DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This Office Action is in response to the Amendment filed on 02/27/2026.
3. Claims 1-20 are pending. All the pending claims are examined and rejected.
Response to Arguments
4. Applicant's arguments filed 02/27/2026 have been fully considered but they are not persuasive.
The applicant argues An does not teach the claimed elements of the current invention. The examiner disagrees. The examiner responses for each argued limitations are herein with the same line as given in the office action rejection.
To begin with the current invention is directed to Automatically Generation Of Video Templates. Similarly An et al (US 20250200825 A1) or “An” the applied reference below is also directed to Content Item Video Generation Template. That is. An discloses Methods and systems for produce a video by applying a template.
Applicant argue that An does not teach a method for automatically generating templates based on images using a machine learning model.
The examiner strongly disagrees. An discloses a method (e.g., flowchart of Fig. 9) or generating video by applying a template to various content items (see Abstract). An applies or generates a template to produce a video for automatically generating templates based on images using a machine learning model (see Abstract , [0158 - [1]].
The applicant argues An does not teach “receiving at least one image by the machine learning model, wherein the machine learning model is trained to generate templates based on input images, and wherein the templates comprise editing components for generating or editing videos”.
The examiner strongly disagrees. An discloses ([0018] The disclosed techniques seek to improve the efficiency of using an electronic device by intelligently and automatically grouping and organizing a collection of content items and modifying at least a portion of the content items using AR elements in accordance with a video generation template to create a video that represents a user's interest. [0152] For example, the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt. In some cases, the generative machine learning models generate an artificial image/video, code segments, and/or text that is responsive to the prompt. Also see [0066, 0155]);
Applicant argue that An does not teach receiving a piece of music recommended based on the at least one image.
The examiner strongly disagrees. An discloses [0174] In some examples, the video generation template system 590 retrieves a soundtrack or audio track associated with the video generation template. The video generation template system 590 determines the beat associated with the audio track and computes how many content items are included in the collection of content items. Also see [0018]).
Applicant argue that An does not teach generating a conditional embedding by a first sub-model of the machine learning model based on a visual embedding indicative of the at least one image and a music embedding indicative of the piece of music.
The examiner strongly disagrees. An discloses ([0146] The personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space. Also see [0147-0148, 0154, 0186].
Applicant argue that An does not teach generating a representation of a template based on the conditional embedding by a second sub-model of the machine learning model.
The examiner strongly disagrees. An discloses ([0066] The artificial intelligence and machine learning system 230 can then combine the collection of previously captured content items into a single video by inserting transitions between the content items, visually arranging the content items on a single page, adding AR elements to the content items, generating new content items based on the previously captured content items, and/or associating the content items with an audio track and synchronizing presentation of the content items according to a beat of the audio track); and [0146] The personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space. The purpose of embeddings is to capture the semantic meaning and relationships between different modalities, allowing for more efficient and accurate processing of multimodal data
Applicant also argue that An does not teach generating the video template based on the representation of the video template, wherein the video template comprises a plurality of editing components corresponding to a plurality time slots, wherein the plurality of time slots covers different time ranges.
The examiner strongly disagrees. An discloses ([0173] As content items are selected from the third user interface 602, the video generation template system 590 populates the timeline 620. The timeline 620 can be populated in the order in which the content items are selected. For example, if the first content item 630 is selected before the second content item 632, the video generation template system 590 associates a first portion of the timeline 620 with the first content item 630 and associates a second portion of the timeline 620 with the second content item 632. The first portion of the timeline 620 can precede the second portion meaning that content items presented in the video during the first portion are presented before content items associated with the second portion. The video generation template system 590 can receive input from the user rearranging the content items in the timeline 620. After a sufficient quantity of content items are selected (e.g., a quantity of content items corresponding to the minimum quantity associated with the video generation template), the video generation template system 590 presents a confirm option 639. Also see [0129, 0155, 0159, 0180, 0187], Fig. 6).
As per dependent claims 2-9, as given in the rejection the limitations of these claims are also anticipated by An.
As per the remaining system claims 10-15 and medium claims 16-20, the response given to the method claim 1 equally applies to the system and medium claims.
Moreover, the applicant arguments are not persuasive, the rejection is maintained.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by An et al (US 20250200825 A1).
An et al ( “An” ) is directed to CONTENT ITEM VIDEO GENERATION TEMPLATE.
