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 Arguments
Applicant’s arguments with respect to claim(s) 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.
Claim(s) 1, 3-11, 13-16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al (US PG Pub No. 2020/0314465), in view of Iyer et al (US PG Pub No. 2020/0228880, hereinafter Iyer2), further in view of Anderson et al (US PG pub No. 2015/0178265)
Regarding claims 1, 11 and 16, Iyer et al teaches a method (Abstract) comprising:
evaluating a video (i.e. classifiers to identify type of content based on the extracted metadata and the content of the multimedia, Para. 0045);
identifying an advertisement object to place in the video (identifying one or more objects present in the multimedia using an Artificial Intelligence (AI) based metadata extractor and classifier, Para. 0044) based on the identified scene model;
selecting one or more 3D object models for the identified advertisement object (retrieving corresponding 3D or 2D shape of the object asset, Para. 0046);
analyzing the video to obtain video content, to identify one or more locations to place the selected 3D object models in the video (detecting edge of the at least one of object and obtain a best matching model to overlay the promotional content, Para. 0048);
analyzing user portrait information to identify user preference tags, wherein the user preference tags, wherein the collected user portrait information includes video browsing and viewing history (the personalized user profile data may include data retrieved from the user profile present on the user device, Para. 0041); and
generating customized advertising content in the video based on the selected 3D object models and the identified user preference tags for one or more given users, wherein the customizes advertising content is rendered in the video at the one or more locations (i.e. a promotional content is created in real time using the components present in the asset database (106) based on the personalized user profile, Figures 4-5, Abstract, 0048, 0065-66, 0069, 0098).
The reference is unclear with respect to identify a scene model based on scene recognition in the video and identified scene model.
In similar field of endeavor, Iyer2 et al teaches concept of identify a scene model based on scene recognition in the video and identified scene model (i.e. the advertisement may be selected from a collection of advertisements in an ad repository based on the scene recognized in the video stream content and/or the user context profile associated with the user, Figure 5, Para. 0073). Therefore, it would have been obvious to one of ordinary skill in the art to modify the reference before the effectively filing date of the claimed invention for the purpose of providing ability to modify advertisements in real time and dynamically tailor the underlying media content to individual users or consumers (Para. 0003).
The combination teaches artificial intelligence and/or machine learning techniques (Iyer2: 0084). The combination is unclear with respect to a trained dataset generated by training of collected user portrait information with an Artificial Neural Network (ANN).
In similar field of endeavor, Anderson et al teaches a trained dataset generated by training of collected user portrait information with an Artificial Neural Network (ANN) (Para. 0012, 0017). The reference also teaches wherein the collected user portrait information includes video browsing and viewing history (0012, 0017). Therefore, it would have been obvious to one of ordinary skill in the art to modify the combination before the effectively filing date of the claimed invention for the purpose of producing enhanced prediction results for recommending content in a given sequence of consumed content.
Regarding claims 3 and 13, Iyer, Iyer2 and Anderson, the combination teaches collecting user portrait information for a user comprising one or more data types of video viewing information, video browsing information, and predefined demographic data for a user (Iyer: the data may be a facial information, extracted user's face, the personal details of the user, the information in terms of roll, pitch and yaw for the viewing direction of the multimedia, user buying habits, demographic information of the user, user responses corresponding to different types of multimedia content and the like, Para. 0041 and Iyer2: the user context profile may indicate various characteristics or attributes associated with the user, such as user identity, demographics, preferences, interests, and so forth. Similarly, the user context profile may indicate various characteristics or attributes associated with the context in which the user consumes video content, such as viewing habits (e.g., what content is watched and when), viewing behavior, Iyer: Para. 0071).
Regarding claims 4, 14 and 18, Iyer, Iyer2 and Anderson, the combination teaches analyzing user portrait information to identify user preference tags comprises collecting updated user portrait information for a user, and analyzing the updated user portrait information for dynamically identifying the user preference tags for generating customized advertising content in the video (i.e. the user context profiles 416 are continuously updated as additional feedback relating to user context is observed, sensed, or otherwise provided by the users or users devices 402a-c. Iyer2: 0059).
Regarding claim 5, Iyer, Iyer2 and Anderson, the combination teaches evaluating the video to identify the scene model based on scene recognition in the video comprises accessing a scene model pool to identify multiple scene models and comparing the multiple scene models with video data content of the video to identify one or more scene models (i.e. Iyer2: key representations and/or themes 306 of the live video content 302 (e.g., race track, trees, fast cars) are then used in conjunction with information obtained from an ad repository 308 (e.g., an advertisement template and/or trained generative model for generating an advertisement) to generate 310 a video clip for an advertisement that maintains the theme, flow, and ambience of the live video content 302, Iyer: Para. 0036, 0052, 0084).
