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 .
With regards to the prior 35 USC § 101 rejections, they are withdrawn in view of applicant’s amendments.
The following rejections are withdrawn in view of applicant’s amendments:
Claim(s) 1, 9, 10, 18, 19 and 20 rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022).
Claim(s) 2, 3, and 6 - 8 rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022) in view of Wang et al (US Patent: 10055465, issued: Aug. 21, 2018, filed: Sep. 9, 2016).
Claim(s) 13 – 15 rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022) in view of Valdivia et al (US Application: US 20170149714, published: May 25, 2017, filed: Nov. 23, 2015).
Claim(s) 4 and 5 rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022) in view of Wang et al (US Patent: 10055465, issued: Aug. 21, 2018, filed: Sep. 9, 2016) in view of Underwood et al (US Patent: 11150782, issued: Oct. 19, 2021, filed: Mar. 19, 2019).
Claim(s) 16 rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022) in view of Srinivasan et al (US Application: US 2017/0147581, published: May 25, 2017, filed: Nov. 24, 2015).
Claim(s) 17 rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022) in view of Lin et al (US Application: US 20210272253, published: Sep. 2, 2021, filed: Feb. 27, 2020) in view of Roessler et al (US Patent: 10582125, published: Mar. 3, 2020, filed: Jun. 1, 2015).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/15/2025, 02/18/2026 and 05/19/2026 are being considered by the examiner.
Allowable Subject Matter
Claims 4, 5, 11 - 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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, 9, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016).
With regards to claim 1, Loui et al teaches a method (paragraph 0018: a computer implemented method using a processor and memory is implemented) comprising:
accessing, by an interaction application, a plurality of previously captured content items; (paragraphs 0024, 0031 and 0032: a plurality of frame images are accessed)
identifying a set of content items of the plurality of previously captured content items that match one or more criteria corresponding to sharable content (Fig. 5, paragraph 0030 and 0031: a set of key frame images (content items) are identified/indexed that match one or more topic criteria and also based on additional evaluation quality metrics);
ranking the set of content items (paragraph 0024, paragraph 0031 and 0032: a select set of content items (key frame images) are designated as ranked/prioritized above the other captured content images for collage generation. Additionally, the keyframe images have ranking metadata that values/ranks them in time/chronological-order);
analyzing each of the ranked set of content items by a … model to automatically modify each content item in the ranked set of content items, a unique modification being applied to each content item in the ranked set of content items (paragraphs 0027 and 0029, Fig. 4d: the key frames in the set of key frames can also have their own unique modification/rotation applied to them ); and
generating a shareable content item feed comprising the ranked set of modified content items (paragraph 0028, Fig. 3: the collage can be shared in a social media feed/posting).
However Loui et al does not expressly teach … after identifying the set of content items that match the one or more criteria, ranking…; analyzing … by a generative machine learning model to automatically modify each content item … .; … generating … the shareable content item feed causing each modified content item in the set of modified content items to be presented in a sequence in which one modified content item being presented in full screen of the interaction application, and a gesture for accessing the shareable content item feed comprising a first swipe up gesture.
Yet Boyd et al teaches after identifying the set of content items that match the one or more criteria, ranking…; to automatically modify each content item … (column 9, lines 10-23, column 10, lines 5-10, column 11, lines 40-55: a set of media content (including images and/or video, etc) is/are selected based on narrative group and ranking) .; … generating … the shareable content item feed causing each modified content item in the set of modified content items to be presented in a sequence in which one modified content item being presented in full screen of the interaction application (column 12, lines 15-27, 63-67 and column 13, lines 1-20: the user can selectively apply edits to modify each content item if desired and the content item is part of a sequence/collection presented as a story (that can be displayed in full screen mode)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al’s ability to select and generate shared media content such selection and generation of shared media content further includes an ability to first identify content items that match criteria and rank the media for sharing, as taught by Boyd et al. The combination would have allowed a user to easily share a collection of content , even when there is a large amount of content.
