Abstract: The method includes identifying, by one or more computer processors, a first content consumed by a user. The method further includes identifying, by one or more computer processors, objects in the first content. The method further includes identifying, by one or more computer processors, a second content on the Internet that is similar to the first content, wherein the second content includes one or more objects that reach a threshold similarity to the identified objects in the first content. The method further includes identifying, by one or more computer processors, a date of creation for the identified first content and the date of creation of the second content. The method further includes arranging, by one or more computer processors, the identified first content and the identified second content in chronological order.

Abstract: The method includes identifying, by one or more computer processors, a first content consumed by a user. The method further includes identifying, by one or more computer processors, objects in the first content. The method further includes identifying, by one or more computer processors, a second content on the Internet that is similar to the first content, wherein the second content includes one or more objects that reach a threshold similarity to the identified objects in the first content. The method further includes identifying, by one or more computer processors, a date of creation for the identified first content and the date of creation of the second content. The method further includes arranging, by one or more computer processors, the identified first content and the identified second content in chronological order.

Abstract: The method includes identifying, by one or more computer processors, a first content consumed by a user. The method further includes identifying, by one or more computer processors, objects in the first content. The method further includes identifying, by one or more computer processors, a second content on the Internet that is similar to the first content, wherein the second content includes one or more objects that reach a threshold similarity to the identified objects in the first content. The method further includes identifying, by one or more computer processors, a date of creation for the identified first content and the date of creation of the second content. The method further includes arranging, by one or more computer processors, the identified first content and the identified second content in chronological order.

Abstract: A computing device identifies a plurality of media files within a data store, each of the media files lacking user established file names. The computing device analyzing the plurality of media files to recognize humans in the media files based on facial recognition and/or voice recognition programs. Using results of the analyzing to generate a plurality of content identification keywords, which are scored and ranked. Establishing a filename prefix for the media files using scored and ranked content identification keywords. Automatically generating a unique file name for each of the media files, wherein each unique file name includes the established filename prefix.

Abstract: The method includes identifying, by one or more computer processors, a first content consumed by a user. The method further includes identifying, by one or more computer processors, objects in the first content. The method further includes identifying, by one or more computer processors, a second content on the Internet that is similar to the first content, wherein the second content includes one or more objects that reach a threshold similarity to the identified objects in the first content. The method further includes identifying, by one or more computer processors, a date of creation for the identified first content and the date of creation of the second content. The method further includes arranging, by one or more computer processors, the identified first content and the identified second content in chronological order.

Abstract: A computing device identifies a plurality of media files within a data store, each of the media files lacking user established file names. The computing device analyzing the plurality of media files to recognize humans in the media files based on facial recognition and/or voice recognition programs. Using results of the analyzing to generate a plurality of content identification keywords, which are scored and ranked. Establishing a filename prefix for the media files using scored and ranked content identification keywords. Automatically generating a unique file name for each of the media files, wherein each unique file name includes the established filename prefix.

Abstract: A set of media files are identified within a data store. Each of the media files lack user established file names, lack user established icons, or lack user established file names and icons. The media files are analyzed to determine semantic metadata. For at least a subset of the media files, the semantic metadata is utilized to automatically generate unique and meaningful file names, file icons, or both file names and file icons for each of the media files in the subset.

Abstract: A set of media files are identified within a data store. Each of the media files lack user established file names, lack user established icons, or lack user established file names and icons. The media files are analyzed to determine semantic metadata. For at least a subset of the media files, the semantic metadata is utilized to automatically generate unique and meaningful file names, file icons, or both file names and file icons for each of the media files in the subset.