Content Classification via Multi-Modal Data Recognition
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Solution Overview
Problem
Existing technologies fail to effectively classify and filter out inappropriate content in networked environments, such as websites and applications, which can lead to adverse user experiences and reputational damage, particularly for advertisers and publishers.
Innovation Solution
A system that utilizes data recognition modules to identify and classify content items like images, audio, and text using OCR, speech-to-text, and text crawling processes, comparing them against predefined definitions to determine appropriateness, thereby enabling targeted advertisement placement, content filtering, and parental control features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content classification systems are implemented to filter inappropriate material, then user experience and reputation are improved, but system complexity and processing time increase
Solution Approach 1:
The content classification system is divided into separate modules: text recognition module, image recognition module, audio recognition module, and classification module. Each module handles specific types of content independently, reducing overall system complexity while maintaining comprehensive classification capability.
Solution Approach 2:
The system performs preliminary classification of content before it is displayed or distributed to users. By pre-classifying websites, applications, products, and content items, the system avoids the need for real-time analysis during user interaction, reducing processing time and complexity.
2Measurement precision
If comprehensive data recognition processes are used to identify all content types, then classification accuracy is improved, but processing speed and efficiency decrease
Solution Approach 1:
Different recognition processes (OCR for text, image recognition for images, speech-to-text for audio) are segmented and applied only to relevant content types. This allows each recognition process to operate independently and efficiently on its specialized data type, maintaining high accuracy without the overhead of processing all content types through all recognition processes.
Solution Approach 2:
The system applies recognition processes selectively based on the content type being analyzed. For example, OCR is applied only to text elements, image recognition only to visual content, and speech-to-text only to audio content. This partial application of recognition processes maintains classification accuracy while significantly improving processing speed by avoiding unnecessary analysis.
Data Source
AI summary
One or more content items can be received at a data recognition module. The data recognition module can utilize, individually or in any combination, image recognition (e.g., OCR, object recognition, etc.), audio recognition (e.g., speech recognition, music identification, etc.), and/or text recognition (e.g., text crawling) in order to identify or recognize at least a portion of the one or more content items. Based on the identified content portion(s), the one or more content items and/or their respective source(s) can be classified. In one example, an image containing a not yet machine-readable curse word can be included in a source webpage. The image can be received at the data recognition module. The curse word contained in the image can be recognized/identified using an OCR process. Based, at least in part, on the recognized/identified curse word, the image and/or the webpage can be classified as likely being associated with inappropriate material.