Mood Series Prediction Using Behavior and Mood Data
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Solution Overview
Problem
Existing methods for predicting user mood fail to account for time-series fluctuations and the influence of future actions, leading to inadequate representation of mood fluctuations and difficulty in predicting future mood accurately.
Innovation Solution
A prediction apparatus that includes a feature extraction unit for past behavior series data, a behavior series prediction unit to forecast future behavior, and a mood series prediction unit that uses both behavior feature data and past mood series data to predict future mood series, employing trained models for accurate mood series prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mood data is quantified and converted into average values for prediction, then prediction simplicity is improved, but time-series fluctuations in mood are lost
Solution Approach 1:
The patent segments mood prediction into two distinct components: a base mood value (average) and a fluctuation component (time-series variations). By separating these elements, the system preserves detailed temporal patterns while maintaining computational simplicity. The base mood captures overall trends, while the fluctuation component recovers the lost temporal dynamics through separate modeling of intra-day and inter-day variations.
2Ease of manufacture
If only past behavior data is used for prediction, then model training simplicity is improved, but influence of future actions on mood cannot be considered
Solution Approach 1:
The patent applies preliminary action by predicting future behavior series before using them to adjust mood predictions. The system first forecasts upcoming behaviors (such as scheduled events or planned activities), then incorporates these predictions into the mood modeling process. This allows the system to account for the impact of future actions on mood without requiring complex retroactive adjustments, maintaining model training simplicity while capturing forward-looking influences.
Data Source
AI summary
Provided is a prediction apparatus including a feature extraction unit configured to extract a feature based on past behavior series data of a prediction target and output behavior feature data, a behavior series prediction unit configured to predict a future behavior series of the prediction target based on the behavior feature data using a trained behavior series prediction model for predicting a behavior series, and a mood series prediction unit configured to predict a future mood series of the prediction target based on the behavior feature data and past mood series data of the prediction target using a trained mood series prediction model for predicting a mood series.