DETAILED ACTION
This action is responsive to the Amendment filed on 07/07/2026. Claims 1-4, 7-11, and 14-18 have been amended. Claim 20 has been canceled. Claim 21 has been added. Claims 1-19 and 21 are pending in the case. Claims 1, 10, and 19 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/07/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-19 and 21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 8, 10-13, 17, 19, and 21 of copending Application No. 18/747,683 (reference application, hereinafter “the ‘683 application”). Although the claims at issue are not identical, they are not patentably distinct from each other because their functionalities are the same; the only difference between the claims of the ‘683 application and the claims of the instant application being that the claims of the ‘683 application appear to recite a “share action”/“send button” where the claims of the instant application appear to recite a “boost” action/button in claims 1, 8, and 15, and a “comment” action/button in claims 2, 9, and 16. Thus, it would have been obvious to adapt the labels assigned to the actions/buttons of the ‘683 application to arrive at the actions/buttons of the instant application. See, for example:
Instant Application
US Pat. App No. 18/747,683
1. A system comprising:
at least one processor;
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving, from a device associated with a user, a request for a media content item to display on the device;
calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items;
ranking the set of media content items based on the respective probabilities;
determining that the respective probability for the media content item is within a top number N of the ranked set of media content items;
detecting, in response to the determining, that the user did not previously perform the boost action for the media content item; and
providing, to the device and in response to the detecting, the media content item together with a first flag to update a display of a boost button in association with a display of the media content item on the device, the boost button being user-selectable to perform the boost action for the media content item.
2. The system of claim 1, the operations further comprising:
calculating a second probability that the user will perform a comment action for the media content item, based on at least one of the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items;
determining that the second probability meets a second threshold value;
detecting, in response to the determining that the second probability meets the second threshold value, that the user did not previously perform the comment action for the media content item; and
providing, to the device and in response to detecting that the user did not previously perform the comment action, a second flag to update display of a comment button in association with display of the media content item on the device, the comment button being user-selectable to perform the comment action for the media content item.
3. The system of claim 2, wherein calculating the respective probabilities comprises:
providing, to a machine learning model, the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items, the machine learning model having been trained with user profile information and prior user engagement data for second users, and with metadata for second media content items; and
receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the respective probabilities from the machine learning model.
4. The system of claim 3, wherein calculating the second probability comprises:
providing, to the machine learning model, the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items; and
receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the second probability from the machine learning model.
5. The system of claim 3, wherein the second users include the user.
6. The system of claim 3, wherein the second media content items include the media content item.
7. The system of claim 2,
wherein updating the display of the boost button comprises changing a color of the boost button, and
wherein updating the display of the comment button comprises changing a color of the comment button.
21. The system of claim 1,
wherein the set of media content items comprises a first predefined number of media content items of the content feed.
1. A system comprising:
at least one processor;
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving, from a device associated with a user, a request for a media content item to display on the device;
calculating respective probabilities that the user will perform a share action for a set of media content items of a content fee, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items;
ranking the set of media content items based on the respective probabilities;
determining that the respective probability for the media content item is within a top number N of the ranked set of media content items;
detecting, in response to the determining, that the user did not previously perform the share action for the media content item; and
providing, to the device and in response to the detecting, the media content item together with a flag to update a display of a send button in association with a display of the media content item on the device, the send button being user-selectable to perform the share action for the media content item.
{Claim 1}
calculating respective probabilities that the user will perform a share action for a set of media content items of a content fee, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items;
{…}
determining that the respective probability for the media content item is within a top number N of the ranked set of media content items;
detecting, in response to the determining, that the user did not previously perform the share action for the media content item; and
providing, to the device and in response to the detecting, the media content item together with a flag to update a display of a send button in association with a display of the media content item on the device, the send button being user-selectable to perform the share action for the media content item.
2. The system of claim 1, wherein the calculating comprises:
providing, to a machine learning model, the user profile information associated with the user, the prior user engagement by the user with other media content items, and the metadata associated with the other media content items, the machine learning model having been trained with user profile information and prior user engagement data for second users, and with metadata for second media content items; and
receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the probability from the machine learning model.
{Claim 2} wherein the calculating comprises:
providing, to a machine learning model, the user profile information associated with the user, the prior user engagement by the user with other media content items, and the metadata associated with the other media content items, the machine learning model having been trained with user profile information and prior user engagement data for second users, and with metadata for second media content items; and
receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the probability from the machine learning model.
3. The system of claim 2, wherein the second users include the user.
4. The system of claim 2, wherein the second media content items include the media content item.
8. The system of claim 1, wherein the send button comprises a send icon, and
wherein updating the display of the send button comprises changing a color of the send icon.
{claim 8}
wherein updating the display of the send button comprises changing a color of the send icon.
