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 .
Status of Claims
The following is a Non-Final Office Action in response to the correspondence filed on 01/30/2025.
Claims 1- 12 are considered in this Office Action. Claims 1- 12 are currently pending.
Claim Objections
Claim 12 objected to because of the following informalities:
Claim 12 misspells motion blur by stating “motion blare”.
Appropriate correction is required.
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- 8 are rejected under 35 U.S.C. 103 as being unpatentable over Dan Shamir (US 20180089194 A1) (hereinafter Shamir) in view of Danny Kalish (US 20190289362 A1) (hereinafter Kalish):
Regarding Claim 1, Shamir 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 (a processor implemented method for generating encapsulated videos, with program instructions stored in memory [0002], [0007], [0144]) of:
wherein the encapsulated video is defined by video rules, media unit rules, and object's parameters and design rules (the encapsulated video is defined by video customizations rules, media-unit customization rules, ordering and appearance, and media object parameters [0003], [0012], [0087], [0091]- [0094], [0100], [0115], [0120]);
generate encapsulated video … by determining video rules, media unit rules object's parameters and design rules (generating the video by determining media unit, customization rules, customization parameters, and object properties. [0018], [0129], [0132], [0135], [0140]) .
Shamir does not explicitly teach the following limitations; however, in an analogous art, Kalish teaches training unified Training Ai model to generate encapsulated video from user input (training a neural network on user input to generate the video [0023], [0186]- [0191])
applying unified trained AI model to generate encapsulated video based on user input (the neural network generates the video based on user input [0011], [0025]- [0027]).
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 encapsulated video generation as disclosed by Shamir to add the AI model that utilizes user input as disclosed above by Kalish to improve the personalization of video production to the user (Kalish [0052]).
Regarding Claim 2, Shamir in view of Kalish teach the method of claim 1. Shamir further teaches objects parameters including Material object parameters, Motion parameters, Camera positions and/or movement by applying set of camera rules (object parameter includes material parameters, motion parameters, and Camera positions [0100]- [0106]).
Regarding Claim 3, Shamir in view of Kalish teach the method of claim 2. Shamir further teaches the Selection customization rule includes rules that apply to parameters which determine the media units to be displayed and the order of media unit appearance (the selection customization ruse is applied to parameters that determine which media units are displayed and in what order [0091], [0098]).
Regarding Claim 4, Shamir in view of Kalish teach the method of claim 2. Shamir further teaches material parameters including properties of objects including at least one: color, position, visibility, shape, size or orientation (the material parameters includes color, shape, and size [0101]).
Regarding Claim 5, Shamir in view of Kalish teach the method of claim 2. Shamir further teaches Motion parameters are determining motion rules of objects in relation to each video frame or group of frames, wherein the motion rules define route or movement pattern in space (motion parameters are rules governing object motion relative to each video frame or group of frames; the rules define the route or movement pattern [0022], [0027], [0103])
Regarding Claim 6, Shamir in view of Kalish teach the method of claim 2. Shamir further teaches the camera rules include Appearance customization rules, Video customization rules, Selection rules of objects or scene, rules that apply to parameters which have a visible effect on the video (Appearance customization rules, Video customization rules, and Selection rules of objects or scene that effect the video visually [0092], [0094], [0097]).
Regarding Claim 7, Shamir in view of Kalish teach the method of claim 2. Shamir further teaches each media object type may require a different optimal compression rule (each object requires different compression rule [0106]).
Regarding Claim 8, Shamir 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 (a processor implemented method for generating encapsulated videos, with program instructions stored in memory [0002], [0007], [0144]) of:
wherein the encapsulated video is defined by video rules, media unit rules and parameters and object's parameters (the encapsulated video is defined by video customizations rules, media-unit customization rules, ordering and appearance, and media object parameters [0003], [0012], [0087], [0091]- [0094], [0100], [0115], [0120]);
generate encapsulated video … by determining video rules, media unit rules and parameters. object's parameters and design rules and parameters. (generating the video by determining media unit, customization rules, customization parameters, and object properties. [0018], [0129], [0132], [0135], [0140]) .
Shamir does not explicitly teach the following limitations; however, in an analogous art, Kalish teaches training multiple Ai model to generate encapsulated video from user input (multiple neural network, training the neural network on user input to generate the video [0023], [0186]- [0191], Table-US-00001);
wherein each model is trained for different aspect of the of the encapsulated video (training two separate neural network modules, each for a different aspect of video generation [0075]- [0082], Table-US-00001).
applying trained AI models to generate encapsulated video based on user input (the neural network generates the video based on user input [0011], [0025]- [0027], [0083]- [0084]).
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 encapsulated video generation as disclosed by Shamir to add the AI model that utilizes user input as disclosed above by Kalish to improve the personalization of video production to the user (Kalish [0052]).
Claims 9- 11 are rejected under 35 U.S.C. 103 as being unpatentable over Dan Shamir (US 20180089194 A1) (hereinafter Shamir) in view of Danny Kalish (US 20190289362 A1) (hereinafter Kalish) further in view of Wu-Hsi Li (US 20240185306 A1) (hereinafter Li):
Regarding Claim 9, Shamir in view of Kalish teach the method of claim 8. Kalish further teaches wherein one AI model is for training video rules based on user (input) for generating encapsulated video by learning user feedback or usage statistics (training the neural network on user input to select video features to generate the video and learning the user feedback [0027], [0080]);.
