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Dynamic Social Filtering based on Life Changes Disclosure Number: IPCOM000237482D
Publication Date: 2014-Jun-18
Document File: 2 page(s) / 65K

Publishing Venue

The Prior Art Database


A method for dynamic social filtering based on life changes is disclosed.

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

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Dynamic Social Filtering based on Life Changes

Disclosed is a method for dynamic social filtering based on life changes.

Social channels often try to suggest/select recommended topics, interests, advertisements, feeds, etc. based on historical activity and/or selected interest by the user. Currently, these systems do not have the capabilities to exclude interest/topics based on life changes. For example, a person could have liked social channels, pages, topics that were of interest based on a loved one (child, husband, parent, etc.). If the loved one were to pass away, it might be difficult to see that information continue to show up in their Social feed. Some services do provide support for "filter bubbles" that shield people from certain aspects of the real world. These services are mechanism for filtering that are dependent on the user's preferences, interests, etc.

The disclosed system and method dynamically changes social filtering and interest by factoring in significant "life changes" (i.e. deaths, births, medical conditions, work/education/monetary status, etc.) and projects whether the sentiment would be positive or negative. Information and messages where the projected sentiment of the receiver would be negative are filtered out with the following example system embodiment:

User initiates log-in to Social Channel

Monitoring Engine examines Information Repositories/Resources/Databases (Info Dbs) to determine if any life changes have occurred since the last log-in .

If No, no further action would occur.

If yes, the system initiates the Analytics Engine.

Analytics Engine examines current interests/likes/feeds/etc. against any new changes from Info Dbs .

Artificial Intelligence Engine determines the (%) likelihood there is any overlap that would need to be changed based on the life change(s) that occurred and the pre-defined Admin rules / user preferences. Interests/likes/feeds/etc. are filtered or remain as is, based on the assessment and projected sentiment positive/negative.

Learning Engine could be used to determine what changes the user manually makes and factor that into the future considerations .

Data Repositories feeding into the Engines could be aggregated in a cloud environm...