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 .
1. This communication is responsive to the Amendment filed 3/4/2026.
2. Claims 1-20 are pending in this application. Claims 1 and 11 are independent claims. In the instant Amendment, claims 1, 2, 4, 11, 12 and 14 were amended. This is a Non-Final action on the RCE filed 4/17/2026.
Claim Rejections - 35 USC § 103
3. 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 (i.e., changing from AIA to pre-AIA ) 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 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.
4. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maharajh et al (“Maharajh” US 2019/0354552) in view of Alahmady (US 2021/0304285) and further in view of Wu et al (“Wu” US 2010/0138370).
Regarding claim 1, Maharajh discloses a computing system, comprising:
at least one processor; and at least one memory device having computer-executable instructions stored thereon that, in response to execution by the at least one processor (see paragraph [0553]; e.g., processor and memory), cause the computing system to:
receive media assets from one or more source devices via a source gateway (see paragraphs [0100] and [0107]);
retain the media assets within a distribution platform for presentation at user devices via a media presentation service, the user devices being remotely located relative to the computing system (see paragraphs [0100], [0107], [0207] and [0313]);
cause a client application in a first user device of the user devices to direct the first user device to present a user interface having multiple interface elements and a media player element to convey digital content comprising a particular media asset of the media assets, the client application being configured to access the media presentation service (see paragraphs [0100], [0107] and [0192]);
receive user activity data from the first user device during, the activity data identifying interaction with multiple second media assets of the media assets presented at the first user device during a defined period of time (see paragraphs [0192] and [0207]); and
access, via one or more application programming interfaces (APIs), a third-party computing subsystem remotely located relative to the computing system, the third-party computing subsystem comprising third-party applications to manage subscriber accounts of the media presentation service (see paragraph [0271], [0281] and [0541]).
Maharajh does not expressly disclose generate a personalized set of access functionalities by applying a machine-learning model to the user activity data, wherein the machine-learning model is one of a plurality of machine-learning models associated with the distribution platform; based on the personalized set of access functionalities, generate, using the user activity data, a user profile, the user profile identifying interest levels of a first subscriber account on multiple types of digital content contained within the multiple media assets, wherein the personalized set of access functionalities are stored in the user profile.
However, Alahmady discloses generate a personalized set of access functionalities by applying a machine-learning model to the user activity data, wherein the machine-learning model is one of a plurality of machine-learning models associated with the distribution platform; based on the personalized set of access functionalities,…the user profile identifying interest levels of a first subscriber account on multiple types of digital content contained within the multiple media assets, wherein the personalized set of access functionalities are stored in the user profile (see paragraphs [0009]-[0015]; [0119]-[0120] and claims 1 and 2; e.g., “utilizing machine learning models to generate content package recommendations for current and prospective customers.”). It would have been obvious to an artisan before the effective filing date of the present invention to include Alahmady’s teachings in Maharajh’s user interface in an effort to provide a more user-friendly interface that gives users control over the content that they receive from content providers thereby increasing customer loyalty and advertisement revenue.
Moreover, Wu discloses generate, using the user activity data, a user profile (see paragraphs [0021] and [0034]). It would have been obvious to an artisan before the effective filing date of the present invention to include Wu’s teachings in the modified Maharajh’s user interface in an effort to provide a more user-friendly interface that saves user time by reducing user interaction steps for common tasks.
Regarding claim 2, Maharajh discloses wherein a first access functionality of the personalized set of access functionalities provides a first type of interaction with the particular media asset; and wherein a second access functionality of the personalized set of access functionalities provides a second type of interaction with the particular media asset (see paragraph [0467]).
Regarding claim 3, Maharajh discloses wherein the personalized set of access functionalities comprises at least one of real-time translation, real-time transcription in a defined language; access to a document mentioned in the particular media asset; detection of haptic capable device and provisioning of four-dimensional (4D) experience during presentation of the particular media asset; a share function to forward information related to the particular media asset to a defined set of recipient devices; access to recommended content; or messaging functionality to send a message having a link to cited, recommended, or curated content related to the particular media asset; a scheduler functionality that prompts to add invites, adds invites, or sends invites for, a live presentation related to the particular media asset (see paragraph [0123]).
Regarding claim 4, Maharajh discloses the at least one memory device having further computer-executable instructions stored thereon that, in response to execution by the at least one processor, further cause the computing system to, generate predictions of engagement levels for prospective subscriber accounts of the media presentation service by applying a second machine-learning model of the plurality of machine-learning models to registrations to an event; and generate predictions of load conditions of the computing system by applying a third machine-learning model of the plurality of machine-learning models to feature vectors comprising at least one of a first feature defining a number of scheduled events, a second feature defining a number of registrants for each timeslot of a presentation, a first categorical variable for the hour of the day for the presentation; or a second categorical variable for day of the week for the presentation (see paragraphs [0192] and [0207]).
Regarding claim 5, Maharajh discloses wherein accessing, via the one or more APIs, the third-party computing subsystem comprises exchanging data between a second gateway of the computing system and at least one of the third-party applications (see paragraphs [0210] and [0313]).
Regarding claim 6, Maharajh discloses wherein the third-party applications comprise one or more of a sales application, a marketing automation application, a customer relationship management (CRM) application, a business intelligence (BI) application, or a marketing automation application (see paragraphs [0210] and [0313]).
Regarding claim 7, Maharajh discloses the at least one memory device having further computer-executable instructions stored thereon that, in response to execution by the at least one processor, further cause the computing system to provide a user interface to access one or more functionalities to supply a second particular media asset of the media assets (see paragraph [0100]).
Regarding claim 8, Maharajh discloses wherein the one or more functionalities comprise a search functionality, a branding functionality, a layout selection functionality, a curation functionality, and a publication functionality (see paragraph [0326]).
Regarding claim 9, Maharajh discloses the at least one memory device having further computer-executable instructions stored thereon that, in response to execution by the at least one processor, further cause the computing system to supply a third particular media asset comprising defined digital content including at least one of directed content or indicia defining a call-to-action (see paragraph [0440]).
Regarding claim 10, Maharajh discloses wherein supplying the third particular media asset to the first user device comprises causing the client application to direct the first user device to present the defined digital content as one or more overlays on the third particular media asset (see paragraph [0440]).
Claims 11-14 are similar in scope to claims 1-4, respectively, and are therefore rejected under similar rationale.
Claim 15 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Claims 16-19 are similar in scope to claims 5-8, respectively, and are therefore rejected under similar rationale.
Claim 20 is similar in scope to claims 9 and 10, and is therefore rejected under similar rationale.
Response to Arguments
5. Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arora et al (US 2017/028534).
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHAWN N TILLERY whose telephone number is (571)272-6480. The examiner can normally be reached M-F 9:00a - 5:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William L Bashore can be reached at (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHAWN N TILLERY/Primary Examiner, Art Unit 2174
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