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 encapsulated video generation as disclosed by Shamir to add the AI model that utilizes user input as disclosed above by Kalish to improve the personalization of video production to the user (Kalish [0052]).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Li teaches AI model training based on user text or given script (using user input and feedback text to train the model and generate the video [0025], [0032]- [0033], [0047]- [0048]).
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 encapsulated video generation as disclosed by Shamir in view of Kalish to further add the AI model training using user text to improve customization of the video generation (Li [0027]).
Regarding Claim 10, Shamir in view of Kalish teach the method of claim 8. Kalish further teaches wherein one AI model is for training selection of Media units based on user (input) for generating encapsulated video by learning user feedback or usage statistics (training the neural network on user input to select the optimal video variant to generate the video and learning the user feedback [0080]- [0081], [0192]- [0199]);.
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 encapsulated video generation as disclosed by Shamir to add the AI model that utilizes user input as disclosed above by Kalish to improve the personalization of video production to the user (Kalish [0052]).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Li teaches AI model training based on user text or given script (using user input and feedback text to train the model and generate the video [0025], [0032]- [0033], [0047]- [0048]).
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 encapsulated video generation as disclosed by Shamir in view of Kalish to further add the AI model training using user text to improve customization of the video generation (Li [0027]).
Regarding Claim 11, Shamir in view of Kalish teach the method of claim 8. Kalish further teaches wherein one AI model is for object parameters based user (input) for generating encapsulated video by learning user feedback or usage statistics (training the neural network on user input and to select the optimal customization values for video features [0080]- [0082], [0142], [0158]- [0160]);.
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 encapsulated video generation as disclosed by Shamir to add the AI model that utilizes user input as disclosed above by Kalish to improve the personalization of video production to the user (Kalish [0052]).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Li teaches AI model training based on user text or given script (using user input and feedback text to train the model and generate the video [0025], [0032]- [0033], [0047]- [0048]).
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 encapsulated video generation as disclosed by Shamir in view of Kalish to further add the AI model training using user text to improve customization of the video generation (Li [0027]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dan Shamir (US 20180089194 A1) (hereinafter Shamir) in view of Danny Kalish (US 20190289362 A1) (hereinafter Kalish) in view of Wu-Hsi Li (US 20240185306 A1) (hereinafter Li) further in view of Pietro Gagliano (US 20230027035 A1) (hereinafter Gagliano):
Regarding Claim 12, Shamir 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 (a processor implemented method for generating encapsulated videos, with program instructions stored in memory [0002], [0007], [0144]) of:
generate encapsulated video … by determining video rules, media unit rules and parameters. object's parameters and design rules and parameters (generating the video by determining media unit, customization rules, customization parameters, and object properties. [0018], [0129], [0132], [0135], [0140]) .
Shamir does not explicitly teach the following limitations; however, in an analogous art, Kalish teaches Training first AI model for training video rules based on user (input) for generating encapsulated video by learning by learning more user feedback (training the neural network on user input to select video features to generate the video and learning the user feedback [0027], [0080]);.
Training second Training AI model for training selection of Media units based on user (input) for generating encapsulated video by learning user feedback or usage statistics (training the neural network on user input to select the optimal video variant to generate the video and learning the user feedback [0080]- [0081], [0192]- [0199]);
Training object parameters … AI model for object parameters based on user (input) for generating encapsulated video by learning more user feedback (training the neural network on user input and to select the optimal customization values for video features [0080]- [0082], [0142], [0158]- [0160]);
applying trained AI models to generate encapsulated video based on user input (the neural network generates the video based on user input [0011], [0025]- [0027], [0083]- [0084]).
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 encapsulated video generation as disclosed by Shamir to add the AI model that utilizes user input as disclosed above by Kalish to improve the personalization of video production to the user (Kalish [0052]).
Kalish does not explicitly teach the following limitations; however, in an analogous art, Li teaches … AI model training based on user text or given script (using user input and feedback text to train the model and generate the video [0025], [0032]- [0033], [0047]- [0048]).
Training fourth AI model for Determining design layout rules and parameters including at least one of: video layers, number of media units, text, layout of objects, composition, timing, animation, effect or motion blare (video deign layout parameters including transition, orientation, special effects determined by user input through the machine learning model [0054], [0033]).
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 encapsulated video generation as disclosed by Shamir in view of Kalish to further add the AI model training using user text to improve customization of the video generation (Li [0027]).
Li does not explicitly teach the following limitations; however, in an analogous art, Li teaches third and fourth AI models (teaches multiple AI models to generate a video [0085], [0089], [0108], [0112], [0115]).
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 encapsulated video generation as disclosed by Shamir in view of Kalish and Li to further add multiple AI/ML models to generate videos as disclosed by Gagliano to decrease the time needed to generate a video by dividing the function to multiple models (Li [0094]).
Conclusion
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.
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, 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MAHMOUD KAMAL ABOUZAHRA/Examiner, Art Unit 2486
/JAMIE J ATALA/Supervisory Patent Examiner, Art Unit 2486
Prosecution Insights