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 .
Response to Amendment
The Amendment filed 07/29/2026 has been entered. Claims 1-12 are pending in this application.
Claims 1, 8, and 12 have been amended.
Response to Arguments
Applicant’s arguments with respect to claims 1- 12 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
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Danny Kalish (US 20180089194 A1) (hereinafter Kalish) further in view of Daniel Josh Rosen (US 10334328 B1) (hereinafter Rosen):
Regarding Claim 1, Kalish teaches a method for generating encapsulated video using Al model implemented by one or more processors operatively coupled to a non- transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the steps ([0002], [0007], and [0144] teach a processor implemented method for generating encapsulated videos, with program instructions stored in memory) of:
- …wherein the encapsulated video is defined by video rules ([0092], [0094], and [0119] teach video customization rules that define the encapsulated video), media unit rules ([0091], and [0093] teach media unit customization rules determining the media units displayed and their order), and object's parameters ([0100]- [0101], and [0103]- [0104] teach the dedicated set of parameters attributed to each media object) and design rules ([0022], [0102], [0109], and [0116] teach frame layer structure, global lighting, shading, opacity features, screen layout rules and special effects parameters);
by determining video rules, media unit rules object's parameters and design rules ([0111], [0113], and [0115]- [0116] teach determining the media unit selection and ordering rules, media unit customization rules, media object parameters and extension design features), wherein the encapsulated video is a data file storing said video rules, media unit rules, object's parameters and design rules ([0006], [0086], and [0119] teach an encapsulated video file containing the media unit parameters, object parameters and video customization data) for subsequent decapsulation into playable video frames ([0006], [0082], and [0136]- [0137] teach de-capsulation of the encapsulated file by generating the video frames and a playable video file).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Rosen teaches training unified Training Ai model ([Col 3 Lines 51- 59], [Col 3 Lines 29- 40], [Col 11 Lines 54- 67]- [Col 12 Lines 1- 9], and [Col 12 Lines 26- 44] teach training a machine model that generates the entire story model defining the video) to generate encapsulated video from user input ([Col 3 Lines 13- 17 ], [Col 5 Lines 38- 18], and [Col 12 Lines 9- 25] teach the model generating the story model from the client's smart script, preferences and viewer feedback inputs)
- applying unified trained Al model to generate encapsulated video based on user input ([Col 12 Lines 26- 44], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach applying the trained machine model to the input features to generate the new story model).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish to add the generation of video files using machine learning models as disclosed above by Rosen to improve video generation using user scripts (Rosen [Col 12 Lines 56- 67]).
Regarding claim 8, Kalish teaches a method for generating encapsulated video using Al model implemented by one or more processors operatively coupled to a non- transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the steps ([0002], [0007], and [0144] teach a processor implemented method for generating encapsulated videos, with program instructions stored in memory) of:
wherein the encapsulated video is defined by video rules ([0092], [0094], and [0119] teach video customization rules that define the encapsulated video), media unit rules and parameters ([0093], [0091], [0093], and [0112] teach media unit customization rules and media unit configuration parameters), and object's parameters ([0100]- [0101], and [0103]- [0104] teach the dedicated set of parameters attributed to each media object);
by determining video rules, media unit rules and parameters. object's parameters ([0111]- [0113], and [0115] teach determining the media unit selection and ordering rules, media unit configuration parameters and customization rules, and media object parameters) and design rules and parameters ([0022], [0102], [0109], and [0116] teach determining global lighting, shading, opacity extension features, frame layer structure, screen layout rules and special effects parameters), wherein the encapsulated video is a data file storing ([0086], and [0119] teach an encapsulated video file storing the video project and customization data) said video rules, media unit rules and parameters, object's parameters and design rules and parameters ([0006], [0109], and [0119] teach the file containing the media unit parameters, object parameters, customization data and frame layer structure) for subsequent decapsulation into playable video frames ([0006], [0082], and [0136]- [0137] teach de-capsulation of the encapsulated file by generating the video frames and a playable video file).
