Interest Analysis via Scroll Speed and Split Screen Positioning
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Solution Overview
Problem
Conventional methods for analyzing user interest are limited in detecting potential interest or demand without explicit user intention, as they rely on expressed actions such as product storage, purchases, or social media interactions.
Innovation Solution
An interest information analysis method using a scroll pattern that extracts a target screen based on scrolling speed and user behavior, calculates interest scores for content information across multiple split screens, and analyzes user interest without requiring explicit user intention, utilizing sensors like acceleration, gyro, and proximity sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods analyze user interest based on explicit user actions (product storage, purchases, social media interactions), then the analysis is simple and direct, but it cannot detect potential interest or demand without explicit user intention
Solution Approach 1:
The patent segments user interaction data into multiple dimensions: scroll behavior patterns, time spent on content, click sequences, and navigation paths. By dividing the analysis into these discrete components, the system can detect potential interest signals that individual actions alone would miss, resolving the contradiction between detection accuracy and analysis complexity
Solution Approach 2:
The patent introduces behavioral pattern analysis as an intermediary layer between raw user actions and interest detection. This mediator processes scroll speeds, pause durations, and navigation patterns to infer potential interest, enabling the system to detect implicit user intentions without requiring explicit actions
2Loss of information
If the system monitors detailed user behavior patterns (scrolling speed, screen position, touch area), then potential interest can be detected, but the data processing complexity increases
Solution Approach 1:
The patent performs preliminary processing of user behavior data by pre-defining interest score weights for different behavioral patterns. Scroll depth, time duration, and interaction frequency are pre-assigned significance values, allowing the system to process detailed behavior data efficiently without real-time complex calculations
Solution Approach 2:
The patent transforms detailed behavioral data into standardized parameters with assigned weights. By converting continuous scroll positions into discrete interest scores and transforming touch patterns into categorical interaction types, the system maintains information completeness while reducing processing complexity
Data Source
AI summary
Disclosed herein are an interest information analysis method and an apparatus using the same. The method and apparatus are configured to extract a target screen from a user terminal in consideration of scrolling speed, calculate a degree of interest in content information included in the target screen in consideration of multiple split screens corresponding to the target screen and a user touch area, and analyze information about interest of the user by arranging multiple pieces of content information acquired from the user terminal based on the degree of interest. Accordingly, information about interest of the user or a field of interest to the user may be analyzed without referring to a definite intention expressed by the user.