative artificial intelligence model, the electronic device comprising:
a processor (e.g., processor 100- figure 1, paragraph 0092) configured to:
identify a plurality of cut images and text corresponding to the plurality of cut images by using digital content (identifying/recognizing a plurality of images and text corresponding to the images by using digital content of multimedia content – figures 3-5, 7, 35-37, paragraphs 0012, 0014, 0074, 0095, 0138);
input a first prompt including the plurality of cut images, the text, and analysis-based information into a generative artificial intelligence model to obtain analysis information on the digital content (input first prompt including a plurality of images, text, and analysis information/feature into generative artificial intelligence (GAI) model such as model 212,304,514,1604,1608,1614,1618, etc. to obtain analysis information/feature on the digital content of the multimedia content – see include, but are not limited to, figures 2-5, 31-32, 35, paragraphs 0073-0074, 0102-0104);
input a second prompt including video asset information comprising the plurality of cut images, the text, and the analysis information, and generation-based information into the generative artificial intelligence model to obtain sequence information composed of a plurality of sentences (input a second prompt of multimedia including video asset information of videos comprising plurality of images, text, and analysis information and story/sequence-generation based information in to the GAI model to obtain sequence information comprises a plurality of sentences such as “man walking a dog outside”, “woman jogging outdoors”, etc. – see include, but are not limited to, figures 2-5, 31-32, 35, paragraphs 0073-0074, 0102-0104, 0106, 0108-0109, 0173);
input a third prompt including the video asset information and matching-based information into the generative artificial intelligence model to select at least one cut image among the plurality of cut images to be matched to each of the sentences (input a third prompt including video asset information/feature to search and matching-based information into the GAI model to select at least one image among a plurality of images to be match each sentences in textual intermediaries – see include, but are not limited to, figures 2-5, 31-32, 35, paragraphs 0073, 0074, 0102-0104, 0106, 0108-0109); and
generate video content by using the plurality of cut images and the information according to a selection result (generate optimized multimedia/video content output by using the images and the information according to a selection result – see include, but are not limited to, figures 4, 6, 21-22, 31, 35, paragraphs 0182-0183).
D’Auria discloses narrator (see figure 22) and the integration of multi-sensory element into cohesive narratives (paragraph 0016). However, D’Auria does not explicitly disclose input second prompt includes narration-based information to obtain narration information.
Ambrozic discloses input a second prompt including video asset information comprising plurality of cut images, text, and analysis information, and narration generation-based information (narrative structure) into the generative artificial intelligence model to obtain narration information (input a prompt of content items comprising plurality of images, text, and analysis information and that follow a narrative structure into the generative model to obtain narrative structure/narration information – see include, but are not limited to, figures 1-4, 7-8, paragraphs 0004-0005, 0024-0027, 0032); and
generate video content by using the plurality of cut images and the narration information according to a selection result (generate a curated video content in playlist by using the images and the narration information/structure according to a selection result– see include, but are not limited to, figures 1-4, 7-10, paragraphs 0024-0027, 0032).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify D’Auria with the teaching of input of prompt including video asset information including narration generation-based information to obtain narration information as taught by Ambrozic in order to yield predictable result of keeping a user engaged in the content or defining order of curate content (see paragraph 0002).
Regarding claim 2, D’Auria in view of Ambrozic discloses the electronic device of claim 1, wherein the analysis information is analysis information on at least one of a character (e.g., man, woman, dog, character, actor, etc.) or a scene for each cut image of the digital content (see include, but are not limited to, D’Auria: figures 3-4, 22, paragraphs 0013, 0130; Ambrozic: paragraphs 0024, 0029).
Regarding claim 3, D’Auria, in view of Ambrozic discloses the electronic device of claim 1, wherein the processor is configured to input a fourth prompt including the video asset information and synopsis generation-based information (summary/overview or brief description, a sentence such as “man walking a dog outside”, a basic scene description, a playlist, etc.) into the generative artificial intelligence model to obtain synopsis information on the digital content (see include, but are not limited to D’Auria: figures 3-7, paragraph 0013, 0015, 0182, 0238; Ambrozic: figures 7-10, paragraph 0024).
Regarding claim 4, D’Auria in view of Ambrozic discloses the electronic device of claim 1, wherein the processor is configured to input a fifth prompt including the video asset information and character analysis-based information (character/actor analysis-based information) into the generative artificial intelligence model to obtain character information (character/actor) on the digital content (see include, but are not limited to, D’Auria: figures 3-4, 22, paragraphs 0013, 0130; Ambrozic: paragraphs 0024, 0029).
Regarding claim 6, D’Auria in view of Ambrozic discloses the electronic device of claim 1, wherein the matching-based information comprises at least one candidate cut image to be matched to each of the sentences and a score for the candidate cut image (matching/comparing at least one image of multimedia to each sentences and a score under target column/rank – see include, but are not limited to, D’Auria: figures 6, 8, 11, 15-16, 31-34, paragraphs 0108, 0246; Ambrozic: figures 3-4, 8-10, paragraphs 0009-0011, 0024, 0027, 0039).
Regarding claim 7, D’Auria in view of Ambrozic discloses the electronic device of claim 1, wherein the processor is configured to input a sixth prompt including the video asset information and use conditions for each image effect (e.g., color, pattern, texture, light, etc.) into the generative artificial intelligence model to select an image effect corresponding to each of the plurality of cut images (see include, but are not limited to, D’Auria: paragraphs 0007, 0106, claim 1; Ambrozic: paragraphs 0024, 0032, 0035).
