DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the amendment filed on 12/01/2025, Claims 1-6, 8, 10-15, 17, 19-24,26 and 29-31 are currently pending.
Applicant Response
In Applicant’s response dated 03/26/2025, Applicant amended Claims 1-6, 8, 10-15,17, 19-24, 26 and 29-31 and cancelled claims 7, 16, and 25 and argued against all objections and rejections previously set forth in the Office Action dated 06/30/2025.
Continued Prosecution Application
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/01/2025 has been entered.
Examiner Comments
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.
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 1-6, 8, 10-15,17, 19-24, 26 and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over Erman et al. (Pub. No.: US 20110307354 Al, Pub. Date: Dec. 15, 2011) in view of Zhou (Pub. No.: US 20150206183 A1, Pub. Date: July 23, 2015) in view of Paleja (Pat. No.: US 8914399 B1, Pub. Date: 2014-12-16) in view of Lewis (Pub. No.: US 9392312 B1, Pub. Date: 2016-07-12).
Regarding independent Claim 1,
Erman teaches a method implemented on at least one machine, each of which has at least one processor, storage, and a communication platform connected to a network for deploying applications (see Erman: Fig.1, [0020], mobile device 110 includes a processor 410, a memory 420, connected to mobile network 130 for deploying applications form application server guide 120), comprising the steps of:
obtaining, by an application eco space operator, interests of a user (see Erman: Fig.2A, [0058], stating that “At step 212, user information is received (obtained). The user information includes any information associated with the user which may be analyzed in order to determine the user profile information for the user (e.g., personal information of the user, preferences of the user, and the like, as well as various combinations thereof).”
recommending, to the user by the application eco space operator and based on the interests of the user, first recommended applications identified from a plurality of applications (see Erman: Fig.6A, [0157], “The recommended applications tab 610 of exemplary AppGuide 600 displays at least a portion of the recommended application information. In one embodiment, recommended applications tab 610 displays a list of recommended applications 611. The list of recommended applications 611 may include any suitable recommended application information associated with recommended applications, such as one or more of the application names of the recommended applications, the application icons of the recommended applications, the like, as well as various combinations thereof. The list of recommended applications 611 may include less or more, as well as different, information associated with the recommended applications included in the list of recommended applications 611.), wherein
each of the plurality of applications includes one or more [media] content (see Erman: Fig.6A, [0199], “recommending applications to users, in other embodiments the principles of the application recommendation capability may be utilized for recommending other types of content to users (e.g., music, television programs, movies, software programs, video games, and the like))”. In other words, Erman’s media application provides media content streams (e.g., music, television programs, movies, software programs, video games, and the like”).
each [media content] is associated with an interest within the interests of the user (see Erman: Fig.6A, [0199], “The user profile information for a user may include and/or be may be derived from any suitable raw user information, such as personal information of the user (e.g., gender, age, and the like), user preference information (e.g., the interests, hobbies, favorite types of music, favorite television programs, favorite movie genres, favorite types of applications, and like user preference information), user activity information (e.g., Internet browsing history that is monitored and logged on the mobile device of the user and/or within the network, the types of applications purchased and used by the user, the characteristics of specific applications purchased and used by the user, and like user activity information), and the like,”), and
each [media content] is an audio channel or a video channel (see Erman: Fig.6A, [0199], “recommending applications to users, in other embodiments the principles of the application recommendation capability may be utilized for recommending other types of content to users (e.g., music, television programs, movies, software programs, video games, and the like))”.
personalizing, by the application eco space operator, each of the recommended applications by, selecting, based on the interests of the user and for each of the recommended applications, at least one of the one or more [media content] to be recommended to the user (see Erman: Fig.2C, [0090], “method 230 may be executed periodically, in response to a request received from the user device of the user (e.g., where the mobile device may pull the recommended application information via a request initiated automatically by the user device, manually by a user of the user device, and the like), based on the usage pattern of the user, in response to an indication that user profile information of the user has changed, in response to an indication that one or more new applications are available and have been profiled, and the like, as well as various combinations thereof.”), resulting in at least one recommended [media content] for each application in the recommended applications (see Erman: Fig.2C, [0083], “selection of recommended applications for a user is performed using user profile information and application profile information. In one embodiment, selection of recommended applications for a user includes steps of (1) receiving user profile information of the user (e.g., from a database storing the user profile information), (2) receiving application profile information of the available applications (e.g., from a database storing the application profile information), and (3) selecting available applications for the user, as recommended applications to be recommended to the user, using the application profile information and the user profile information,”) and
displaying, by the application eco space operator and via a user interface, a personalized application eco space for the user (see Erman: Fig.6A-6D, [0157], “The recommended applications displayed in the recommended applications tab 610 may be arranged in any order. In one embodiment, for example, the recommended applications may be arranged in an order from highest probability match to lowest probability match based on the matching of the user profile information and the application profile information.”), the personalized application eco space comprising:
each of the recommended applications (see Erman: Fig.6A-6D, [0165], “the list of recommended applications 611 includes a listing of five applications recommended to the user.”); and
content of the corresponding at least one channel for each application in the recommended applications (see Erman: Fig.6A-6D, [0199], “application recommendation capability may be utilized for recommending other types of content to users (e.g., music, television programs, movies, software programs, video games, and the like).”
