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Smarter analysis for network recommendations and Community generation based on user's presence in a particular time slice at a given location

IP.com Disclosure Number: IPCOM000238862D
Publication Date: 2014-Sep-23
Document File: 2 page(s) / 31K

Publishing Venue

The IP.com Prior Art Database


Humans from the dawn of civilization have been curious and anxious to know about other fellow humans around them and find ways to engage with them either for personal or professional reasons. As the enterprises grow larger they tend to become more disconnected, a problem that arises along with its size and diversity, is that people do not know each other that well. In not that rare scenarios people working for large organizations are not even aware who is the person sitting next to them. Apart from other problems, the issue that concerns these organisations most is that this disconnect among employees can have a detrimental effect on effectiveness of collaboration and innovation. This paper tries to provide a mechanism to anyone interested in knowing about exact people or groups who are in their physical proximity and interest group. The mechanism uses an algorithm which can effectively filter out noise ( unwanted people or groups ) from the list of people a user might want to look at to figure who were the people who were co-located at a particular time of interest.

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Smarter analysis for network recommendations and Community generation based on user's presence in a particular time slice at a given location

A mechanism capable of smartly analyzing all the people with whom you might want to get related to in social ecosystem of an organisation ( or otherwise ) by understanding and generating patterns based on a user's presence in a particular time slice at a given location. This mechanism could be utilized in mechanisms like recommending people with whom a user should be connected to, or in starting of dynamic communities based around social contexts or places on interests.

This is how the mechanism is going to work:
1. User logs in to a system or there is auto login mechanism through the location tracking feature
2. System divides user's day in time slices either by pre-configured values or by analyzing behavior - A typical example of analyzing user behavior is to analyze user's calendar, types of meetings, or by tracking location.

3. System then slices and dices data of other users to look for a pattern using which users with similar behaviors can be identified
4. System then either sends messages to these users to join an already existing community or to form a new community.

5. Send a specific messages suggesting users to make connections.

1. Facilitates in making of a strongly connected organisation, where people are more aware of others working and participating in similar projects, technologies or activities.

1. Allows user to expand his/her network by either enabling them to send network invites selecting from the list of people, or recommending them with people who share similar interests or places.

3. Enables creation of t communities including all or some from the list of people around a common subject of interest. Conventional way includes ask...