Regarding claim 9, limitations of a method that corresponds to the limitations of the electronic device in claim 1 are analyzed as discussed in the rejection of claim 1. In particular, D’Auria in view of Ambrozic discloses a method performed by an electronic device for generating video content using digital content based on a generative artificial intelligence model, the method comprising:
identifying a plurality of cut images and text corresponding to the plurality of cut images by using digital content;
inputting a first prompt including the plurality of cut images, the text, and analysis -based information into a generative artificial intelligence model to obtain analysis information on the digital content;
inputting a second prompt including video asset information comprising the plurality of cut images, the text, and the analysis information, and narration generation-based information into the generative artificial intelligence model to obtain narration information composed of a plurality of sentences;
inputting a third prompt including the video asset information and matching-based information into the generative artificial intelligence model to select at least one cut image among the plurality of cut images to be matched to each of the sentences; and
generating video content by using the plurality of cut images and the narration information according to a selection result (see similar discussion in the rejection of claim 1).
Regarding claims 10-12, 14-15, the additional limitations that correspond to the additional limitations of the electronic device of claims 2-4, 6-7 are analyzed as discussed in the rejection of claims 2-4, 6-7.
Claims 5, 8, 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over D’Auria (US 20240232937) in view of Ambrozic et al. (US 20210312318) as applied to claim 1 or claim 9 and further in view of Verma et al. (US 20210193109).
Regarding claim 5, D’Auria in view of Ambrozic discloses the electronic device of claim 1, wherein the processor is configured to process the narration information (see include, but are not limited to, Ambrozic: figures 1, 7-8). However, D’Auria does not explicitly disclose the processor is configured to convert information into audio content.
Verma discloses processor is configured to convert narration information into audio content (convert text of narrator into audio/speech – see include, but are not limited to, paragraphs 0107, 0114).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify D’Auria in view of Ambrozic with the teaching of converting narration information into audio content as taught by Verma in order to yield predictable result of improving convenience for audience or providing user with a feeling of being present in the audio playback (paragraph 0003).
Regarding claim 13, additional limitations of the method that correspond to the additional limitations of the electronic device of claim 5 are analyzed as discussed in the rejection of claim 5.
Regarding claim 8, D’Auria in view of Ambrozic discloses the electronic device of the electronic device of wherein the processor is configured to: identify at least one keyword (keyword, word, text, etc. in text mining/analysis) for searching information for selecting content/information for video output (see include, but are not limited to, D’Auria: figures 2-4, 21, paragraph 0085). However, D’Auria does not explicitly disclose identifying keyword for searching background music for the video content based on the video asset information, and select background music from a music database based on the at least one keyword.
Verma discloses a processor is configured to: identify at least one keyword (particular word, or phrases) for searching background music for the video content based on the video asset information (searching soundtrack to playback as background music/soundtrack of a movie), and select background music from a music database based on the at least one keyword (see include, but are not limited to, paragraphs 0019, 0114, figures 1, 8-9).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify D’Auria in view of Ambrozic with the teaching of providing background music based on keyword as taught by Verma in order to yield predictable result of improving convenience for audience or providing user with a feeling of being present in the audio playback (paragraph 0003).
Regarding claim 16, additional limitations of the method that correspond to the additional limitations of the electronic device of claim 8 are analyzed as discussed in the rejection of claim 8.
Claims 8 and 16 are alternatively rejected under 35 U.S.C. 103 as being unpatentable over D’Auria (US 20240232937) in view of Ambrozic et al. (US 20210312318) as applied to claim 1 or claim 9 and further in view of Kim et al. (US 20260205671).
Regarding claim 8, D’Auria in view of Ambrozic discloses the electronic device of the electronic device of wherein the processor is configured to: identify at least one keyword (keyword, word, text, etc. in text mining/analysis) for searching information for selecting content/information for video output (see include, but are not limited to, D’Auria: figures 2-4, 21, paragraph 0085). However, D’Auria does not explicitly disclose identifying keyword for searching background music for the video content based on the video asset information, and select background music from a music database based on the at least one keyword.
Kim discloses a processor is configured to: identify at least one keyword for searching background music for the video content based on the video asset information, and select background music from a music database based on the at least one keyword (identifying words/keyword/text in the script to search and use the text in the script to search a database that stores background music/sound effect to select background music based on the word in the script – see include, but are not limited to, paragraph 0156, figure 14).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify D’Auria in view of Ambrozic with the teaching of selecting background music based on text/keyword as taught by Verma in order to yield predictable result of improving background music to user (paragraph 0156) to improve feeling/attraction for user.
Regarding claim 16, additional limitations of the method that correspond to the additional limitations of the electronic device of claim 8 are analyzed as discussed in the rejection of claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Menendez (US 20250294216) discloses systems and method for deep recommendations using signature analysis.
Taveira et al. (US 20250348909) discloses advertisement matching for generative artificial intelligence/machine learning model.
Chaturvedi et al. (US 12380484) discloses contextually relevant user-based product recommendations based on scene information.
Arcilla et al. (US 20250246909) discloses system and method for automated training content augmentation.
Doken et al. (US 20250159276) discloses systems and methods for generating media linked overlay image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN SON P HUYNH whose telephone number is (571)272-7295. The examiner can normally be reached 9:00 am-6:00 pm.
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/AN SON P HUYNH/Primary Examiner, Art Unit 3795
September 15, 2026
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