Emotion-Adaptive Video Messaging Customization

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Solution Overview

Problem

Current multimedia messaging applications (MMAs) lack the ability to effectively customize text messages within modifiable videos, failing to personalize content based on user context and emotional state.

Innovation Solution

The MMA system allows users to customize text messages and styles within modifiable videos by analyzing recent messages and emotional states, using machine learning techniques to suggest relevant reels and modify text messages, animations, and soundtracks accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If pre-generated videos are stored in a database and provided on demand, then video generation speed is improved, but customization capability deteriorates

Engineering Contradiction:

Improvevideo generation speedVSAvoidcustomization capability

Core Design Contradiction:

SpeedVSAdaptability or versatility

Solution Approach 1:

The video is segmented into a template portion (pre-generated and stored in database) and a customization portion (user-specific text messages and media). The template provides the base structure and animation while the customization portion allows personalization based on user context and emotional state, resolving the contradiction between fast generation and customization capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adapts the video content by replacing static pre-generated videos with dynamically generated videos that incorporate user-specific text messages, emotions, and media. The text messages are animated according to the video template's timing and rhythm, creating a dynamic customization that maintains generation speed while improving adaptability.

Inventive Principle:

Principle #15Dynamics

2Manufacturing precision

If text messages are manually typed by users, then customization precision is improved, but operation complexity increases

Engineering Contradiction:

Improvetext message customization precisionVSAvoiduser operation simplicity

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically analyzing recent messages and emotional states to generate customized text messages without requiring manual user input. The machine learning model processes user data and produces appropriate text messages that match the context and emotional state, eliminating the need for users to manually type while maintaining high customization precision.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical action of typing text messages is replaced by an automated machine learning system that analyzes communication context and emotional states. The system substitutes the mechanical process of manual text entry with an intelligent generation process that produces customized messages based on patterns in user behavior and emotional data.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If machine learning analysis is performed on recent messages and emotional states, then personalization quality is improved, but processing time increases

Engineering Contradiction:

Improveemotional state detection precisionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-processing and storing recent messages and emotional state data in a structured format that can be quickly queried. The machine learning model is pre-trained on user communication patterns, allowing it to rapidly analyze new inputs without requiring extensive real-time processing, thus reducing the time loss while maintaining high measurement precision.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250119492A1Customizing modifiable videos of multimedia messaging application

Publication Date: 2025.04.10 SNAP INC

AI summary

Provided are systems and methods for customizing modifiable videos. An example method includes analyzing recent messages associated with a user in a multimedia messaging application to determine an emotional state of the user, determining, based on the emotional state, a customized property of a modifiable feature of a modifiable video, where the modifiable video includes a preset property of the modifiable feature, replacing the preset property of the modifiable feature with the customized property of the modifiable feature, providing a user interface enabling the user to view the modifiable video and modify the customized property of the modifiable feature to a modified property, and, upon determining that the user has modified the customized property, storing information concerning the emotional state, the customized property, and the modified property to a statistical log.