User Profile Generation via Digital Image Trend Analysis
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Solution Overview
Problem
Current targeted marketing techniques rely heavily on user profiles, but there is a need for improved methods to generate and enhance these profiles to better identify user interests and preferences, particularly through the analysis of digital image records.
Innovation Solution
A method that analyzes digital image records to identify trends, which are then used to generate or update user profiles by incorporating user subject interests, preferences, and levels of interest, allowing for the creation of relevant invitations such as advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional targeted marketing techniques are used to generate user profiles, then the marketing process can be implemented, but the user profiles lack sufficient quantity and quality of information to effectively identify user interests
Solution Approach 1:
The patent introduces digital image records as an intermediary data source between traditional marketing systems and user profile generation. These images serve as a mediator that contains implicit user interest information, which is then extracted through automated analysis processes, bridging the gap between existing marketing infrastructure and enhanced user profiling capabilities
Solution Approach 2:
The patent replaces manual profile creation methods with automated image analysis systems. Computer vision algorithms, machine learning models, and automated processing pipelines substitute human analysts, enabling scalable extraction of user interest information from large volumes of digital images without proportionally increasing operational complexity
2Measurement precision
If digital image records are analyzed to identify user interests, then the quality and relevance of user profiles improve, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary organization and tagging of digital image records before detailed analysis. Images are pre-categorized, metadata is extracted in advance, and potential user interests are identified through quick filtering processes, preparing the data structure ahead of time to accelerate the subsequent in-depth analysis phase
Solution Approach 2:
The patent divides the image analysis process into multiple independent stages: initial filtering, feature extraction, interest identification, and profile generation. Each stage processes specific aspects of the images independently, allowing for parallel processing and optimization of individual steps without requiring complete reprocessing of all images
3Adaptability or versatility
If multiple types of analysis are performed on digital image records (scene classification, face detection, object detection, audio analysis), then comprehensive user interest information is obtained, but the system complexity and processing requirements increase
Solution Approach 1:
The patent implements a multi-functional analysis platform that performs scene classification, face detection, object detection, and audio analysis through integrated processing modules. A single system architecture handles multiple analysis types by routing images through different processing pipelines as needed, eliminating the need for separate dedicated systems for each analysis function
Solution Approach 2:
The patent employs dynamic analysis workflows that adapt the type and depth of processing based on the specific characteristics of each image and the user's profile generation needs. The system dynamically selects which analysis functions to apply to each image, adjusting processing intensity and methodology in real-time rather than applying all analyses uniformly to all images
Data Source
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AI summary
Systems and methods for generating user profiles based at least upon an analysis of image content from digital image records are provided. The image content analysis is performed to identify trends that are used to identify user subject interests. The user subject interests may be incorporated into a user profile that is stored in a processor-accessible memory system.