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System and Method for Application Suggestions Based on Social Networks and Thresholds

IP.com Disclosure Number: IPCOM000225667D
Publication Date: 2013-Feb-25
Document File: 4 page(s) / 33K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a system and method for automatically exposing users to relevant applications and services that are used by individuals in the user's social network. The exposure is automated, based on an analysis of the user's use of applications, along with the use of users in the social network. Because a significant number of users in a social network have shared interests or jobs, in this manner, a user is likely to be exposed to useful applications and services.

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System and Method for Application Suggestions Based on Social Networks and

Thresholds

Users of computers, smart-phones, tablets, video games, and other information-processing devices may often benefit from being made aware of useful applications and services.

The current invention is an automated system for exposing users to potentially useful applications and services. For example, if a user is a graphic designer, it is likely that there are other graphic designers or like-minded people in that user's social network. Social networks include those formed through web-based tools as well as gaming sites, virtual universe sites, email and instant message contact lists, a record of emailing and messaging, etc.

Disclosed is a system and method for automatically exposing users to relevant applications and services that are used by individuals in a social network. The suggestion is automated, based on an analysis of the user's own use of applications, along with the application use of people in the user's social network.

The novel contribution is a system and method comprising the following analysis components:


• AC1: analyzes user's use of applications and services


• AC2: analyzes applications and services used by individuals in a user's social network


• AC3: analyzes results from AC1 and AC2

In addition, a novel fourth component is based on trigger from AC3; a communication automatically conveyed to user regarding possibly useful applications and services discovered by AC2. The AC3 analysis may include the number of users n1 in a social network who have used an application or service with frequency > f. The depth of the social network searched may be controlled (e.g. friends of friends of friends).

Simplified Sequence of Steps:

1. AC1 analyzes the user's use of applications and services (e.g., scans computing device for applications recently used)

2. AC2 analyzes the user's social network for application use by individuals in the network (e.g., a network comprising email contacts and the friends of email friends)

3. If math-function-of (AC1 results, AC2 results, n) > threshold, then notify user of applications discovered by AC2. (n may be the number of users in the social network, and/or it can reflect frequency of use, distance in a social network, and other features)

Additionally, the AC2 invokes the novel aspect of considering attributions that may be mined and used with respect to Degree Centrality, Betweenness Centrality, Closeness, Eigenvalue Hub, and Authority. As an example, an "Authority" generally has a high

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number of relationships "pointing to it" and acts as a knowledge source of information. A Hub is an individual that points to a relatively large number of Authorities. These characterizations of the social network can be made known using known network-analysis tools. In enterprise business scenarios, employee subject matter experts (SMEs) may also be explicitly identified by individuals (e.g., expert...