Personalized Software Message Prompting via User Behavior Analysis
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Solution Overview
Problem
Existing software message prompt technologies fail to provide pertinent and personalized prompts, leading to user distraction and ineffective problem resolution due to uniform prompts being sent to all users, regardless of their specific hardware environments or user behavior.
Innovation Solution
A method and device that acquire user operation features and their success status to determine relevant feature groups and weights, allowing for personalized message prompting based on user behavior, with thresholds to decide when to prompt features or error messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the same fixed prompts are sent to every user, then the software can provide uniform information to all users, but the prompts become a disturbance to some users and fail to satisfy the needs of other users
Solution Approach 1:
The patent applies local quality by customizing prompt content according to each user's specific characteristics, behavior patterns, and needs. Instead of uniform prompts for all users, the system delivers personalized prompts that are locally optimized for each user's context, thereby reducing distraction while maintaining information delivery effectiveness.
Solution Approach 2:
The patent implements dynamics by making prompt delivery adaptive and changeable based on user behavior and feedback. The system dynamically adjusts which prompts are sent, when they are sent, and what content is included, transforming the static uniform prompt approach into a dynamic personalized prompt system that responds to user characteristics.
2Adaptability or versatility
If multiple prompts are provided to cover all possible user needs, then the software can address more user requirements, but the excessive prompts become a disturbance and interrupt user thoughts
Solution Approach 1:
The patent applies partial action by selectively providing only the necessary prompts to each user based on their specific needs and behavior patterns. Instead of sending all possible prompts to every user (excessive action), the system filters and delivers only the relevant subset of prompts, thereby maintaining comprehensive coverage of user needs while avoiding the harmful effect of excessive prompting.
Solution Approach 2:
The patent implements feedback mechanisms to monitor user responses and adjust prompt delivery accordingly. By analyzing user behavior and feedback, the system learns which prompts are useful to which users and adjusts future prompt delivery to optimize the balance between coverage and distraction, sending prompts only when they are likely to be beneficial.
3Adaptability or versatility
If personalized prompts are provided based on user behavior features, then the prompts become more pertinent and effective, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the user base into distinct segments or groups based on shared characteristics and behavior patterns. This segmentation approach enables personalized prompt delivery without requiring completely unique prompts for every individual user, thereby reducing system complexity while maintaining personalization benefits. Users within the same segment receive similar personalized prompts.
Solution Approach 2:
The patent implements universality by creating a multi-functional prompt system that can adapt to different user types, behaviors, and contexts using a unified framework. The system uses universal algorithms and models that can handle various personalization scenarios, reducing the need for separate complex systems for different user categories while still achieving personalized prompt delivery.
Data Source
AI summary
A message prompting method applied in the field of computer technology is disclosed, which comprises acquiring a feature of an operation of a user on software as well as whether or not the operation is successful, determining a feature group of the feature and its corresponding weight, the feature group containing features of a same category and having a weight which represents a relevance among the features within the group, and determining the message to be prompted to the user according to the weight and a pre-determined threshold in combination with whether or not the operation is successful. A message prompting device is also disclosed. The disclosure prompts software features with better pertinence to a user according to behavior features of the user, provide a personalized way of feature notification, avoid disturbing the user, improve the user experience, enhance friendliness, and can provide effective guidance to the user.