Digital Human Generation via Multi-Source Data Integration
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Solution Overview
Problem
The dispersion of user data from various sources makes it difficult to sufficiently mine and integrate the multiple dimensions of user information, such as interests, habits, and health data, which are essential for forming a comprehensive digital human representation.
Innovation Solution
A digital human generation method and system that acquires and processes data from multiple sources using a digital human model, which includes user profile models in dimensions like image, health, behavioral habits, social patterns, and consumption habits, to generate a digital human representation by cleaning, annotating, and applying algorithms like classification or deep learning to create user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user data is collected from multiple data sources, then the quantity and diversity of user information increases, but the data becomes dispersed and difficult to integrate
Solution Approach 1:
The patent merges data from multiple sources (social networks, mobile terminals, wearable devices, online shopping platforms) into a unified digital human model. The system integrates heterogeneous data types including image data, text data, and sensor data into a single comprehensive user profile structure, resolving the dispersion issue while maintaining data quantity.
Solution Approach 2:
The digital human model serves as a universal framework that can accommodate multiple dimensions of user data simultaneously. The model structure is designed to be multi-functional, handling various data types (image, text, sensor) and multiple data sources through a single integrated architecture, reducing integration complexity.
2Loss of information
If multiple dimensions of user data are collected, then the comprehensiveness of user profile increases, but the difficulty of data processing and mining increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modules: data acquisition module, data cleaning module, data annotation module, and digital human generation module. Each module handles specific aspects of processing, making the overall complex task more manageable while preserving all user information dimensions.
Solution Approach 2:
The patent introduces intermediate processing steps including data cleaning and data annotation as mediators between raw multi-dimensional data and the final digital human model. These intermediary processes organize and standardize diverse data types, reducing processing difficulty while maintaining information completeness.
3Measurement precision
If comprehensive user data is integrated into a digital human model, then the representation accuracy of users improves, but the system complexity increases
Solution Approach 1:
The patent transitions from traditional single-dimensional user profiles to multi-dimensional digital human representation. The system adds new dimensions including image dimension, health dimension, behavioral habit dimension, social pattern dimension, consumption habit dimension, and interest and hobby dimension, significantly improving user representation accuracy through dimensional expansion.
Data Source
AI summary
A digital human generation method and system, where the method includes: defining a digital human model, where the digital human model includes multiple dimensions of user profile models; acquiring multiple dimensions of data of a specific user that is from multiple data sources; and processing, based on the multiple dimensions of user profile models included in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user, where the multiple dimensions of user profiles of the specific user form a digital human corresponding to the specific user.