However the combination does not teach analyzing … by a generative machine learning model to automatically modify each content item …; … , and a gesture for accessing the shareable content item feed comprising a first swipe up gesture.
Yet Boyd2 et al teaches analyzing … by a generative machine learning model to automatically modify each content item … (paragraph 0067, 0074, 0075: a neural network along with an animation model enables generation of modification(s) to content (such as detecting a first object/face in a content item and applying a smile augmentation to the object/face based upon user edit/modification preference/input ).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al and Boyd et al’s ability to initially share media content items based upon a matching, ranking, and modification in a collection/story-form; such that the modification of content items could have been based on usage of a generative machine learning model, as taught by Boyd2 et al. The combination would have allowed users to be presented with shared content that is interest/engaging and also curated.
However the combination does not expressly teach … , and a gesture for accessing the shareable content item feed comprising a first swipe up gesture.
Yet Greenberg et al teaches … , and a gesture for accessing the shareable content item feed comprising a first swipe up gesture (paragraph 0108, 0111, 0444, 0448: it is well known to implement a gesture to swipe up on shared content to access a shared collection of items in a feed is taught such that the items can be displayed in full screen mode if desired).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al and Boyd2 et al’s ability to share a collection/story of content on a feed, such that the feed could be displayed and navigated in full screen mode as a result of using a swipe gesture (such as swipe up), as taught by Greenberg et al. The combination would have made it easier for content providers to create a package of media content that would also have a functionality to convey a compelling narrative that is intuitive to navigate.
With regards to claim 9. The method of claim 1, the combination of Loui et al, Boyd et al, Boyd2 et al and Greenberg et al teaches wherein analyzing each of the ranked set of content items by the generative machine learning model comprises: detecting a depiction of a first object in a first content item of the set of content items; automatically selecting an augmented reality experience based on preferences of a user of the interaction application and that corresponds to the first object; and automatically modifying the first object in the first content item using the augmented reality experience to generate the first modified content item in the shareable content item feed, as similarly explained in the rejection of claim 1 ( paragraph 0067, 0074, 0075: a neural network enables generation of modification(s) to content (such as detecting a first object/face in a content item and applying a smile augmentation to the object/face based upon user edit/modification preference/input ), and is rejected under similar rationale.
With regards to claim 18. The method of claim 1, the combination of Loui et al, Boyd et al, Boyd2 et al and Greenberg et al teaches further comprising: processing the set of content items by the generative machine learning model to identify a burst collection of images that were captured within a specified time period; and converting the burst collection of images into an animation for inclusion in the shareable content item feed , as already explained in the rejection of claim 1 (Loui et al, Boyd et al and Boyd2 et al were both combined in the rejection to show that the processed content items could be a collection of images (frames in video data or a series of images) and those images could be modified and shared based upon user preferences/settings. Additionally, as explained in the rejection of claim 1, Loui et al, Boyd et al, Boyd2 et al and Greenberg et al were explained to show that shared modified content can include video (interpreted as animation)), and is rejected under similar rationale.
