Dynamic Interest Profile System for Automated User Tracking
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Solution Overview
Problem
Existing personal information management systems struggle to dynamically track user interests due to differences in user preferences and behaviors, leading to inefficient personalized application features that may be bothersome or inaccurate.
Innovation Solution
A method and system that monitor and process electronic documents to determine important people and terms for a user, generating a dynamic interest profile that adapts based on document changes, using statistical techniques and incremental updates to refine importance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user entry of important topics is used to create personalized profiles, then the accuracy of user interest tracking is improved, but the time consumption and user burden increase significantly
Solution Approach 1:
The system automatically analyzes user's electronic documents (emails, calendars, word processing documents, instant messages) to extract topics, people, and entities of importance without requiring user input. The interest profile is built and updated autonomously by processing the user's existing document stream, eliminating the need for users to manually enter topics while maintaining high accuracy in tracking user interests
Solution Approach 2:
The system continuously monitors and processes the user's document stream in the background to pre-build and update the interest profile before any application needs it. This preliminary automated analysis ensures the profile is ready for use without requiring user intervention at the moment of need
2Extent of automation
If machine-learning techniques are used to track user interests, then automation is improved, but the complexity of the system increases and users may forego personalized behavior
Solution Approach 1:
The system extracts only the essential information needed for interest tracking from the user's document stream - specifically topics, people, and entities mentioned in emails, calendars, word processing documents, and instant messages. By focusing on extracting these key elements rather than implementing complex machine learning models, the system achieves effective automation with reduced complexity
Solution Approach 2:
The system uses a universal approach of analyzing multiple document types (emails, calendars, word processing documents, instant messages) through the same automated process to build the interest profile. This multi-functional document processing capability achieves comprehensive automation without requiring separate complex systems for each document type
3Adaptability or versatility
If personalized application features are added to accommodate user differences, then the relevance of features to individual users is improved, but the complexity of managing diverse user preferences increases
Solution Approach 1:
The system creates customized interest profiles for each individual user based on their unique document stream, capturing their specific topics, people, and entities of importance. Each user receives personalized application features tailored to their local preferences and interests, while the underlying system maintains a unified automated profile generation approach that manages complexity
Data Source
AI summary
Described are a dynamic interest profile (DIP) system and method for dynamically tracking interests of a user based on personal information. The DIP system obtains electronic documents of the user from a document stream and processes the documents to obtain certain information therefrom. Based on the information obtained from the documents, the DIP system identifies terms, people, documents, and collections that are of importance to the user. These items of importance become part of a dynamic interest profile of the user. The dynamic interest profiles persist in a database. The DIP system also provides an application program interface (API) for accessing DIPs in the database. Application programs can employ this API to customize program behavior to the particular interests of the user executing those programs.