Personalized Emotive Autography via Dynamic User Classifiers
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Solution Overview
Problem
Existing emotive autography techniques lack user-centricity, using generic classifiers that fail to accurately tailor content summaries to individual preferences, resulting in ineffective emotional reflection for content creators and consumers.
Innovation Solution
The development of user-specific classifiers that adapt over time, utilizing a dynamic framework for video and audio summarization, which segments and scores content based on individual preferences, allowing for personalized emotive autographs by clustering semantically similar signals and assigning preference scores, and incorporating user feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic classifiers are used for emotive autography, then device complexity is reduced, but measurement precision of user preferences deteriorates
Solution Approach 1:
The patent segments the classifier system into multiple specialized classifiers, each trained to detect specific emotional dimensions (e.g., joy, sadness, anger). This segmentation allows each classifier to focus on a particular aspect of user preference, improving measurement precision while distributing system complexity across modular components rather than requiring a single complex universal classifier
Solution Approach 2:
The patent implements dynamic classifiers that adapt and evolve based on user feedback and interaction patterns. The classifiers are continuously refined through machine learning algorithms that adjust weights and parameters in real-time, enabling the system to improve preference measurement accuracy over time without requiring complete system redesign
2Adaptability or versatility
If user-specific classifiers are implemented, then adaptability to individual preferences is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal classifier framework that serves multiple users through a shared architecture. The base classifier structure, data processing pipelines, and evaluation metrics are universal and reusable across all users, while only the trained parameters and weightings are user-specific. This multi-functionality approach enables high adaptability to individual preferences without proportionally increasing overall system complexity
Solution Approach 2:
The patent employs template-based classifier designs where a master classifier template is copied and customized for each user. Rather than building entirely new classifiers for each user, the system replicates a proven effective base structure and adapts it through user-specific training data, significantly reducing the complexity burden of serving multiple users with personalized classifiers
3Measurement precision
If dynamic classification frameworks are used, then measurement precision of emotional content is improved, but loss of computational resources increases
Solution Approach 1:
The patent applies partial classification by focusing computational resources on the most relevant emotional dimensions for each specific content type and user context. Rather than running all possible emotional classifiers on every piece of content, the system selectively activates only those classifiers necessary for accurate measurement in the given context, reducing unnecessary computational expenditure while maintaining high precision where needed
Solution Approach 2:
The patent implements periodic retraining and updating of classifiers at scheduled intervals rather than continuously. The dynamic classification framework updates its parameters and adapts to new data in periodic batches, allowing computational resources to be concentrated at specific update moments rather than constantly consumed, thus maintaining measurement precision while reducing overall energy loss
Data Source
AI summary
A method of emotive autography includes calculating a plurality of classifiers associated with an individual user. Each of the classifiers indicates a preference of the user for an associated type of multimedia content. Multimedia data is received including video data, audio data and/or image data. The multimedia data is divided into semantically similar segments. A respective preference score is assigned to each of the semantically similar segments by use of the classifiers. The semantically similar segments are arranged in a sequential order dependent upon the preference scores. An emotive autograph is presented based on the semantically similar segments arranged in the sequential order.