Attribute-Based User Data Visualization Clustering
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Solution Overview
Problem
Users face difficulties in identifying and piecing together information from multiple sources of usage history, as existing methods present data sequentially in text format, requiring manual effort to retrieve relevant information across different products and services.
Innovation Solution
Implementing attribute-based user data visualization methods that categorize and cluster events by categories and time periods, allowing for intelligent grouping and visual presentation of user data using computing devices and systems, enabling users to easily identify and interact with data segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If user data is presented in sequential text format, then data completeness is maintained, but information retrieval efficiency deteriorates
Solution Approach 1:
The patent segments user data into distinct event categories (e.g., communications, computations, content consumption) and presents them as separate visual groups rather than a single sequential text stream. This allows users to quickly locate relevant information by category without manually scanning through all events chronologically.
Solution Approach 2:
The patent transforms one-dimensional sequential text data into a two-dimensional visual layout where events are organized by category and time period. This dimensional transformation enables users to perceive data relationships spatially, dramatically reducing the time and effort required to retrieve specific information.
2Loss of information
If multiple sources of usage history are integrated, then information completeness improves, but data processing complexity increases
Solution Approach 1:
The patent creates a universal event categorization framework that can handle multiple data sources (different products and services) through a common classification system. Events from various sources are assigned to standardized categories, allowing integrated presentation without requiring separate processing logic for each source.
Solution Approach 2:
The system automatically performs data clustering and categorization without requiring user intervention. The computing device autonomously organizes events from multiple sources into visual groups based on category and time period, eliminating the need for manual data compilation while maintaining information completeness.
3Measurement precision
If detailed user event data is preserved, then data accuracy is maintained, but visual presentation complexity increases
Solution Approach 1:
The patent extracts essential attributes from detailed event data (category, time period, event type) and presents only these key characteristics in the visual display. The full detailed data is preserved in the background but selectively presented based on category grouping, maintaining accuracy while simplifying visual complexity.
Data Source
AI summary
Systems and methods for attribute-based user data visualizations are described, including determining that an event is associated with a category, the event is one of a plurality of events that are associated with a user using one or more products; clustering the event with a group of events that are associated with the category; identifying at least a portion of the group of events and at least a portion of another group of events of another category, based on one or more time periods; and providing the at least the portion of the group of events and the at least the portion of the another group of events of the another category for visual presentation.