User Mood Inference via Baseline Profile Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing targeted content delivery systems often fail to align content with user receptiveness due to a general understanding of user interests, leading to misalignment and decreased satisfaction for both content providers and receivers, as they do not account for factors like user mood at a particular time.
Innovation Solution
A system and method for inferring a user's current mood by generating an individual baseline mood profile based on mood-associated characteristic data, using mood rules to combine and weight this data to produce an inferred mood with a confidence score, which can then be used to select appropriate content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a general baseline mood profile representing a standard mood for a hypothetical person is used, then the system complexity is reduced, but the measurement precision of user mood is insufficient
Solution Approach 1:
The patent segments the baseline mood profile into two distinct types: a general baseline mood profile representing a standard mood for a hypothetical person, and an individual baseline mood profile specific to each user. This segmentation allows the system to balance between using a simple general profile for low complexity scenarios and employing personalized individual profiles when higher precision is required, thereby resolving the contradiction between system complexity and measurement precision.
2Measurement precision
If individual baseline mood profiles are generated for each user based on mood-associated characteristic data, then the measurement precision of user mood is improved, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic baseline mood profile system where the individual baseline mood profile is not static but evolves over time as more mood-associated characteristic data becomes available. The system dynamically updates and refines the individual profile based on accumulating user data, allowing it to adapt and improve measurement precision while managing complexity through progressive learning rather than requiring complete initial personalization.
3Measurement precision
If more mood-associated characteristic data is collected and analyzed, then the measurement precision of user mood is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and analyzing mood-associated characteristic data in the background to generate and maintain the individual baseline mood profile before it is needed for mood inference. The system proactively accumulates and processes user data over time, so that when mood inference is required, the baseline profile is already prepared and updated, reducing the real-time processing time and avoiding delays in content delivery.
Data Source
AI summary
An individual's responsiveness to targeted content delivery can be affected by a number of factors, such as an interest in the content, other content the user is currently interacting with, the user's current location, or even the time of day. A way of improving targeted content delivery can be to infer a user's current mood and then deliver content that is selected, at least in part, based on the inferred mood. The present technology analyzes mood-associated characteristic data collected over a period of time to produce at least one baseline mood profile for a user. The user's current mood can then be inferred by applying one or more mood rules to compare current mood-associated data to at least one baseline mood profile for the user.