Devices, methods, and non-transitory program storage devices (NPSDs) are disclosed herein to provide for the privacy-respectful learning of iconic scenes and places, wherein the learning is based on information received from one or more client devices in response to one or more collection criteria specified as part of one or more collection operations launched by a server device. In some embodiments, differential privacy techniques (such as the submission of predetermined amounts of noise-injecting, e.g., randomly-generated, data in conjunction with actual data) are employed by the client devices, such that any insights learned by the server device only relate to “hot spots,” “themes,” or other scenes, objects, and/or topics that are highly popular and captured in the digital assets (DAs) of many users, ensuring there is no way for the server device to learn or glean any insights related to particular users of individual client devices participating in the collection operations.

This invention discloses a computer-implemented method, caused by a server, for hierarchical causality-based stitching of content and for serving said stitched content as output content, said method comprising: tracking, and measuring, a first set of markers, for a first content consumer, consuming a first content item; tracking, and measuring, a first set of markers, for a second content consumer, consuming a second content item; receiving, by said first content consumer, a request corresponding to a marker from said first set of markers; computing a “pertinence indicator”; computing a “colliding score”; automatically collating said first content item, correlative to said first user, and a second content item, correlative to said second user, to form at least an output content, if said “pertinence indicator” is within said pre-defined rules of correlation and if said “colliding score” is within said pre-determined threshold; and serving said collated output content.

This invention discloses a computer-implemented method, caused by a server, for hierarchical causality-based stitching of content and for serving said stitched content as output content, said method comprising: tracking, and measuring, a first set of markers, for a first content consumer, consuming a first content item; tracking, and measuring, a first set of markers, for a second content consumer, consuming a second content item; receiving, by said first content consumer, a request corresponding to a marker from said first set of markers; computing a “pertinence indicator”; computing a “colliding score”; automatically collating said first content item, correlative to said first user, and a second content item, correlative to said second user, to form at least an output content, if said “pertinence indicator” is within said pre-defined rules of correlation and if said “colliding score” is within said pre-determined threshold; and serving said collated output content.

A method of embodying an online media service having a multiple voice system includes a first operation of collecting preset online articles and content from a specific media site and displaying the online articles and content on a screen of a personal terminal, a second operation of inputting a voice of a subscriber or setting a voice of a specific person among voices that are pre-stored in a database, a third operation of recognizing and classifying the online articles and content, a fourth operation of converting the classified online articles and content into speech, and a fifth operation of outputting the online articles and content using the voice of the subscriber or the specific person, which is set in the second operation.

A method of embodying an online media service having a multiple voice system includes a first operation of collecting preset online articles and content from a specific media site and displaying the online articles and content on a screen of a personal terminal, a second operation of inputting a voice of a subscriber or setting a voice of a specific person among voices that are pre-stored in a database, a third operation of recognizing and classifying the online articles and content, a fourth operation of converting the classified online articles and content into speech, and a fifth operation of outputting the online articles and content using the voice of the subscriber or the specific person, which is set in the second operation.

Examples of the present disclosure describe systems and methods for content-based multimedia retrieval with attention-enabled local focus. In aspects, a search query comprising multimedia content may be received by a search system. A first semantic embedding representation of the multimedia content may be generated. The first semantic embedding representation may be compared to a stored set of candidate semantic embedding representations of other multimedia content. Based on the comparison, one or more candidate representations that are visually similar to the first semantic embedding representation may be selected from the stored set of candidate semantic embedding representations. The candidate representations may be ranked, and top ‘N’ candidate representations (or corresponding multimedia items) may be retrieved and provided as search results for the search query.

Examples of the present disclosure describe systems and methods for content-based multimedia retrieval with attention-enabled local focus. In aspects, a search query comprising multimedia content may be received by a search system. A first semantic embedding representation of the multimedia content may be generated. The first semantic embedding representation may be compared to a stored set of candidate semantic embedding representations of other multimedia content. Based on the comparison, one or more candidate representations that are visually similar to the first semantic embedding representation may be selected from the stored set of candidate semantic embedding representations. The candidate representations may be ranked, and top ‘N’ candidate representations (or corresponding multimedia items) may be retrieved and provided as search results for the search query.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

A media application segments a library of media associated with a user account into episodes, wherein each episode is associated with a corresponding time period. The media application generates, using an event machine-learning model, an event signal that indicates a likelihood that an event occurred in each episode, wherein the event machine-learning model is a classifier that receives the media as input. The media application generates an event significance score for each episode. The media application determines one or more events from the episodes based on the event signal and a corresponding event significance score exceeding a threshold event significance value. The media application provides a user interface that includes corresponding media from the one or more events.