With regards to claim 19 the combination of Loui et al, Boyd et al, Boyd2 et al and Greenberg et al teaches a system comprising: at least one processor; and at least one memory component having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: accessing, by an interaction application, a plurality of previously captured content items; identifying a set of content items of the plurality of previously captured content items that match one or more criteria corresponding to sharable content; after identifying the set of content items that match the one or more criteria, ranking the set of content items; analyzing each of the ranked set of content items by a generative machine learning model to automatically modify each content item in the ranked set of content items, a unique modification being applied to each content item in the ranked set of content items; and generating a shareable content item feed comprising the ranked set of modified content items, the shareable content item feed causing each modified content item int eh set of modified content items to be presented in a sequence in which one modified content item replaces display of another modified content item, a first modified content item being presented in full screen of the interaction application, and a gesture for accessing the shareable content item feed comprising a first swipe up gesture, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 20, the combination of Loui et al, Boyd et al, Boyd2 et al and Greenberg et al teaches a non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: accessing, by an interaction application, a plurality of previously captured content items; identifying a set of content items of the plurality of previously captured content items that match one or more criteria corresponding to sharable content; after identifying the set of content items that match the one or more criteria, ranking the set of content items; analyzing each of the ranked set of content items by a generative machine learning model to automatically modify each content item in the ranked set of content items, a unique modification being applied to each content item in the ranked set of content items; and generating a shareable content item feed comprising the ranked set of modified content items, the shareable content item feed causing each modified content item int eh set of modified content items to be presented in a sequence in which one modified content item replaces display of another modified content item, a first modified content item being presented in full screen of the interaction application, and a gesture for accessing the shareable content item feed comprising a first swipe up gesture, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
Claim(s) 2, 3, and 6 - 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016) in view of Wang et al (US Patent: 10055465, issued: Aug. 21, 2018, filed: Sep. 9, 2016).
With regards to claim 2. The method of claim 1, Loui et al, Boyd et al, Boyd2 et al and Greenberg et al teaches … the plurality of previously captured content items, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach … further comprising: generating a new sharable content item feed periodically or in response to detecting that a new content item has been added to the plurality of previously captured content items.
Yet Wang et al teaches … further comprising: generating a new sharable content item feed periodically or in response to detecting that a new content item has been added to the plurality of previously captured content items (column 1, lines 45-52, column 5, lines 5-37, Fig 5A: a new content item is generated when there are new/updated information available and presented for a requested content feed and a scroll gesture/input is detected to present one or subsequent more content items (that could have been modified in rank/order) based on how the user scrolls to subsequent content items).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified the combination of Loui et al, Boyd et al, Boyd2 et al and Greenberg et al’s ability to initiate modification/generation and subsequent sharing of a feed of content-items, such the sharing of content (the feed) would have been updated with new generated content items when there is new content available, as taught by Wang et al. The combination would have allowed Loui et al and Blackburne et al to have provided better user experience by providing new content without initiating navigational input (Wang et al, column 1, lines 30-37).
With regards to claim 3. The method of claim 1, the combination of Loui et al, Boyd et al, Boyd2 et al, Greenberg et al and Wang et al teaches further comprising: receiving a request to access a content sharing user interface; in response to receiving the request, presenting an image in the content sharing user interface; detecting a gesture corresponding to accessing the shareable content item feed; and in response to detecting the gesture, presenting a first modified content item from the shareable content item feed in the content sharing user interface (as similarly explained in the rejection of claim 2 (Wang et al, column 1, lines 45-52, column 5, lines 5-37, Fig 5A: a new content item is generated when there are new/updated information available and presented for a requested content feed and a scroll gesture/input is detected to present one or subsequent more content items (that could have been modified in rank/order) based on how the user scrolls to subsequent content items), and is rejected under similar rationale).
With regards to claim 6. The method of claim 3, the combination of Loui et al, Boyd et al, Boyd2 et al, Greenberg et al and Wang et al teaches presenting .. the ranked set of modified content items in the sharable content item feed, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
However the combination explained in the rejection of claim 3, did not address further comprising: analyzing dwell time of the user while presenting some of the ranked set of modified content items in the sharable content item feed; determining that a dwell time associated with an individual modified content item of the sharable content item feed transgresses a threshold value; in response to determining that the dwell time associated with the individual modified content item transgresses the threshold value, determining one or more attributes associated with the individual modified content item; and dynamically adjusting the sharable content item feed based on the one or more attributes associated with the individual modified content item.