21. The system of claim 1,
wherein the set of media content items comprises a first predefined number of media content items of the content feed.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 8, 15, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fuloria et al. (US Patent Application Pub. No. 2013/0030905, hereinafter “Fuloria”).
As to independent claims 1, 8, and 15, Fuloria shows a system [¶ 26], a method [¶ 37], and a concomitant non-transitory computer-readable storage medium [¶ 77], comprising:
receiving, from a device associated with a user, a request for a media content item to display on the device [receiving, from a device associated with a user, a request for a “social networking object” to display on the device (¶¶ 08 & 37)];
calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items; ranking the set of media content items based on the respective probabilities; determining that the respective probability for the media content item is within a top number N of the ranked set of media content items [Respective probabilities that the user will perform a boost action for a set of media content items of a content feed may be calculated to determine at least a top ranked media content item. In other words, a set may be comprised of at least one media content item such that the “set” of one or more media content items may be ranked to determine at least the top/likeliest media content item. Fuloria also shows that “the determination as to whether to display the super-like button to a particular user depends on that user's prior credit or spending history; users who promote a large number of stories, or consistently promote stories pertaining to a particular subject, are more likely to promote similar stories in the future, and social networking system 100 should therefore display the “super-like” interface more frequently to such users.” (Fuloria: ¶ 37).
See also ¶¶ 40-43 & 48 for more respective probability calculations and ¶¶ 39-45 for ranking/weighing the set/at least one media content item.
For example, as to the ranking/weighting aspect, Fuloria shows that both before and after the respective probability that a user will perform a boost (like a “like” or “super like”) action for any given media content item is calculated, “weights 505 of the affinity function enable it to be used by various processes in the social networking system environment for different purposes. For example, when determining to which of a promoting user's friends to display a promoted social networking object, an ad algorithm may use the measure of affinity 515 function to determine which of a user's connections have the highest probability of being interested in the promoted social networking object. The measure of affinity 515 may then be based on those predictor functions 510 that show how interested the user is in viewing the content posted by another user as well as one or more predictor functions 510 that show how interested the user is in various actions that may be mentioned in the social endorsement. The ad algorithm would thus provide relatively large weights for these predictor functions 510 so that the resulting measure of affinity would more accurately determine which social endorsements would be more interesting to the viewing user. The ad algorithm would then select display the promoted story to the determined friends” (Fuloria: ¶ 45).];
detecting, in response to the determining, that the user did not previously perform the boost action for the media content item [fig. 8A; ¶¶ 61-62]; and providing, to the device and in response to the detecting, the media content item together with a first flag to update a display of a boost button in association with a display of the media content item on the device, the boost button being user-selectable to perform the boost action for the media content item [“FIG. 4 depicts the an example method of displaying a user-promoted story or piece of content via sponsored story application 301 and sponsored story system 304. In step 401, sponsored story application 301 receives a promoted social networking object from a user. In particular embodiments, promoted social networking objects may be called “super liked” social networking objects. In particular embodiments, social networking system 100 provides users a button for each story or piece of content allowing the user to “like” or “super-like” a given story or piece of content. In particular embodiments, the “super like” button is a pop-up dialog box that appears after a user has “liked” a social networking object prompting the user as to whether he or she would like to feature or “super like” the content for a given price. In particular embodiments, the probability of the “super-like” button appearing is based off social relevancy factors, such as whether the story is likely to be promoted by another user. For example, in particular embodiments, a social coefficient representing the degree of “closeness” between two users on the network may be utilized in determining whether to provide the “super-like” interface; two users who are close, and therefore have a high social coefficient, are more likely to promote each others' social networking objects. In particular embodiments, social networking system 100 takes into account the type of social networking objects and determines whether to provide the “super like” interface based on the story type. For example, personal milestones, such as starting a new job, having a change in relationship status, having a child or posting pictures relating thereto, are likely to be promoted by the user or other users. In particular embodiments, the determination as to whether to display the super-like button to a particular user depends on that user's prior credit or spending history; users who promote a large number of stories, or consistently promote stories pertaining to a particular subject, are more likely to promote similar stories in the future, and social networking system 100 should therefore display the “super-like” interface more frequently to such users. This disclosure contemplates any suitable method of providing an interface to a user for promoting social networking objects.” (¶ 37)].
As to dependent claim 21, Fuloria further shows:
wherein the set of media content items comprises a first predefined number of media content items of the content feed [the set of media content items comprises a first predefined number of media content items (at least one/top media content item) of the content feed (¶¶ 37, 40-43, & 48)].
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 2-7, 9-14, and 16-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Fuloria in view of Han et al. (US Patent Application Pub. No. 2020/0007944, hereinafter “Han”).