As per claim 1, An (US 20250200825 A1) disclose a method (see flowchart of Fig. 9) for automatically generating 0158 - [1]] The video generation template system 590 can continuously or periodically analyze images captured by one or more interaction client 104, such as the UI components 520. The video generation template system 590 can apply one or more machine learning models to a collection of previously captured content items to select content items that match criteria or instructions of one or more video generation templates), comprising:
receiving at least one image by the machine learning model, wherein the machine learning model is trained to generate ([0018] The disclosed techniques seek to improve the efficiency of using an electronic device by intelligently and automatically grouping and organizing a collection of content items and modifying at least a portion of the content items using AR elements in accordance with a video generation template to create a video that represents a user's interest. [0152] For example, the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt. In some cases, the generative machine learning models generate an artificial image/video, code segments, and/or text that is responsive to the prompt. Also see [0066, 0155]);
receiving a piece of music recommended based on the at least one image ([0066] the artificial intelligence and machine learning system 230 can then combine the collection of previously captured content items into a single video by inserting transitions between the content items, visually arranging the content items on a single page, adding AR elements to the content items, generating new content items based on the previously captured content items, and/or associating the content items with an audio track and synchronizing presentation of the content items according to a beat of the audio track. [0171] The video generation template system 590 receives input from the user that selects a subset of content items from the content items presented in the second user interface 601. [0174] In some examples, the video generation template system 590 retrieves a soundtrack or audio track associated with the video generation template. The video generation template system 590 determines the beat associated with the audio track and computes how many content items are included in the collection of content items. Also see [0018]);
generating a conditional embedding by a first sub-model of the machine learning model based on a visual embedding indicative of the at least one image and a music embedding indicative of the piece of music ([0146] In some examples, the personal AI agent 502 feeds the knowledge graph to an external or internal process to generate the latent embeddings. The personal AI agent 502 stores the latent embeddings in the multimodal memory 508 for use in generating the on-the-fly content. Also see [0147-0148, 0154, 0186]; and
generating a representation of a ([0066] The artificial intelligence and machine learning system 230 can then combine the collection of previously captured content items into a single video by inserting transitions between the content items, visually arranging the content items on a single page, adding AR elements to the content items, generating new content items based on the previously captured content items, and/or associating the content items with an audio track and synchronizing presentation of the content items according to a beat of the audio track); and
generating the 0173] As content items are selected from the third user interface 602, the video generation template system 590 populates the timeline 620. The timeline 620 can be populated in the order in which the content items are selected. For example, if the first content item 630 is selected before the second content item 632, the video generation template system 590 associates a first portion of the timeline 620 with the first content item 630 and associates a second portion of the timeline 620 with the second content item 632. The first portion of the timeline 620 can precede the second portion meaning that content items presented in the video during the first portion are presented before content items associated with the second portion. The video generation template system 590 can receive input from the user rearranging the content items in the timeline 620. After a sufficient quantity of content items are selected (e.g., a quantity of content items corresponding to the minimum quantity associated with the video generation template), the video generation template system 590 presents a confirm option 639. Also see [0129, 0155, 0159, 0180, 0187], Fig. 6).
As per claim 2, An (US 20250200825 A1) further discloses that the method of claim 1, wherein the second sub-model of the machine learning model comprises a latent diffusion model ([0088] Generative Adversarial Networks (GANs) may be used in applications such as image synthesis, video prediction, and style transfer. [0092] Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data. The latent space can be manipulated to generate new variations of the output data).
As per claim 3, An (US 20250200825 A1) further discloses that the method of claim 1, further comprising: training the machine learning model using pairs of training data, wherein each pair of training data comprises a particular ([0087] In some examples the trained machine-learning program, such as personalized AI agent system 232 may be a generative AI model. Generative AI is a term that may refer to any type of artificial intelligence that can create new content from training data. For example, generative AI can produce text, images, video, audio, code or synthetic data that are similar to the original data but not identical. In some cases, generative AI can include or implement large language models (LLMs). [0114] The databases 304 also include trained machine learning techniques 307 that stores parameters of one or more machine learning models that have been trained during training of the video generation template system 590 (FIG. 5) and/or the personalized AI agent system 232 (FIG. 2). For example, trained machine learning techniques 307 stores the trained parameters of one or more artificial neural network machine learning models or techniques).
As per claim 4, An (US 20250200825 A1) further discloses that the method of claim 3, further comprising: generating a representation of the particular [0092] Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data); and
generating a conditional embedding corresponding to the particular 0146] The personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space. The purpose of embeddings is to capture the semantic meaning and relationships between different modalities, allowing for more efficient and accurate processing of multimodal data. In some examples, the personal AI agent 502 feeds the knowledge graph to an external or internal process to generate the latent embeddings. The personal AI agent 502 stores the latent embeddings in the multimodal memory 508 for use in generating the on-the-fly content recommendations and analysis. In some cases, the content recommendations are provided to the user system 102 without the user issuing a specific request for the content recommendations.
