Cognitive Reminder Notification Based on User Profile and Activity
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Solution Overview
Problem
Users face challenges in utilizing information from QA systems as answers are often not immediately usable due to temporal characteristics, requiring users to remember or re-ask questions, and changes in answers can lead to stale information.
Innovation Solution
A method is implemented in a data processing system to analyze user profile and activity information to determine the best time for scheduling reminder notifications based on temporal characteristics of questions and answers, using natural language processing to identify temporal aspects and calculate optimal notification times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users rely on QA system answers directly, then information retrieval efficiency is improved, but answer usability deteriorates due to temporal characteristics requiring users to remember or re-ask questions
Solution Approach 1:
The system performs preliminary actions by automatically analyzing temporal characteristics of answers and proactively scheduling reminder notifications before the answer becomes stale. This eliminates the need for users to manually remember or re-ask questions, as the reminder is prepared and sent in advance at the optimal time calculated by the system.
2Ease of operation
If users store QA answers for later use, then answer usability is improved, but information reliability deteriorates due to potential answer changes leading to stale information
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring whether an answer has changed since it was generated. When a change is detected, the system adjusts the reminder notification timing or content accordingly, ensuring users receive updated information. This feedback loop maintains information reliability while preserving answer usability.
3Reliability
If reminder notifications are sent immediately, then information reliability is improved by providing current answers, but loss of time increases as users cannot prepare adequately
Solution Approach 1:
The system dynamically adjusts the reminder notification timing based on multiple factors including the temporal characteristics of the answer, user preferences, and predicted answer stability. Rather than using a fixed immediate notification approach, the system calculates an optimal notification time that balances information currentness with user preparation needs, making the reminder timing adaptive and flexible.
4Ease of operation
If the system analyzes user profile and activity information to calculate optimal reminder timing, then ease of operation is improved by personalizing notifications, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting and analyzing user profile information and activity history without requiring explicit user input. The system autonomously calculates optimal reminder timing based on observed user patterns and preferences, reducing the need for complex user configuration interfaces while still providing personalized notifications.
Data Source
AI summary
A data processing system determines whether a natural language query or a result has a temporal characteristic. In response, at least one of user profile information or user activity history information for a user is analyzed to identify user characteristics indicative of a timeframe for scheduling a reminder notification of the result. A timeframe for scheduling the reminder notification is calculated based on results of analyzing the user profile information or user activity history information. A reminder notification is scheduled to be output at a scheduled reminder notification time based on the calculated timeframe. At a later time, in response to the later time being equal to or later than the scheduled reminder notification time, the reminder notification is output to a client device associated with the user, wherein the reminder notification specifies the result generated for the natural language query.