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System for Presenting the Most Optimized Interest to a User Based on an Interest Stream and Enabling Users to Follow other Users' Interest Streams Disclosure Number: IPCOM000242097D
Publication Date: 2015-Jun-18
Document File: 4 page(s) / 50K

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The Prior Art Database


Disclosed is a system that uses various inputs to automatically select the optimal ordering of an interest stream, or a list of relevant interests based on a number of factors, for a user.

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System for Presenting the Most Optimized Interest to a User Based on an Interest Stream and Enabling Users to Follow other Users ' Interest Streams

Systems exist to capture a list of interests relevant to a user; however, these interests are merely stored in memory. A method is needed to then determine which interests to return to the user, and in what order.

This article refers to this ordering of interests as an interest stream , and looks further into a system that is capable of optimizing this interest stream in order to keep the user engaged. Furthermore, an intelligent system is needed to not only return the optimized interest stream, but also include resources to facilitate interest within the specific subject and intrigue the user as quickly as possible.

The novel contribution is a system that uses various inputs to select the optimal ordering of an interest stream. These inputs include, but are not limited to:

 Context of when the interest was documented

 User's interest database and relationship to current interest

 Current emotions of the user

 Level of emotion displayed when the interest was first documented

 Previous interactions with similar interests and similar environmental stimuli

In conjunction with these inputs, the system applies predictive analytics, natural language processing (NLP), and correlation analysis to help determine an overall score for the particular interest in the interest database. The system continues by providing resources to help the user further explore Interest2. The system leverages analytic capabilities, predictive modeling, and web crawlers to provide a list of resources to the user. For instance, a queue such as Interest2:= [Resource1, Resource2, Resource3] is generated because it is relevant for the user based on the user's current emotional state.

When users further explore the interest context, the system only utilizes the associated user profile information. In addition, overlap of interests between User-A and User-B, who have similar profile databases, prompts similar items in each person's associated interest stream. For example, User-A is interested in Brand-X speakers and Brand-Y speakers. User-B's stream includes Brand-A speakers and A/V receivers. Thus, the system includes A/V receivers in User-A's stream and includes Brand-Y speakers in User-B's stream, assuming that User-A and User-B are like-minded individuals.

This is in direct contrast to the recommended items function available to a user while shopping online, which offers alternate and additional products in the same product category. The novel system uses similar functionality, but different product categories, which enables users to leverage products that fill the same need, but exist in a different product classification. Therefore,interest experience is not the same as shopping experience.

The novel system focuses on solving the underlying interest of learning about specific


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