As per claim 5, An (US 20250200825 A1) further discloses that method of claim 4, wherein the generating a representation of the particular
determining a plurality of groups of editing components corresponding to time slots of the particular video template ([0180] the video generation template system 590 applies the one or more AR elements (e.g., overlays the one or more AR elements) on the object depicted in the content item to modify the content item. The video generation template system 590 can then place the modified content item back in the timeline 620 for presentation in the video generated using the instructions of the content item generation template).
generating a plurality of groups of editing component embeddings corresponding to the time slots of the particular 0171] The video generation template system 590 receives input from the user that selects a subset of content items from the content items presented in the second user interface 601. The video generation template system 590 can present a timeline 620. The timelines 620 represents how long each content item is presented in the video that combines the selected content items according to the instructions of the video generation template. and
generating the representation of the particular [0173] As content items are selected from the third user interface 602, the video generation template system 590 populates the timeline 620. The timeline 620 can be populated in the order in which the content items are selected. For example, if the first content item 630 is selected before the second content item 632, the video generation template system 590 associates a first portion of the timeline 620 with the first content item 630 and associates a second portion of the timeline 620 with the second content item 632. The first portion of the timeline 620 can precede the second portion meaning that content items presented in the video during the first portion are presented before content items associated with the second portion. The video generation template system 590 can receive input from the user rearranging the content items in the timeline 620. After a sufficient quantity of content items are selected (e.g., a quantity of content items corresponding to the minimum quantity associated with the video generation template), the video generation template system 590 presents a confirm option 639. [0180] In some examples, in the process of generating the video according to the instructions of the video generation template, the video generation template system 590 can select one or more content items for applying AR elements and/or generate new content items).
As per claim 6, An (US 20250200825 A1) further discloses that the method of claim 1, further comprising: receiving text input by a user ([0152] for example, the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt); and
generating the conditional embedding by the first sub-model of the machine learning model based on the visual embedding indicative of the at least one image, the music embedding indicative of the piece of music, and a text embedding indicative of the text input by the user ([0146] The personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space. The purpose of embeddings is to capture the semantic meaning and relationships between different modalities, allowing for more efficient and accurate processing of multimodal data. Also see [0147-0148]).
As per claim 7, An further discloses that the method of claim 1, further comprising: refining the plurality of editing components by performing spatial adjustments on at least a subset of the plurality of editing components ([0042] An image processing system 202 provides various functions that enable a user to capture and augment (e.g., annotate or otherwise modify or edit) media content associated with a message. [0184] The video generation template system 590 can adjust transitions between the content items of the generated video 640 to match a new beat of the alternate soundtrack. For example, the generated video 640 may initially present a first content item for three beats followed by a second content item for five beats of the default soundtrack. After receiving input that selects the alternate soundtrack, the video generation template system 590 modifies the generated video 640 to present the first content item for five beats of the alternate soundtrack followed by the second content item for two beats).
As per claim 8, An (US 20250200825 A1) further discloses that the method of claim 1, further comprising: refining the plurality of editing components by performing temporal alignments based on the piece of music ([0184] For example, the generated video 640 may initially present a first content item for three beats followed by a second content item for five beats of the default soundtrack. After receiving input that selects the alternate soundtrack, the video generation template system 590 modifies the generated video 640 to present the first content item for five beats of the alternate soundtrack followed by the second content item for two beats. Also see [0018, 00165, 0166, 0177 and 0181]).
As per claim 9, An (US 20250200825 A1) further discloses that the method of claim 1, further comprising: generating a video using the video template, wherein the 0174] In some examples, the video generation template system 590 retrieves a soundtrack or audio track associated with the video generation template. The video generation template system 590 determines the beat associated with the audio track and computes how many content items are included in the collection of content items. The video generation template system 590 determines, based on the beat, that the quantity of content items in the collection of content items transgresses a sound synchronization threshold. In response, the video generation template system 590 adjusts (e.g. increases or decreases) how many content items are presented for each beat or collection of beats of the audio track. Also see [0184-0184 and 0186-0187]).
As per system claims 10-15, An (US 20250200825 A1) discloses a system ([0011] FIG. 10 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed to cause the machine to perform any one or more of the methodologies recited in claims 1-6, respectively). Thus the system claims are also rejected under similar citations given to the method claims.
As per non-transitory computer-readable storage medium of claims 16-20 , An (US 20250200825 A1) disclose a storage, for example [0099] FIG. 3 is a schematic diagram illustrating data structures 300, which may be stored in a database 304 of the interaction server system 110). Thus the storage medium claims are also rejected under similar citations given to the method claims 1-5, respectively..
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11863844 B2 discloses various embodiments for dynamically generating an advertisement in a video stream are disclosed. In one embodiment, video stream content associated with a video stream for a user device is received. Video analytics data is obtained for the video stream content, which indicates a scene recognized in the video stream content. An advertisement to be generated and inserted into the video stream content is then selected based on the scene recognized in the video stream content, and an advertisement template for generating the selected advertisement is obtained. Video advertisement content corresponding to the advertisement is then generated based on the advertisement template and the video analytics data. The video advertisement content is then inserted into the video stream content, and the modified video stream content is transmitted to the user device.
7. THIS ACTION IS MADE FINAL. 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.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TADESSE HAILU whose telephone number is (571)272-4051; and the email address is Tadesse.hailu@USPTO.GOV. The examiner can normally be reached Monday- Friday 9:30-5:30 (Eastern time).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bashore, William L. can be reached (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TADESSE HAILU/ Primary Examiner, Art Unit 2174
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