wherein the displaying comprises generating a tab for each of the recommended applications on the user interface so that the user can selectively activate any of the recommended applications without exiting other of the recommended applications (see Erman: Fig.6A-6D, [0159], “The recommended applications displayed in the recommended applications tab 610 are selectable from the recommended applications tab 610. The recommended applications displayed in recommended applications tab 610 may be selected for initiating various actions.”)
obtaining, by the application eco space operator after the displaying of the personalized application eco space, user interaction data based on user interactions with a first channel in a first application of the plurality of applications (see Erman: Fig.6A-6D, [0192], “user information tab 640 may include a user information monitoring permissions button 643 which, when selected, provides a capability for the user to set one or more user information monitoring permissions utilized by the MD 110 for automatically collecting user information at the MD 110.”)
As shown above, Erman teaches all the limitations shown above. Erman further teaches in [0199] recommending other types of content to users (e.g., music, television programs, movies, software programs, video games, and the like). Furthermore, Erman teaches a system that have a recommendation capability uses an application guide server that is configured for selecting recommended applications for a user and for providing recommended application information associated with the recommended applications to the user. Erman discloses applications providing recommendation of media content such as music, video, television programs and other media streams ([0038-0041]. However, Erman does not disclose or teach organizing content within each application into “channels.”
Erman does not teach or disclose the system wherein:
each of the plurality of applications includes one or more channels of content, wherein each channel in the one or more channels is associated with an interest within the interests of the user, and wherein each channel in the one or more channels is an audio channel or a video channel;
at least one of the one or more channels to be recommended to the user, resulting in at least one recommended channel for each application in the recommended applications
adjusting, by the application eco space operator, at least one of the recommended applications based on the user interaction data, resulting in a new channel being recommended in a second application of the plurality of applications, wherein the second application is distinct from the first application,
wherein the new channel was not previously recommended by the application eco space operator or previously viewed by the user; and
wherein the first channel is subscribed by the user, and the new channel has similar content as the first channel but is not subscribed by the user.
However, Zhou teaches the system wherein:
each of the plurality of applications includes one or more channels of content (see Zhou: Fig.5, [0048], “users are facilitated to create channels based on their interests, for example, by the channel initiating module 408. The interests may be defined in various forms, such as but not limited to, keywords, topics/categories, content sources, exemplary documents, persons, entities, brands, social account identifications, interest tags in social accounts, Wikipedia entries, social roles, demographics, etc. The interest may be explicitly inputted by the users and/or identified and recommended to users by the interest channel platform 402 based on the user's profile and online behavior.”), wherein each channel in the one or more channels is associated with an interest within the interests of the user (see Zhou: Fig.6, [0052], “The channel initiating module 408 in this example includes an interest discovering unit 602, an interest grouping unit 604, and a channel creating unit 606. The interest discovering unit 602 is configured to collect information related to the user 404 and identify one or more implicit interests based on the collected information related to the user 404.”) and wherein each channel in the one or more channels is an audio channel or a video channel (see Zhou: Fig.4, [0047], “The presented content may be determined based on relevance between each piece of content and the associated interest of the channel. Content may be organized and presented based on user interests. For example, in one channel, the channel presenting module 414 not only presents news, articles, but also present photo galleries, videos, discussions, ads, social feeds, and people with similar interests to users. The channel sharing module 416 in this example is responsible for facilitating other users (followers) 422 to follow the channels 406 based on the interests associated with the channels 406.”)
at least one of the one or more channels to be recommended to the user, resulting in at least one recommended channel for each application in the recommended applications (see Zhou: Fig.11, [0063], “The channel sharing recommendation unit 1102 is configured to identify other users who may be interested in the channels created by the user 404 (channel owner). The identification may be achieved by, for example, analyzing the explicit and implicit interests of the potential followers and the social relationship between the channel owner and the potential followers. Once identified, the channels may be recommended to the potential followers to follow.”)
