Multimedia User Profile Creation via Machine Learning Extraction
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Solution Overview
Problem
Existing solutions fail to effectively automatically determine user attributes from multimedia files stored on mobile devices, limiting their usefulness for personalized advertisements and other applications.
Innovation Solution
A computer-implemented system and method that processes multimedia files using machine learning models to extract content data and metadata, producing content-based user attributes that can be used to infer user interests and traits, which are then stored in a user profile database for various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to process multimedia files, then user attribute extraction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the multimedia processing task into distinct phases: initial user profile information collection, multimedia file processing, attribute inference, and profile updating. This segmentation allows the system to process files incrementally rather than all at once, reducing peak computational load and processing time while maintaining accuracy through systematic analysis of images and videos.
Solution Approach 2:
The system performs preliminary actions by collecting initial user profile information before multimedia processing. This pre-collected data serves as a foundation that guides the machine learning models, allowing them to focus on specific attribute inference tasks rather than analyzing all possible attributes from scratch, thereby reducing processing time while maintaining extraction accuracy.
2Loss of information
If comprehensive multimedia analysis is performed, then user profile completeness is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal processing framework that handles multiple types of multimedia files (images and videos) using the same machine learning model architecture. The system extracts various user attributes (interests, activities, demographics) through a unified approach, reducing the need for separate specialized processing systems for each file type or attribute category, thereby managing complexity while maintaining profile completeness.
Solution Approach 2:
The system introduces an intermediary layer of attribute inference that translates raw multimedia content into structured user profile attributes. This intermediary processing stage simplifies the complexity by converting complex multimedia analysis into standardized attribute categories that can be systematically stored and utilized, making the overall system more manageable while preserving comprehensive user information.
3Measurement precision
If manual user input is required for profile creation, then profile accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically collect and process multimedia files from the user's device without requiring manual profile creation. The machine learning models automatically extract user attributes from images and videos, and the system autonomously updates the user profile, eliminating the need for users to manually input information while maintaining profile accuracy through automated analysis.
Solution Approach 2:
The system replaces the mechanical process of manual user input with automated machine learning-based attribute extraction. Instead of users manually filling out profile forms, the system uses computational algorithms to analyze multimedia content and infer user attributes, substituting manual operation with automated intelligent processing that maintains accuracy while improving ease of use.
Data Source
AI summary
The present invention is a computer-implemented system and method for creating a user profile based on multimedia files. The method includes storing multimedia files associated with one or more users in at least one computing device. The method includes obtaining content data from the multimedia files stored in the computing device through a content extraction module. The content extraction module performs steps to obtain the content data from multimedia files. A mobile application accesses the files stored in the computing device. Files are extracted and processed by utilizing one or more machine learning algorithms and retrieving content data and file metadata for each multimedia file from the processed data. Content-based user attributes are created by analyzing the aggregated content data and file metadata, Content-based user attributes are transmitted to an application server and stores the content-based user attributes to a user profile database.