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A Method and System for Associating Emotions with Media for Improved Correlations and Recommendations

IP.com Disclosure Number: IPCOM000238505D
Publication Date: 2014-Aug-31
Document File: 3 page(s) / 129K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is disclosed for associating emotions with media for improved correlations and recommendations.

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This is the abbreviated version, containing approximately 51% of the total text.

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A Method and System for Associating Emotions with Media for Improved Correlations and Recommendations

Disclosed is a method and system for leveraging emotion tracking capability and associating it with media for improved recommendations.

At a high level, the system tracks emotions of a user when the user is viewing the media. The media can be, but need not be limited to, a book, a movie and a song that is viewed by the user. The tracked emotions are then correlated with specific points, such as, a page or a time in the media.

In a scenario, the emotion data is used to summarize data points and share with an author of a book that has a pending user confirmation. This feedback is then used by the author to assess if the locations of the emotion coincide with the author's intention at that content location. The emotion data is also used to summarize the data points to better suggest other reading materials that indicate the user's preference.

In another scenario, the method highlights document areas associated with particular emotions and provides feedback of emotion to the author about the author's intent. The method also enables a user to assess a book's momentum to decide whether to continue to read based on emotional outcome of other users for the book.

In another scenario, the method considers the emotion data as a multifaceted rating provided by a user and leverages data mining algorithms to recommend content to the user based on other users with similar experiences. The emotion data is also considered as a configurable user rating, where the user defines what the user is

looking for, for instance, horror, comedy etc. The emotion data is accordingly mined to find recommendations based on other users with similar experiences

In yet another scenario, the emotion data is used to highlight areas of particular emotion in the content. For example, the data is used for highlighting areas of confusion when a patent attorney is reviewing a legal document. The areas o...