Yet Wang et al further teaches analyzing dwell time of the user while presenting some of the ranked set of modified content items in the sharable content item feed; determining that a dwell time associated with an individual modified content item of the sharable content item feed transgresses a threshold value; in response to determining that the dwell time associated with the individual modified content item transgresses the threshold value, determining one or more attributes associated with the individual modified content item; and dynamically adjusting the sharable content item feed based on the one or more attributes associated with the individual modified content item (column 13, lines 55-67 and column 14, lines 1-37: scroll speed (interpreted as screen dwell time) is compared against a threshold (average) and in response to the scrolling of a content item going beyond bounds considered as average, the sharable content item feed is then adjusted based upon consumption time (and type) attribute/meta data that corresponds to content items in the list and reordering /adjusting is performed such that the content items in the feed are presented (traversal-top to downward direction) based upon referencing/searching the consumption-time and type associated with the content items).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified the combination of Loui et al, Boyd et al, Boyd2 et al, Greenberg et al, and Wang et al’s ability to present a ranked set of modified content items in a sharable content item feed through at least analysis with a generative machine model, such that dwell time is processed to determine whether to adjust generated content of the sharable content item feed and reordering (modifying order) of generated content from top to subsequent traversal, as also taught by Wang et al. The combination would have allowed Loui et al, Boyd et al, Boyd2 et al , Greenberg et al and Wang et al to have allowed Loui et al, Blackburne et al and Wang et al to have provided better user experience by providing new content without initiating navigational input (Wang et al, column 1, lines 30-37).
With regards to claim 7. The method of claim 6, the combination of Loui et al, Boyd et al, Boyd2 et al , Greenberg et al and Wang et al teaches wherein adjusting the sharable content item feed comprises: searching for a collection of content items associated with the one or more attributes; analyzing the collection of content items by the generative machine learning model to automatically modify each content item in the collection of content items; and adding the modified content items in the collection of content items to a top of the shareable content item feed to present the modified content items in the collection of content items before other content items that are in the shareable content item feed, as explained in the rejection of claim 6 (see how Wang et al was further combined with the combination of Loui et al, Blackburne et al and Wang et al to perform order modification of contents of the content item feed), and is rejected under similar rationale.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016) in view of Wang et al (US Patent: 10055465, issued: Aug. 21, 2018, filed: Sep. 9, 2016) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022)..
With regards to claim 8. The method of claim 7, the combination of Loui et al, Boyd et al, Boyd2 et al , Greenberg et al and Wang et al teaches searching for the collection of content items associated with the one or more attributes, in the rejection of claim 7, and is rejected under similar rationale.
However the combination of Loui et al, Boyd et al, Boyd2 et al , Greenberg et al and Wang et al applied in the rejection of claim 7 did not expressly teach wherein the one or more attributes indicate a depiction of a first face and a second face of a first person and a second person, respectively, in the individual modified content item
Yet Blackburne et al further teaches wherein the one or more attributes indicate a depiction of a first face and a second face of a first person and a second person, respectively, in the individual modified content item (paragraphs 0053 and 0075-0076, 0117, 0119, 0121: a first person’s face and a second person’s face is recognized in a augmented modified content for tracking and sharing).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al , Greenberg et al and Wang et al’s ability to search for the collection of content items associated with the one or more attributes, such that the attributes would have further included a depiction of a first and second face, as also taught by Blackburne et al. The combination would have enhanced and seamlessly produced augmented content for consumption by users of social media.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016) in view of Blackburne et al (US Application: US 20230360282, published: Nov. 9, 2023, filed: May 5, 2022).
With regards to claim 10. The method of claim 9, the combination of Loui et al, Boyd et al, Boyd2 et al and Greenberg et al teaches further comprising: … the set of content items; … the shareable content item feed, as similarly explained in the rejection of claim 9, and is rejected under similar rationale.
However the combination does not expressly teach … detecting a depiction of a second object in a second content item of the set of content items; automatically generating text or graphical elements based on the preferences of the user of the interaction application; and automatically overlaying the text or graphical elements on a portion of the first content item to generate a second modified content item in the shareable content item feed.