As to dependent claims 2, 9, and 16, Fuloria further shows:
calculating a second probability that the user will perform a comment action for the media content item, based on at least one of the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items; determining that the second probability meets a second threshold value; detecting, in response to the determining that the second probability meets the second threshold value, that the user did not previously perform the comment action for the media content item [fig. 8A; ¶¶ 61-62]; and providing, to the device and in response to detecting that the user did not previously perform the comment action, a second flag to update a display of a comment {…} in association with the display of the media content item on the device [“The predictor functions may predict whether a user will perform a particular action based on the user's interest in the action. For example, a predictor function may be derived based on a user's historical activity (such as interacting with other users' promoted social networking objects, for example). Additionally, the predictor function may include a decay factor that causes the strength of the signal provided by the user's historical activity to decay with time. The predictor functions may predict any number of actions, which may be within or outside of the social networking system. For example, these actions may include various types of a user's communications, such as messages, posting of content, and commenting on content; various types of a user's observation actions, such as viewing profiles of other connections and viewing photos and content posted by other connections; and various types of coincidence information about two or more users, such as being tagged in the same photograph, checked in at the same location, and attending the same event. A predictor function may be determined using a machine learning algorithms trained on historical activity and past user responses or data farmed from users by exposing them to various options and measuring responses.” (¶ 42)],
the comment button being user-selectable to perform the comment action for the media content item [“{…} Photo story 803 also includes the customary “like”, “comment”, and “share” buttons, allowing a viewer of photo story 803 to express an affinity for the story, comment on the album, or relink the album on his or her own feed, respectively. Photo story 803 includes “feature” button 803 a, which allows the user to promote the photo story. In particular embodiments, “feature” button 803 a may be a “super-like” button. {…}” (¶ 62)].
As shown above, Fuloria shows both displaying a selectable comment button and also calculating the probability that the user will actually perform said comment. Nonetheless, Fuloria does not appear to explicitly recite to “update a display of a comment button” as apparently intended. In an analogous art, Han shows:
providing{…} a second flag to update a display of a comment button in association with the display of the media content item on the device, the comment button being user-selectable to perform the comment action for the media content item [“{…} different types of icons may be represented by different colors or grayscales. For example, shock is represented by blue and happiness is represented by yellow. When the comment icon of shock has the greatest weight (the greatest input time density) at a certain moment (in the first time interval), the second comment icon is determined as the comment icon of shock, and blue is presented at the moment (the first time interval).” (¶ 72)
“FIG. 7 is a screen diagram of an exemplary application scenario of a method for displaying interactive attributes shown according to some embodiments of the disclosure. {…} a second comment icon having the greatest weight in each of the multiple first time intervals is acquired, and then for each first time interval, the second comment icon corresponding to the first time interval can be displayed. Different types of icons may be represented by different colors/grayscales, for example, shock is represented by dark gray and happiness is represented by light gray, so that the playing progress control region (the first time interval) where the comment icon of shock has the greatest weight is displayed in dark gray, and the playing progress control region (the first time interval) where the comment icon of happiness has the greatest weight is displayed in light gray.” (¶ 81)].
One of ordinary skill in the art, having the teachings of Fuloria and Han before them prior to the effective filing date of the claimed invention, would have been motivated to incorporate Han’s comment icon display updating techniques into Fuloria. The rationale for doing so would have been “to offer more relevant information to each user in view of the users' own stated interests” (Fuloria: ¶ 04) and/or to obtain “a system that is more efficient and beneficial for both the user and the system” (Fuloria: ¶ 05), especially when Han’s approach would allow Fuloria “to clearly and intuitively display interactions, improve the display effect, and enhance user experience” (Han: ¶ 04). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fuloria and Han (hereinafter, the “Fuloria-Han” combination) in order to obtain the invention as recited in claims 2, 9, and 16.
As to dependent claims 3, 10, and 17, Fuloria-Han further shows:
wherein calculating the respective probabilities comprises: providing, to a machine learning model, the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items, the machine learning model having been trained with user profile information and prior user engagement data for second users, and with metadata for second media content items; and receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the respective probabilities from the machine learning model [calculating the respective probabilities comprises: providing, to a machine learning model, the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items, the machine learning model having been trained with user profile information and prior user engagement data for second users, and with metadata for second media content items; and receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the respective probabilities from the machine learning model (Fuloria: ¶¶ 42 & 48)].
As to dependent claims 4, 11, and 18, Fuloria-Han further shows:
wherein calculating the second probability comprises: providing, to the machine learning model, the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items; and receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the second probability from the machine learning model [calculating the second probability comprises: providing, to the machine learning model, the user profile information associated with the user, the prior user engagement by the user with the other media content items, and the metadata associated with the other media content items; and receiving, in response to the providing of the user profile information, the prior user engagement, and the metadata to the machine learning model, the second probability from the machine learning model (Fuloria: ¶¶ 42 & 48)].