Regarding claims 6, 15 and 19, Iyer, Iyer2 and Anderson, the combination teaches identifying the advertisement object to place in the video based on the identified scene model comprises accessing an advertisement pool to identify multiple advertisements and compare the multiple scene models with video data content to identify one or more advertisement objects to place in the video (Iyer: i.e. Content management server (104) may procure a list of brand object assets from the asset database (106) and may select a best matching object asset based on the personalized user profile, Para. 0069 and Iyer2: key representations and/or themes 306 of the live video content 302 (e.g., race track, trees, fast cars) are then used in conjunction with information obtained from an ad repository 308 (e.g., an advertisement template and/or trained generative model for generating an advertisement) to generate 310 a video clip for an advertisement that maintains the theme, flow, and ambience of the live video content 302, Iyer: Para. 0036, 0052, 0084).
Regarding claim 7, Iyer, Iyer2 and Anderson, the combination teaches selecting one or more 3D object models for the identified an advertisement object comprises accessing a 3D object model pool for selecting a 3D object model based on multiple stored 3D object models (Iyer: content management server (104) may procure a list of brand object assets from the asset database (106) and may select a best matching object asset based on the personalized user profile. Further, the content management server (104) may retrieve the corresponding 2D or 3D object from Object Asset database (106) and paints the 2D or 3D model with the color of brand and other attributes required by the brand, Para. 0069 and Iyer2: Para. 0036, 0052, 0084).
Regarding claim 8, Iyer, Iyer2 and Anderson, the combination teaches analyzing user portrait information to identify user preference tags as discussed above. The combination is unclear with respect to accessing a public information dataset and collecting available user preference information for a user from the public information dataset. However, the examiner takes official notice that both concepts and advantages are well known and expected in the art. It would have been obvious to one of ordinary skill in the art to modify the combination by specifically a public information dataset and collecting available user preference information for a user from the public information dataset before the effectively filing date of the claimed invention for the common knowledge purpose of providing ability to modify advertisements in real time and dynamically tailor the underlying media content to group of users or consumers.
Regarding claims 9 and 20, Iyer, Iyer2 and Anderson, the combination teaches generating customized advertising content in the video based on the selected 3D object models and identified user preference tags comprises generating the video with selected 3D object models to replace selected objects in video content in the video (Iyer: content management server (104) may procure a list of brand object assets from the asset database (106) and may select a best matching object asset based on the personalized user profile. Further, the content management server (104) may retrieve the corresponding 2D or 3D object from Object Asset database (106) and paints the 2D or 3D model with the color of brand and other attributes required by the brand. Figure 4, and Iyer2: Para. 0036, 0052, 0084).
Regarding claim 10, Iyer, Iyer2 and Anderson, the combination teaches wherein generating customized advertising content in the video based on the selected 3D object models and identified user preference tags comprises generating the video with the selected 3D object models added to video content in the video (Iyer: content management server (104) may procure a list of brand object assets from the asset database (106) and may select a best matching object asset based on the personalized user profile. Further, the content management server (104) may retrieve the corresponding 2D or 3D object from Object Asset database (106) and paints the 2D or 3D model with the color of brand and other attributes required by the brand. Figure 4, and Iyer2: Para. 0036, 0052, 0084).
Claim(s) 2, 12 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al, in view of Iyer2 et al, in view of Anderson et al, further in view of Narayanan et al (US PG Pub No. 2023/0068437).
Regarding claims 2, 12 and 17, Iyer, Iyer2 and Anderson, the combination teaches analyzing the video data to identify a pose of the selected 3D object model in the video according to the scene information, and rendering the selected 3D model in the video based on the identified pose (i.e. object asset may be adjusted to match the size and coordinates of the at least one object by performing a shrink, rotate and flip operations on the object asset, Para. 0049). The reference isn’t clear with respect to 6DOF (Six degrees of freedom).
In similar field of endeavor, Narayanan et al teaches 6DOF (Six degrees of freedom). Therefore, it would have been obvious to one of ordinary skill in the art to modify the reference before the effectively filing date of the claimed invention for the purpose of providing ability to modify advertisements accurately in real time and dynamically tailor the underlying media content to individual users or consumers providing immersive viewing experience.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUNAL LANGHNOJA whose telephone number is (571)270-3583. The examiner can normally be reached M-F: 9:00AM - 5:00PM ET.
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, Brian Pendleton can be reached at (571) 272-7527. 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.
/KUNAL LANGHNOJA/Primary Examiner, Art Unit 2425
Prosecution Insights