Because Erman and Zhou are in the same field of endeavor of App or content Recommendation based on user interest, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify Erman’s media applications into Zhao’s channel-based content structure to improve content organization and personalization style. After modification of Erman, the Application Guide (AppGuide) 600 that recommend application to the user can also incorporate the mechanism of providing content channels and interest-based content channel recommendation mechanism as taught by Zhou. One would have been motivated to make such a combination in order to provide an improved solution for facilitating users to discover, organize, and share content and applications by allowing users to create channels to categorize individuals' information interests in order to facilitate users to better discover content that users are interested in, manage information, and form better interest-based social interaction (see Zhou, [0035])
Erman and Zhou does not explicitly teach the system wherein:
adjusting, by the application eco space operator, at least one of the recommended applications based on the user interaction data, resulting in a new channel being recommended in a second application of the plurality of applications, wherein the second application is distinct from the first application,
wherein the new channel was not previously recommended by the application eco space operator or previously viewed by the user; and
wherein the first channel is subscribed by the user, and the new channel has similar content as the first channel but is not subscribed by the user.
Erman, Zhou, and Paleja does not teach the system wherein:
adjusting, by the application eco space operator, at least one of the recommended applications based on the user interaction data (see Paleja: Fig.3, Col.12, Line 56-63, “Identifying related applications based on usage can therefore include looking up an application the user selected in the usage associations dataset to find related applications. The resulting set of related applications can then be ranked according to association score. At block 328, a subset of the related applications is selected to recommend to the user, which is output as a set of recommendations at block 330.”), resulting in a new channel being recommended in a second application of the plurality of applications see Paleja: Fig.5, Col.15, Line 3-9, “identifying related applications based on usage can therefore include looking up an application the user selected in the usage associations dataset to find related applications. The resulting set of related applications can then be ranked according to association score. At block 328, a subset of the related applications is selected to recommend to the user, which is output as a set of recommendations at block 330.”), wherein the second application is distinct from the first application (see Paleja: Fig.5, Col.15, Line 10-20, “At block 504, non-application items are identified that are associated with the applications. As described above, one possible mined dataset 252 (see FIG. 2) can relate applications and non-application items, such as any item available in the electronic catalog described above with respect to FIG. 1. Since application selection and non-application item selection behavior for users can both be tracked by the ICS 110, relating such behaviors together can be possible. Thus, associations can be made, for instance, between users who use foodie or gourmet cooking applications and users who purchase high-end cookware.”),
Because Erman, Zhou, and Paleja are in the same field of endeavor of an application program recommending method, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Erman to include the method that share recommended application based on updated user interaction information and a method that recommend un-subscribe content based on subscribed content based on collected user interaction information of the first application as taught by Paleja. After modification of Erman, the Application Guide (AppGuide) 600 that recommend application to the user can also incorporate the mechanism of recommending similar/unsubscribed content to different application AppGuide based on collected user interaction information of the first application and share recommended application as taught by Paleja. One would have been motivated to make such a combination in order to enhance the accuracy of predicting an application program to be recommended to the user to improve and enrich the overall experience by presenting to users the right mix of information at the right time.
Erman, Zhou, and Paleja doer not tech the system wherein:
the new channel was not previously recommended by the application eco space operator or previously viewed by the user, and
the first channel is subscribed by the user, and the new channel has similar content as the first channel but is not subscribed by the user.
However, Lewis teaches the system wherein:
the new channel was not previously recommended by the application eco space operator or previously viewed by the user (see Lewis: Fig.5, Col.17, 55-64, “The method 500 temporarily subscribes the user to the one or more recommended channels. In one embodiment, at block 520, the method 500 provides media items from the one or more recommended channels to the user. For example, the method 500 may present media items from the one or more recommended channels to the user on an activity feed of the user (as illustrated in FIG. 1). In another embodiment, at block 520, the method 500 may provide the user with access to the media items”), and
the first channel is subscribed by the user (see Lewis: Fig.5, Col.17, 51-55, “identifying the one or more recommended channels based on the subset of users (e.g., identifying channels that the subset of users accessed, subscribed to, and/or that belong to the subset of users).”, and the new channel has similar content as the first channel but is not subscribed by the user (see Lewis: Fig.5, Col.17, 56-63, “At block 515, the method 500 temporarily subscribes the user to the one or more recommended channels. In one embodiment, at block 520, the method 500 provides media items from the one or more recommended channels to the user. For example, the method 500 may present media items from the one or more recommended channels to the user on an activity feed of the user (as illustrated in FIG. 1).).”, i.e. the initial recommended channel is unsubscribed before interaction)
Because Erman, Zhou, Paleja and Lewis are in the same field of endeavor of an application program recommending method, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Erman to include the method that share recommended application based on updated user interaction information and a method that recommend un-subscribe content based on subscribed content based on collected user interaction information of the first application as taught by Lewis. After modification of Erman, the Application Guide (AppGuide) 600 that recommend application to the user can also incorporate the mechanism of recommending similar/unsubscribed content to different application AppGuide based on collected user interaction information of the first application and share recommended application as taught by Lewis. One would have been motivated to make such a combination in order to enhance the accuracy of predicting an application program to be recommended to the user to improve and enrich the overall experience by presenting to users the right mix of information at the right time.