Yet Blackburne et al teaches … detecting a depiction of a second object in a second content item of the set of content items; automatically generating text or graphical elements based on the preferences of the user of the interaction application; and automatically overlaying the text or graphical elements on a portion of the first content item to generate a second modified content item in the shareable content item feed (Blackburne et al, paragraphs 0053 and 0075-0076) to modify the first face object that is augmented with graphical astronaut effects (thus yielding a first modified content item) and also to modify a second face object from a second video content item(s) that is also augmented /modified with astronaut effects (thus yielding a second modified content item). These modified content items are combined and can be shared on a social media feed (paragraph 0117, 0119, 0121: captured images can be shared)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al and Greenberg et al’s ability to generate a first modified content item (from a fist content item) in a shareable content feed, such that the generated content items can further include a content item that takes into account a second object within the set of content items as a precursor to generating and overlaying text of graphical elements on a portion of the first content item, as taught by Blackburne et al. The combination would have enhanced and seamlessly produced augmented content for consumption by users of social media.
Claim(s) 14 - 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016) in view of Valdivia et al (US Application: US 20170149714, published: May 25, 2017, filed: Nov. 23, 2015).
With regards to claim 14. The method of claim 1, the combination of Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al teaches wherein the plurality of previously captured content items corresponds to content items that have been captured by the interaction application … and wherein ranking the set of content items comprises prioritizing a first set of content items over a second set of content items in response to determining that the first set of content items has been captured more recently than the second set of content items , as similarly explained in the rejection of claim 1 (Loui et al was explained that images that occur prior to other images (chronologically) are ordered in chronological order when shared), and is rejected under similar rationale.
However the combination does not expressly teach content items that have been captured … within a specified prior time interval …
Yet Valdivia et al teaches content items that have been captured … within a specified prior time interval … (paragraph 0063, 0073 and 0079: items that are posted within a period of time are clustered to a particular event posting and ordered can be chronologically)
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al’s ability to include captured content item(s) to a shareable item feed, such additional consideration is taken for content items that are within a time interval with respect to posting in the feed, as taught by Valdivia et al. The combination would have allowed improvement in presentation of related posts and encouraged increased social networking activity (Valdivia et al, paragraph 0006).
With regards to claim 15. The method of claim 1, Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al teaches further comprising: receiving, by the interaction application, a request from a user to share content with one or more other users ….; … generating a shareable content item feed that includes modified content items identified, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However, the combination does not expressly teach … to share content with one or more other users that are part of a conversation with the user; generating the one or more criteria based on identifiers of the user and the one or more other users; and generating a shareable content item feed that includes modified content items identified based on the identifiers of the user and the one or more other users and preferences of the user and the one or more other users.
Yet Valdivia et al teaches … to share content with one or more other users that are part of a conversation with the user; generating the one or more criteria based on identifiers of the user and the one or more other users; and generating a shareable content item feed that includes modified content items identified based on the identifiers of the user and the one or more other users and preferences of the user and the one or more other users (paragraph 0082: content shard for a content feed are shared based upon identification of particular users that are allowed to have access to the shared content).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al’s ability to generate a content item feed having modified content items through user sharing, such that the sharing of modified content would be associated/shared with identified users, as taught by Valdivia et al. The combination would have allowed Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al to have implemented a customizable and granular level of applying /implementing access policies.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016) in view of Srinivasan et al (US Application: US 2017/0147581, published: May 25, 2017, filed: Nov. 24, 2015).
With regards to claim 16. The method of claim 1, the combination of Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al teaches further comprising: identifying, by the generative machine learning model, a set of content item modifications … the ranked set of content items … shared content items, as similarly explained in the rejection of claim 1 (it is noted that the rejection of claim 1 further explains the content item modifications of Loui et and Blackburne et al were at least two different modifications), and is rejected under similar rationale.
However the combination of Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al does not expressly teach .. a set of content item modifications that have been applied to previously shared content items; and automatically selecting different modifications to apply to each of the ranked set of content items from the set of content item modifications that have been applied to the previously shared content items.
