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 .
DETAILED ACTION
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 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 of this title, 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 1-2, 4-6, 8-9, 11-13, 15-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aguilar et al. (US 2018/0174616) in view of Novikoff et al. (US 2018/0068019) in view of Ramadorai et al. (US 2018/0295396) and further in view of Hicken (US 2006/0265349).
Regarding claim 1, Aguilar teaches a system comprising:
at least one processor (Fig. 7, processor 702);
at least one memory component (Fig. 7, system memory 714) storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising (System in Figs. 1-5 and 7):
selecting, from a collection of media items, a number of media items for use in a video montage comprising the number of media items (Paragraph 0032 teaches a user is able to select video clips in “the one or more source video clips may include video clips selected by a user” or that a system is able to select automatically a number of video clips);
determining a theme parameter from the number of media items by performing a visual analysis on the number of media items or by analyzing metadata associated with the number of media items (Figs. 3, 5 and paragraphs 6, 28 and 42 teaches “determining a common theme” by analyzing source video clips);
identifying an audio track having a theme parameter corresponding to the theme parameter determined from the number of media items (while Aguilar teaches determining a theme as discussed above and teaches determining the audio for further theme based analysis, fails to explicitly teach “identifying an audio track having a theme parameter corresponding to the theme parameter determined from the number of media items”); and
generating, using the data structure, a video montage from the number of media items and the audio track (Fig. 5, step 506 teaches generating a compiled video comprising the plurality of video segments, which as discussed above determines the editing theme).
However, while Aguilar teaches determining a theme from a set of selected video clips, fails to teach, but Novikoff teaches “identifying an audio track having a theme parameter corresponding to the theme parameter determined from the number of media items (Paragraphs 0081-0082 and step 302 teaches the claimed); and
generating a data structure specifying an identity and order of the number of media items and a start location of the audio track (Paragraphs 0159 teaches order of video and Fig. 3, step 320 determines the soundtrack for the respective selected videos),
generating, using the data structure, a video montage from the number of media items and the audio track (Paragraphs 0086 and step 322 teaches wherein montages are created based on the selected media items and the audio track).
It is noted that while Aguilar also teaches its own montage ability, the incorporation of Novikoff presents the additional benefit of identify and order the media clips with the identified audio track to generate the montage.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to incorporate the teachings of Novikoff into the system of Aguilar because said incorporation allows for the benefit of improving the overall system by automatically selecting the best soundtrack to be associated with corresponding videos to improve impact by relating emotion/mood in the music to the video clips/segments and also for the purposes of reducing the time it takes by automatically associating such soundtracks with the videos (see paragraphs 27 and 82).
While Aguilar and Novikoff teaches the claimed as discussed in claim 1 above, however, Novikoff and Aguilar fail to teach the claimed, but Ramadorai teaches the claimed: determining that a particular media item used in the video montage has been deleted from the local media gallery of visual media items (paragraphs 188-196 teaches a montage refreshing process in which media is determined to be removed or deleted altogether. “Bubbles” as recited in Ramadorai essentially provides a data store/container for one or more events or topics of interest, therefore, wherein the media is deleted altogether from the bubbles, the montage is equally updated. Since the montage is updated to the removed media, the media item deleted appears to be used in the previous montage);
in response to determining that the particular media item has been deleted from the local media gallery, scanning the local media gallery to identify a replacement media item having characteristics corresponding to characteristics of media items remaining in the data structure (Ramadorai partially teaches this limitation: paragraphs 142, 147-169, 261-267 teaches wherein scanning is performed and also teaches scoring candidate media based on markers, metadata, visual analysis (faces, objects, audio) – i.e. characteristics. Clustering is also performed on similar visual characteristics. However, the scanning is done for the highest-scored remaining media, not explicitly matched against characteristics of items remaining in the data structure. The difference is taught by another reference below);
updating the data structure to replace the particular media item in the video montage (paragraphs 143 and 188-196 teaches a montage refreshing process in which media is determined to be removed or deleted altogether and the montage is updated); and
regenerating the video montage based on the updated data structure (paragraphs 143 and 188-197 teaches a montage refreshing process in which media is determined to be removed or deleted altogether and the montage is updated with the changes in the media being removed altogether).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to incorporate the teachings of Ramadorai into the system of Aguilar and Novikoff such that deleting media content results in a montage/summary that previously included the now deleted media to be refreshed such that the deletion is accounted for in the new montage as taught by Ramadorai is utilized in the propose combination’s own video summary/montage system because said incorporation allows for the benefit of saving time by creating automatic summaries (paragraph 134).