Regarding Claim 2,
Erman, Zhou, Paleja and Lewis teaches all the limitations of Claim 1. Zhou further teaches the method wherein:
the new channel is selected based on the interests of the user and the user interaction data (see Erman: Fig.2C, [0083], “selection of recommended applications for a user is performed using user profile information and application profile information. In one embodiment, selection of recommended applications for a user includes steps of (1) receiving user profile information of the user (e.g., from a database storing the user profile information), (2) receiving application profile information of the available applications (e.g., from a database storing the application profile information), and (3) selecting available applications for the user, as recommended applications to be recommended to the user, using the application profile information and the user profile information,”)”
Regarding Claim 3,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 2. Erman further teaches the method wherein
selection of the personalized multiple applications personalized channel with respect to at least one of popularity, recency, rating (see Erman: Fig.6B, “selection of the sixth application (APP 6) in the list of installed applications 621 results in display of an action menu 622 including selectable menu items which, when selected, result in initiation of indicated actions for the sixth application (illustratively, a VIEW DETAILS menu item for enabling the user to view details associated with the sixth application, a START menu item for enabling the user to launch the sixth application, a RATE menu item for enabling the user to enter a rating for the sixth application, and a RECOMMEND menu item for enabling the user to recommend the sixth application to others).
Regarding Claim 4,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 2. Erman further teaches the method wherein:
selection of the new channel is further based on popularity, recency, and rating (see Erman: Fig. 6A-6D, [0120], the application guide server is able to provide recommended application updates to a user and the user is provided with a dynamic, customized Application Guide presenting recommendation application information which changed dynamically,”)
Regarding Claim 5,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 1. Erman further teaches the method wherein:
recommended applications is associated with a category; and content of the at least one recommended channel for each of the (see Erman: Fig.6A-6D, [0199], “embodiments the principles of the application recommendation capability may be utilized for recommending other types of content to users (e.g., music, television programs, movies, software programs, video games” i.e. Describing providing different recommendation mobile app categories contents recommendation capability may be utilized for recommending other types of content to users (e.g., music, television programs, movies, software programs, video games, and the like”)
Regarding Claim 6,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 1. Erman further teaches the method wherein
at least one of the recommended applications is created by a third-party developer (see Erman: Fig.2B, [0068], “The application information may be received from any suitable source of such information (e.g., from the application store, directly from the application developer(s), from one or more other sources of application information.”)
Regarding Claim 8,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 1. Erman further teaches the method wherein:
the application eco space corresponds to an integrated application represented by a link of an operating system so that the user, via the link, can access the recommended applications at the same time in the application eco space (see Erman: for e.g. Fig. 6A, [0159] disclosing “The recommended applications displayed in the recommended applications tab 610 are selectable from the recommended applications tab 610,” and also “transmission of a message including a hyperlink to the application in response to initiation of a request to recommend the recommended application to one or more friends of the user.”)
Regarding independent Claims 10 and 19,
Claims 10 is directed to a system claim and Claim 19 is directed to a non-transitory machine-readable medium claim and both claims have similar/same technical features and claim limitations as Claim 1 and are rejected under the same rationale.
Regarding Claims 11 and 20,
Claims 11 is directed to a System claim and Claim 20 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 2 and is rejected under the same rationale.
Regarding Claims 12 and 21,
Claims 12 is directed to a System claim and Claim 21 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 3 and is rejected under the same rationale.
Regarding Claims 13 and 22,
Claims 13 is directed to a System claim and Claim 22 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 4 and is rejected under the same rationale.
Regarding Claims 14 and 23,
Claims 14 is directed to a System claim and Claim 23 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 5 and is rejected under the same rationale.