As to dependent claims 5, 12, and 19, Fuloria-Han further shows:
wherein the second users include the user [the second users include the user (Fuloria: ¶¶ 30, 33, 37, 40-42, & 47-48)].
As to dependent claims 6 and 13, Fuloria-Han further shows:
wherein the second media content items include the media content item [the second media content items include the media content item (Fuloria: ¶¶ 30, 33, 37, 40-42, & 47-48)].
As to dependent claims 7 and 14, Fuloria-Han further shows:
wherein updating the display of the boost button comprises changing a color of the boost button, and wherein updating the display of the comment button comprises changing a color of the comment button [updating the display of the boost button comprises changing a color of the boost button, and wherein updating the display of the comment button comprises changing a color of the comment button (Han: ¶¶ 72 & 81)].
Response to Arguments
Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues:
“ Fuloria fails to disclose each limitation of amended independent claim 1, particularly with respect to at least the claimed features of "calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item," "ranking the set of media content items based on the respective probabilities," and "determining that the respective probability for the media content item is within a top number N of the ranked set of media content items," as recited in currently amended independent claim 1.
Paragraph [0037] of Fuloria describes that "the probability of the 'super-like' button appearing is based off social relevancy factors, such as whether the story is likely to be promoted by another user."
However, this passage of Fuloria does not disclose "calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item" and "ranking the set of media content items based on the respective probabilities," much less "determining that the respective probability for the media content item is within a top number N of the ranked set of media content items."”
The Office respectfully disagrees. Fuloria shows respective probabilities that the user will perform a boost action for a set of media content items of a content feed may be calculated to determine at least a top ranked media content item. In other words, a set may be comprised of at least one media content item such that the “set” of one or more media content items may be ranked to determine at least the top/likeliest media content item. The instant specification corroborates that this is a reasonable interpretation when it states (in at least ¶¶ 86, 90, and 96 of the originally filed Specification version) that when ranking the top N media content items, N can be any value (which would include the top N=1, as taught by Fuloria). Fuloria also shows that “the determination as to whether to display the super-like button to a particular user depends on that user's prior credit or spending history; users who promote a large number of stories, or consistently promote stories pertaining to a particular subject, are more likely to promote similar stories in the future, and social networking system 100 should therefore display the “super-like” interface more frequently to such users.” (Fuloria: ¶ 37). See also ¶¶ 40-43 & 48 for more respective probability calculations and ¶¶ 39-45 for ranking/weighing the set/at least one media content item. For example, as to the ranking/weighting aspect, Fuloria shows that both before and after the respective probability that a user will perform a boost (like a “like” or “super like”) action for any given media content item is calculated, “weights 505 of the affinity function enable it to be used by various processes in the social networking system environment for different purposes. For example, when determining to which of a promoting user's friends to display a promoted social networking object, an ad algorithm may use the measure of affinity 515 function to determine which of a user's connections have the highest probability of being interested in the promoted social networking object. The measure of affinity 515 may then be based on those predictor functions 510 that show how interested the user is in viewing the content posted by another user as well as one or more predictor functions 510 that show how interested the user is in various actions that may be mentioned in the social endorsement. The ad algorithm would thus provide relatively large weights for these predictor functions 510 so that the resulting measure of affinity would more accurately determine which social endorsements would be more interesting to the viewing user. The ad algorithm would then select display the promoted story to the determined friends” (Fuloria: ¶ 45).
Therefore, the Office respectfully asserts that the cited art sufficiently teaches the limitations recited in the amended claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicants are reminded of the extension of time policy as set forth in 37 C.F.R. § 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 extension fee pursuant to 37 C.F.R. § 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Inventor
Document ID
Relevance
Ding; Shilin et al.
US 20180276533 A1
“calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items; ranking the set of media content items based on the respective probabilities; determining that the respective probability for the media content item is within a top number N of the ranked set of media content items”
Erant; Ergin et al.
US 20180081500 A1
“calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items; ranking the set of media content items based on the respective probabilities; determining that the respective probability for the media content item is within a top number N of the ranked set of media content items”
Xiong; Liang et al.
US 20190114528 A1
“calculating respective probabilities that the user will perform a boost action for a set of media content items of a content feed, the set including the media content item, the respective probabilities being calculated based on at least one of user profile information associated with the user, prior user engagement by the user with other media content items, and metadata associated with the other media content items; ranking the set of media content items based on the respective probabilities; determining that the respective probability for the media content item is within a top number N of the ranked set of media content items”
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/ALVARO R. CALDERON IV/
Examiner
Art Unit 2171
/KIEU D VU/ Supervisory Patent Examiner, Art Unit 2171