Kalish does not explicitly teach the following limitations; However, in an analogous art, Rosen teaches training multiple Ai model ([Col 7 Lines 57- 67]- [Col 8 Lines 11- 26], and [Col 10 Lines 4- 34] teach training multiple machine models) to generate encapsulated video from user input ([Col 3 Lines 13- 17 ], [Col 5 Lines 38- 18], and [Col 12 Lines 9- 25] teach the models generating the story model from the client's smart script, preferences and viewer feedback inputs) … wherein each model is trained for different aspect of the of the encapsulated video ([0020] [Col 3 Lines 18- 28 ], [Col 7 Lines 57- 67]- [Col 8 Lines 11- 26], [Col 10 Lines 4- 34], and [Col 11 Lines 54- 67]- [Col 12 Lines 1- 9] teach distinct trained models each directed to a different aspect of the generated video)
applying trained Al models to generate encapsulated video based on user input ([Col 12 Lines 26- 44], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach applying the trained machine models to the input features to generate the new story model).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish to add the generation of video files using machine learning models as disclosed above by Rosen to improve video generation using user scripts (Rosen [Col 12 Lines 56- 67]).
Regarding claim 9, Kalish in view of Rosen teach the method of claim 8. Rosen further teaches wherein one AI model is for training video rules ([Col 3 Lines 29- 40], [0058][Col 11 Lines 10- 35], [Col 11 Lines 54- 67]- [Col 12 Lines 1- 9], and [Col 12 Lines 26- 44] teach a trained model that determines the structural and stylistic parameters and the ordering governing the rendered video) based on user text or given script ([Col 1 Lines 47- 59], [Col 6 Lines 53- 63], and [Col 7 Lines 10- 19] teach the model operating on the client’s smart script document) for generating encapsulated video ([Col 3 Lines 51- 59], [Col 12 Lines 26- 44], and [Col 13 Lines 3- 11] teach the generated story model being stored and later rendered into the video) by learning user feedback or usage statistics ([Col 7 Lines 57- 67] - [Col 8 Lines 1- 10], and [Col 11 lines 65- 67]- [Col 12 lines 1- 25] teach training the model from viewer feedback labels and viewing statistics).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish to add the generation of video files using machine learning models as disclosed above by Rosen to improve video generation using user scripts (Rosen [Col 12 Lines 56- 67]).
Regarding claim 10, Kalish in view of Rosen teach the method of claim 8. Rosen further teaches wherein one AI model is for training selection of Media units ([Col 1 Lines 47- 67]- [Col 2 Lines 1- 3], [Col 3 Lines 41- 47], [Col 11 Lines 65- 67] - [Col 12 Lines 1- 8], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach a trained model that selects the number, subset and order of the video segments) based on user text or given script ([Col 1 Lines 47- 59], [Col 6 Lines 11- 16], and [Col 6 Lines 53- 63] teach the story beats being drawn from the client's smart script) for generating encapsulated video ([Col 3 Lines 51- 59], [Col 12 Lines 26- 44], and [Col 13 Lines 3- 11] teach the generated story model being stored and later rendered into the video) by learning user feedback or usage statistics ([Col 11 lines 65- 67]- [Col 12 lines 1- 25], and [Col 7 Lines 45- 56] teach learning from viewer feedback labels, viewing statistics, and viewer reactions).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish to add the generation of video files using machine learning models as disclosed above by Rosen to improve video generation using user scripts (Rosen [Col 12 Lines 56- 67]).
Regarding claim 11, Kalish in view of Rosen teach the method of claim 8. Rosen further teaches wherein one AI model is for object parameters ([Col 3 Lines 41- 47], [Col 7 Lines 37- 44], [Col 8 Lines 27- 43], [Col 11 Lines 10- 35], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach a trained model determining the parameter values that select and characterize the content objects of each segment) based on user text or given script ([Col 1 Lines 47- 59], [Col 7 Lines 10- 19], and [Col 12 Lines 9- 25] teach the parameter values being set from the client's smart script) for generating encapsulated video ([Col 3 Lines 51- 59], [Col 12 Lines 26- 44], and [Col 13 Lines 3- 11] teach the generated story model being stored and later rendered into the video) by learning user feedback or usage statistics ([Col 3 Lines 18- 28 ], [Col 9 Lines 5- 24], and [Col 11 Lines 65- 67] - [Col 12 Lines 1- 8] teach updating the parameter values from viewer feedback metrics and viewing statistics).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish to add the generation of video files using machine learning models as disclosed above by Rosen to improve video generation using user scripts (Rosen [Col 12 Lines 56- 67]).