Regarding Claims 15 and 24,
Claims 15 is directed to a System claim and Claim 24 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 6 and is rejected under the same rationale.
Regarding Claims 17 and 26,
Claims 17 is directed to a System claim and Claim 26 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 8 and is rejected under the same rationale.
Regarding Claims 17 and 26,
Claims 17 is directed to a System claim and Claim 26 is directed to a non-transitory machine-readable medium claim and the claim sets have similar technical features and claim limitations as Claim 8 and is rejected under the same rationale.
Regarding Claim 29,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 1. Zhou further teaches the method wherein:
the user interaction data includes data and content associated with the first channel (see Zhou: Fig.10, [0062], “an exemplary process of the content gathering module 410, according to an embodiment of the present teaching. Starting from block 1002, content is fetched from various content sources based on associated interests for each channel. At block 1004, advertisements are fetched based on associated interests for each channel. Moving to block 1006, users are facilitated to save content in each channel. The content may be created or consumed by the users. Duplicated content is removed at block 1008, and the remaining content is ranked based on relevance at block 1010 in order to ease the information overload problem. At block 1012, content is categorized in each channel according to a predefined categorizing policy to further organize the content.”)
Because Erman, Zhou, Paleja and Lewis are in the same field of endeavor of App or content Recommendation based on user, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Erman to include the method wherein information associated with the respective at least one personalized channel of the at least one personalized application includes data and content associated with the respective at least one personalized channel as taught by Zhou.. One would have been motivated to make such a combination in order to provide an improved solution for facilitating users to discover, organize, and share content and applications by allowing users to create channels to categorize individuals' information interests in order to facilitate users to better discover content that users are interested in, manage information, and form better interest-based social interaction (see Zhou, [0035])
Regarding Claim 30,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 29. Zhou further teaches the method wherein:
the user interaction data characterizes at least one of an interaction of the user with the data and content of the first channel, social connection of the user with another user via the communication platform, and online behavior of the user (see Zhou: Fig.3, [0043], “an exemplary interest network composed of a plurality of interest channels created by different users, according to an embodiment of the present teaching. Based on the shared interests, interest channels created by different users may be connected to form an interest network in this example. In other words, the channels are used as interests-based social interaction platforms that connect people with similar interests. In this example, all the channels with associated interests related to "Sports" (e.g., "Soccer," "Volleyball," "Swimming," "Tennis," "Basketball") may be connected together to form a "Sports" interest network.”)
Because Erman, Zhou, Paleja and Lewis are in the same field of endeavor of App or content Recommendation based on user, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Erman to include the method wherein information further characterizes at least one of an interaction of the user with the data and content of the respective at least one personalized channel, social connection of the user with another user via the communication platform, and online behavior of the user as taught by Zhou. One would have been motivated to make such a combination in order to provide an improved solution for facilitating users to discover, organize, and share content and applications by allowing users to create channels to categorize individuals' information interests in order to facilitate users to better discover content that users are interested in, manage information, and form better interest-based social interaction (see Zhou, [0035])
Regarding Claim 31,
Erman, Zhou, Paleja and Lewis teach all the limitations of Claim 30. Zhou further teaches the method wherein:
the information is used to update the first information (see Zhou: Fig.5, [0049], “Users may also be facilitated to create and/or save content and categorize them into channels. At block 506, content in each channel is refined based on user interaction, for example, by the content refining module 412. In other words, users may participate in refining content in the channels. In one example, users may add or detect interests in the channels to help the interest channel platform 402 to change their content discovery/gathering strategies. In another example, users may change attributes, such as channel name, tags, or description, which may also help the interest channel platform 402 to update its content discovery/gathering strategies.”)
Because Erman, Zhou, Paleja and Lewis are in the same field of endeavor of App or content Recommendation based on user, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Erman to include the method wherein the information is used to update the first information as taught by Zhou. One would have been motivated to make such a combination in order to provide an improved solution for facilitating users to discover, organize, and share content and applications by allowing users to create channels to categorize individuals' information interests in order to facilitate users to better discover content that users are interested in, manage information, and form better interest-based social interaction (see Zhou, [0035])
Response to Arguments
Applicant’s arguments with respect to claim amendments have been considered but are moot considering the new combination of references being used in the current rejection. The new combination of references was necessitated by Applicant’s claim amendments. Therefore, the claims are rejected under the new combination of references as indicated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20160323619 A1
Lewis, Justin
Title: Recommending a composite channel
Description: This disclosure relates to the field of content sharing platform and, in particular, to a cross-application content player of a content sharing platform.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145