Yet Srinivasan et al teaches .. [prior user content authoring preferences] that have been applied to previously shared content items; and automatically selecting [based upon prior user content authoring preferences] to apply to each …. set of content items from the set of content item modifications that have been applied to the previously shared content items (paragraph 0051: different prior content authoring content applied by the user for previous shared content item(s) are analyzed and those different prior content authoring content can also be applied to content items being shared).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al’s ability to apply different modifications to content items for sharing, such that prior shared content could be analyzed to apply the different modifications to subsequently shared content, as taught by Srinivasan et al. The combination would have allowed a user to quickly and easily share content items (Srinivasan et al, paragraph 0027)
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Loui et al (US Application: US 2022/0222876, published: Jul. 14, 2022, filed: Jul. 27, 2017) in view of Boyd et al (US Patent: 10693819, issued: Jun. 23, 2020, filed: Feb. 26, 2018) in view of Boyd2 et al (US Application: US 2022/0103495, published: Mar. 31, 2022, filed: Sep. 30, 2021) in view of Greenberg et al (US Application: US 2016/0103805, published: Apr. 14, 2016) in view of Lin et al (US Application: US 20210272253, published: Sep. 2, 2021, filed: Feb. 27, 2020) in view of Roessler et al (US Patent: 10582125, published: Mar. 3, 2020, filed: Jun. 1, 2015).
With regards to claim 17. The method of claim 1, Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al teaches further comprising: processing the set of content items by the generative machine learning model … a .. image for inclusion in the shareable content item feed; … identifying by the generative machine learning model a video … selecting a single frame from the video that satisfies a quality metric …. adding the … image to the shareable content item feed, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not teach … to identify a collection of images that are similar; blending the collection of images into a first image for inclusion in the shareable content item feed; identifying … a video in the set of content items; removing one or more objects from the single frame and repairing distortion in the single frame to generate a second image; and adding the second image to the shareable content item feed.
Yet Lin et al teaches … to identify a collection of images that are similar; blending the collection of images into a first image for inclusion in the shareable content item feed
(paragraph 0028 , 0126 and abstract: a collection of images are compared against each other to determine face similarity and a missing person could be identified for blending of the images to form a composite image that can be shared)
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al’s ability to process, using a generative machine learning model, one or more content items (for sharing in a content item feed), such that the generated content items used for sharing undergo further processing such as blending, as taught by Lin et al. The combination would have allowed reduction of issues when merging people from different images (Lin et al, paragraph 0005).
However the combination does not expressly teach identifying … a video in the set of content items; selecting a single frame from the video … removing one or more objects from the single frame and repairing distortion in the single frame to generate a second image …
Yet Roessler et al teaches identifying … a video in the set of content items; selecting a single frame from the video … removing one or more objects from the single frame and repairing distortion in the single frame to generate a second image … (column 17, lines 20-47: a frame is selected and the frame is repaired by removing distortions and/or blur).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Loui et al, Boyd et al, Boyd2 et al , and Greenberg et al’s ability to process, using a generative machine learning model, one or more content items (for sharing in a content item feed), such that the generated content items used for sharing undergo further processing such as distortion processing, as taught by Roessler et al. The combination would have allowed enhanced the quality and content of an image for better user experience (Roessler et al, column 1, lines 15-20).
Response to Arguments
Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive.
With regards to applicant’s argument concerning the independent claims, the applicant argues the prior combination of Loui and Blackburne fails to teach the newly amended limitations in the independent claims. However, the amendments have changed the scope of the invention, which necessitated a new grounds of rejection. The examiner respectfully directs the applicant’s attention to how a new combination (Loui et al, Boyd et al, Boyd2 et al and Greenberg et al) is applied to teach the independent claim(s).
The applicant argues the other claims that depend directly or indirectly upon one or more of the independent claim(s) are allowable; however, this argument is not persuasive since the independent claims have been shown/explained to be rejected above.
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.
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/WILSON W TSUI/Primary Examiner, Art Unit 2172
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