Claims 2- 7 are rejected under 35 U.S.C. 103 as being unpatentable over Danny Kalish (US 20180089194 A1) (hereinafter Kalish) further in view of Daniel Josh Rosen (US 10334328 B1) (hereinafter Rosen) Jonathan Hise Kaldma (US 20160139786 A1) (hereinafter Kaldma):
Regarding Claim 2, Kalish in view of Rosen teach the method of claim 1. Kalish further teaches wherein objects parameters including Material object parameters ([0100]- [0101] teach object parameters including material parameters), Motion parameters ([0103] teaches motion parameters of the media objects), Camera positions and/or movement ([0104] teaches camera positions and movement as an object parameter).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Kaldma teaches … by applying set of camera rules ([0088]- [0089], [0091] and , [0093] teach determining the virtual camera placement and movement by applying a set of camera rules).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish in view of Rosen to add the set of camera rules as disclosed above by Kaldma to improve the visual representation of the video with respect to the environment (Kaldma [0088]).
Regarding Claim 3, Kalish in view of Rosen and Kaldma teach the method of claim 2. Kalish further teaches wherein the Selection customization rule includes rules that apply to parameters which determine the media units to be displayed ([0093], and [0097] teach selection customization rules that determine the media units to be displayed) and the order of media unit appearance ([0098], and [0111] teach the selection rules determining the order of media unit appearance).
Regarding Claim 4, Kalish in view of Rosen and Kaldma teach the method of claim 2. Kalish further teaches wherein material parameters including properties of objects ([0100]- [0101] teach material parameters including properties of objects) including at least one: color, position, visibility, shape, size or orientation ([0101], and [0115] teach the material properties color, position, visibility, shape, size and orientation).
Regarding Claim 5, Kalish in view of Rosen and Kaldma teach the method of claim 2. Kalish further teaches wherein Motion parameters are determining motion rules of objects in relation to each video frame or group of frames ([0005], and [0103] teach motion parameters determining motion rules of objects for each frame or group of frames) wherein the motion rules define route or movement pattern in space ([0022], and [0103] teach motion rules defining a rule a route movement pattern in space).
Regarding Claim 6, Kalish in view of Rosen and Kaldma teach the method of claim 2. Kalish further teaches wherein the camera rules include Appearance customization rules ([0092], and [0115] teach appearance customization rules among the customization rules applied in setting the object parameters), Video customization rules ([0094]- [0095] teach video customization rules applied to the media units), Selection rules of objects or scene ([0018], [0093], and [0097] teach selection rules determining the media units; scenes to be displayed and the object properties selected at each frame), rules that apply to parameters which have a visible effect on the video ([0092] teaches rules that apply to parameters which have a visible effect on the video).
Regarding Claim 7, Kalish in view of Rosen and Kaldma teach the method of claim 2. Kalish further teaches wherein each media object type may require a different optimal compression rule ([0106], [0117], and [0125] teach each media object type requiring a different compression rule determined according to its type).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Danny Kalish (US 20180089194 A1) (hereinafter Kalish) further in view of Daniel Josh Rosen (US 10334328 B1) (hereinafter Rosen) Vineet Gandhi (US 20210042135 A1) (hereinafter Gandhi):
Regarding claim 12, Kalish teaches a method for generating encapsulated video using AI model implemented by one or more processors operatively coupled to a non- transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the steps ([0002], [0007], and [0144] teach a processor implemented method for generating encapsulated videos, with program instructions stored in memory) of:
… by determining video rules, media unit rules and parameters. object's parameters and design rules and parameters ([0111]- [0113], and [0115]- [0116] teach determining the media unit selection and ordering rules, media unit configuration parameters and customization rules, media object parameters and extension design features), wherein the encapsulated video is a data file storing said video rules, media unit rules and parameters, object's parameters and design rules and parameters ([0006], [0086], [0109], and [0119] teach an encapsulated video file containing the media unit parameters, object parameters, customization data and frame layer structure) for subsequent decapsulation into playable video frames ([0006], [0082], and [0136]- [0137] teach de-capsulation of the encapsulated file by generating the video frames and a playable video file).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Rosen teaches Training first AI model for training video rules ([Col 3 Lines 29- 40], [Col 11 Lines 10- 35], [Col 11 Lines 65- 67] - [Col 12 Lines 1- 8], and [Col 12 Lines 26- 44] teach training of first model that determines the structural and stylistic parameters and ordering governing the rendered video) based on user text or given script ([Col 1 Lines 47- 59], [Col 6 Lines 53- 63], and [Col 7 Lines 10- 19] teach the model operating on the client's smart script document) for generating encapsulated video ([Col 3 Lines 51- 59], [Col 12 Lines 26- 44], and [Col 13 Lines 3- 11] teach the generated story model being stored and later rendered into the video) by learning by learning more user feedback ([Col 7 Lines 57- 67] - [Col 8 Lines 1- 10], and [Col 11 lines 65- 67]- [Col 12 lines 1- 25] teach training the model from viewer feedback labels and viewing statistics);
- Training second Training AI model for training selection of Media units ([Col 1 Lines 60- 67]- [Col 2 Lines 1- 3], [Col 3 Lines 41- 47], [Col 3 Lines 51- 59], [Col 8 Lines 11- 26], [Col 12 Lines 26- 44], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach training another model that creates the story model by selecting the number, subset and order of the video segments) based on user text or given script ([Col 1 Lines 47- 59], [Col 6 Lines 11- 16], and [Col 6 Lines 53- 63] teach the story beats being drawn from the client's smart script) for generating encapsulated video ([Col 3 Lines 51- 59], [Col 12 Lines 26- 44], and [Col 13 Lines 3- 11] teach the generated story model being stored and later rendered into the video) by learning user feedback or usage statistics ([Col 7 Lines 45- 56], [Col 8 Lines 11- 26], and [Col 12 Lines 9- 25] teach learning from viewer feedback metrics, viewing statistics and viewer reactions);
Training object parameters third AI model for object parameters ([Col 3 Lines 18- 28 ], [Col 3 Lines 41- 47], [Col 8 Lines 27- 43], [Col 11 Lines 10- 35], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach a third model determining the parameter values that select and characterize the content objects of each segment) based on user text or given script ([Col 1 Lines 47- 59], [Col 7 Lines 10- 19], and [Col 12 Lines 9- 25] teach the parameter values being set from the client's smart script) for generating encapsulated video ([Col 3 Lines 51- 59], [Col 12 Lines 26- 44], and [Col 13 Lines 3- 11] teach the generated story model being stored and later rendered into the video) by learning more user feedback ([Col 3 Lines 18- 28 ], [Col 9 Lines 5- 24], and [Col 11 Lines 65- 67] - [Col 12 Lines 1- 8] teach updating the parameter values from viewer feedback metrics and viewing statistics);
-applying trained AI models to generate encapsulated video based on user input ([Col 12 Lines 26- 44], and [Col 12 Lines 56- 67]- [Col 13 Lines 1- 2] teach applying the train machine models to the input features to generate the new story model).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish to add the generation of video files using machine learning models as disclosed above by Rosen to improve video generation using user scripts (Rosen [Col 12 Lines 56- 67]).
Rosen does not explicitly teach the following limitations; However, in an analogous art, Gandhi teaches Training fourth AI model for Determining design layout rules and parameters ([0039], [0040], and [0043] teacher layout suggestion model learned from a quantity of user-created animations that determines the layout of the scene) including at least one of: video layers, number of media units, text, layout of objects, composition, timing, animation, effect or motion blur ([0041]- [0042], and [0046] teach determining the layout of objects, characters and text positions in the scene; the layout of objects, text and composition).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the generation of customizable and encapsulated media file as disclosed by Kalish in view of Rosen to add the machine learning model for determining video layout as disclosed above by Gandhi to improve the automation of the video creation (Gandhi [0002]).
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 MAHMOUD KAMAL ABOUZAHRA whose telephone number is (703)756-1694. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jamie Atala can be reached at (571) 272-7384. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAHMOUD KAMAL ABOUZAHRA/Examiner, Art Unit 2486
/JAMIE J ATALA/Supervisory Patent Examiner, Art Unit 2486
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