However, Aguilar, Novikoff and Ramadorai fails to teach, but in a similar endeavor, Hicken teaches the claimed: in response to determining that the particular media item has been deleted from the local media gallery (see paragraphs 8, 27 and 32 teaches unavailable from the database), scanning the local media gallery to identify a replacement media item having characteristics corresponding to characteristics of media items remaining in the data structure (paragraphs 7, 8, 32-34 and 35 teaches wherein replacement media is scanned for that best fits the “group profile … generated based on acoustic analysis of a plurality of songs in the playlist”, “playlist characterization”, replacement via “weighted combination of the individual acoustic analysis data and group profile data (e.g. profile data for the entire playlist)” and replacement songs are selected from “user’s existing collection” (local store)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to incorporate Hicken’s playlist characterization based replacement technique into the proposed combination of Aguilar, Novikoff and Ramadorai’s video montage combination because Hicken addresses the same underlying problem: maintaining the coherence/essence of a curated, themed media collection when one of its constituent items becomes unavailable, merely in a different by closely related media domain (audio playlists rather than video montage), and the claimed system itself already bridges these domains by selecting an audio track whose theme parameter corresponds to the theme of the visual media items, showing that a POSIT assembling such a system would naturally look to audio-recommendation and playlist management techniques as an analogous source of solutions. Applying Hicken’s known technique, deriving a group characterization from the items remaining in a data structure and using it to select a replacement for an unavailable member, to Ramadoria’s already known and disclosed deletion triggered montage refresh mechanism is simply the use of a known technique to improve a similar device (a curated, themed media compilation) in the same way, yielding the predictable result of replacement that preserves the overall character of the collection rather than merely resembling the item it replaces. Additionally, the benefit of the proposed combination is also expressly identified by Hicken because such an incorporation allows for the benefit of maintaining the essence of the selected media items in the playlist even when replaced (paragraph 15).
Regarding claim 2, Hicken teaches the claimed wherein the characteristics of media items remaining in the data structure comprise a theme parameter determined by performing a visual analysis on the media items remaining in the data structure or by analyzing metadata associated with media items remaining in the data structure (Examiner notes the alternative language in the claim. paragraphs 7, 8, 32-34 and 35 teaches wherein replacement media is scanned for that best fits the “group profile … generated based on acoustic analysis of a plurality of songs in the playlist”, “playlist characterization”, replacement via “weighted combination of the individual acoustic analysis data and group profile data (e.g. profile data for the entire playlist)” and replacement songs are selected from “user’s existing collection” (local store)). The prior motivation as discussed above is incorporated herein.
Regarding claim 4, Aguilar teaches wherein the number of media items are selected based on a particular time period or based on recency of the number of media items (paragraph 32 teaches time of capture).
Regarding claim 5, Novikoff teaches further comprising:
wherein generating the video montage comprises: generating individual video segments from each media item in the number of media items (paragraphs 0159 and Fig. 3, step 322 teaches assembling each of the selected video clips); and
assembling the individual video segments into the video montage based on an order specified in the data structure (Fig. 3, steps 320 and 322 determines the assembling of the video clips into a theme-based video). The prior motivation as discussed above is incorporated herein.
Regarding claim 6, Novikoff teaches further comprising:
causing display of the video montage (paragraphs 0095 and 0189);
receiving user input to remove or replace a particular media item in the video montage (paragraphs 0095 and 0189teaches editing);
updating the data structure to remove or replace the particular media item in the video montage (paragraphs 0095 and 0189teaches adding or removing); and
regenerating the video montage based on the updated data structure (paragraph 0095 and 0189 and EDL). The prior motivation as discussed above is incorporated herein.
Regarding method claims 8-9 and 11-13, they are rejected for the same reasons as discussed in systems claims 1-2 and 4-6 above, respectively.
Regarding medium claims 15-16 and 18-20, they are rejected for the same reasons as discussed in systems claims 1-2 and 4-6 above, respectively.
Claim 3, 7, 10, 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Aguilar et al. (US 2018/0174616) in view of Novikoff et al. (US 2018/0068019) in view of Ramadorai et al. (US 2018/0295396) and further in view of Hicken (US 2006/0265349) and further in view of Snibbe et al. (US 2015/0286716).
Regarding claim 3, Aguilar, Novikoff, Ramadorai and Hicken teaches the claimed as discussed in claim 1 above, however fails to teach, but Snibbe teaches wherein the theme parameter comprises a descriptor of an image augmentation (Fig. 5 and paragraphs 64-71 teaches wherein post processed audio and/or video effects augmented the with effect metadata are stored in the metadata database and stored with the media item generation database 344 as illustrated in Fig. 5. Paragraph 93-94 also teaches that the matching algorithm that selects a video clip based on the very same media item generation database 344, which includes the effects information 526).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to incorporate the teachings of Snibbe into the proposed combination of Aguilar, Novikoff, Ramadorai and Hicken so that video effects information associated with the video/media item is stored along with the file because said incorporation allows for the benefit of improving the accuracy of matching media items based on a user’s search (paragraph 136).
Regarding claim 7, Snibbe teaches the claimed wherein the image augmentation comprise augmented reality effects applied to media items (Fig. 5 and paragraphs 64-71 and paragraphs 93-94). The prior motivation as discussed above is incorporated herein.
Claims 10 and 17 are rejected for the same reasons as discussed in claim 3 above.
Method claim 14 is rejected for the same reasons as discussed in system claim 7 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GELEK W TOPGYAL whose telephone number is (571)272-8891. The examiner can normally be reached M-F (9:30-6 PST).
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, William Vaughn can be reached on 571-272-3922. 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.
/GELEK W TOPGYAL/Primary Examiner, Art Unit 2481
Prosecution Insights