User Activity Prediction Using Usage Data Categorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack an effective method to predict user preferences and provide personalized services or targeted advertising based on user behavior trends across various computing devices, failing to utilize usage data efficiently for improved user experiences.
Innovation Solution
A system that categorizes website usage data using a preference recommendation engine, scoring algorithm, and categorization engine to predict user activities and preferences, allowing for personalized services and targeted advertising by analyzing frequency and time-based data, with user opt-in/opt-out options for privacy control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If usage data is collected and analyzed to predict user preferences, then personalized services and targeted advertising can be provided, but user privacy concerns increase
Solution Approach 1:
The patent introduces an intermediary mechanism (opt-in/opt-out system) between data collection and personalization delivery. This intermediary allows users to control their participation in data collection, thereby mediating between the need for usage data to enable personalized services and the concern for user privacy. The system acts as a buffer that respects user choices while still enabling personalization for those who consent.
2Measurement precision
If multiple computing devices are monitored to track user behavior trends, then more accurate user preference predictions can be made, but system complexity increases
Solution Approach 1:
The patent implements a universal data collection framework that handles multiple computing devices (set-top boxes, mobile devices, PCs) through a single integrated system. The usage data collection and analysis mechanism is designed to work across different device types and platforms, allowing the same system architecture to process data from various sources without requiring device-specific implementations, thereby reducing overall system complexity while maintaining comprehensive tracking capability.
3Speed
If real-time usage data processing is implemented, then timely personalized content delivery is achieved, but computational resources increase
Solution Approach 1:
The patent employs preliminary action by pre-processing and categorizing usage data as it is collected, organizing it into structured formats with defined categories and weights before analysis is needed. This preliminary organization of data reduces the computational burden during real-time preference prediction, allowing the system to deliver personalized content timely without requiring excessive computational resources during the actual delivery moment.
Data Source
AI summary
A system for determining an interest of a user based on usage data is provided. Usage data corresponding to a plurality of websites accessed by a user is received from one or more computing devices of the user. The received usage data is categorized into a plurality of categories based on character strings that reference the individual websites. What content items from individual websites the user has selected to view is recorded in order to identify one or more items of interest. An activity of the user at a given time period is predicted based on the categorizations, the identified one or more items of interest, and times and duration of times the user viewed the individual websites. The system performs an action at the given time period based on the predicted activity.