Mood Estimation via Cross-Correlated Data Filtering
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Solution Overview
Problem
Existing mood detection techniques for users of communication devices do not adequately consider external factors that may influence a user's emotional state, making it challenging for service providers to react appropriately to the user's mood, especially as interactions become more complex and context-dependent.
Innovation Solution
A method that receives user data from communication devices and external sources, processes this data to filter out irrelevant information, and cross-correlates it with user profiles to estimate mood, allowing for adjustments in network and service policies, as well as generating interaction suggestions based on the user's mood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mood detection techniques consider only user-originated data (tone of voice, keywords, repetitive dialing), then the detection method remains simple, but the mood estimation accuracy is insufficient because external factors affecting user mood are not considered
Solution Approach 1:
The patent segments the mood detection system into multiple independent data sources: user-originated data (tone, keywords, dialing patterns), external sources (social media, news, weather), and environmental data. Each segment is processed separately through filtering and cross-correlation with user profiles before being integrated for final mood estimation, reducing overall system complexity while improving accuracy
Solution Approach 2:
The patent introduces user profiles as an intermediary element that cross-correlates with all data sources. The profiles serve as a mediator that filters and contextualizes data from multiple sources, enabling accurate mood estimation without requiring direct complex integration of all raw data streams
2Adaptability or versatility
If service providers implement comprehensive mood detection considering multiple data sources, then user experience improvement is enabled, but the system complexity and implementation difficulty increase significantly
Solution Approach 1:
The patent creates a universal mood detection framework that can process multiple types of data sources (user data, external sources, environmental data) through a common architecture of filtering and cross-correlation with user profiles. This multi-functional system enables service providers to adapt to various user moods across different contexts without implementing separate specialized systems for each data source
Solution Approach 2:
The patent performs preliminary actions by pre-processing and filtering data from multiple sources before mood estimation, and by maintaining pre-established user profiles that contain cross-correlated data. This preliminary preparation reduces the complexity of real-time mood detection and enables faster service adaptation when mood changes occur
Data Source
AI summary
A mood of a user is estimated based on a user's profile, data indicative of a user's mood received from a communication device associated with the user and from sources other than the user, and environmental data with a potential impact on the user's mood. Data indicative of the user's mood and the environmental data are processed to filter out data that is not relevant to the user's mood. The filtered data is cross-correlated with the user profile, and the mood of the user is estimated based on the cross-correlated filtered data. A network and services may be controlled based on a user's mood. Suggestions for interacting with the user may be generated based on the user's mood.