Context-Based Text Generation for Multi-Modal Content Relevance
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Solution Overview
Problem
The process of manually sifting through large amounts of social media content to select and add relevant information is time-consuming and involves complex user interfaces, making it inefficient for users to manage or create content effectively.
Innovation Solution
A computer-implemented method using machine-learned models to generate context-based text content by detecting, recognizing, and classifying features in content data associated with various modalities, including images, audio, and video, to automatically produce context-based text segments and content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sifting and selection of social media content is performed, then relevant information can be identified, but the process becomes time-consuming and requires complex user interface interactions
Solution Approach 1:
The system performs automatic content analysis and text generation without requiring manual user intervention. Machine learning models autonomously detect features, determine contexts, and generate text content, allowing the system to serve itself rather than requiring manual sifting and selection by users
Solution Approach 2:
The patent replaces manual mechanical processes (user clicking, scrolling, selecting) with automated computational processes. Machine learning models substitute for human users in performing content analysis, feature detection, and text generation tasks that previously required manual interaction
2Reliability
If manual selection and addition of relevant information to social media content is performed, then content relevance is improved, but the process involves interaction with complex user interfaces
Solution Approach 1:
The system automatically determines contexts and generates text content without requiring users to navigate complex interfaces. The machine learning model autonomously performs feature detection, context determination, and text generation, eliminating the need for manual user interaction with complicated controls
Solution Approach 2:
The patent extracts the complex processing tasks from the user interface and relocates them to backend machine learning models. Users simply provide input content, while the system handles the complex analysis and generation processes automatically, separating the simple user interaction from the complex processing
3Productivity
If automated text generation using machine-learned models is implemented, then manual effort is reduced and real-time updates are enabled, but the system requires processing of multiple data modalities
Solution Approach 1:
The machine learning model is designed to handle multiple data modalities (images, text, audio, video) through a unified architecture. The model performs feature detection, context determination, and text generation across different input types, making it a universal system that can process diverse content types through a single multi-functional platform
Data Source
AI summary
Methods, systems, devices, and non-transitory computer readable media for generating context-based text content are provided. The disclosed technology can include receiving content data comprising content associated with one or more data modalities. One or more associated with the content data can be determined. Based on inputting the content data and context data based on the one or more contexts into one or more machine-learned models, one or more context-based text segments based on the content data can be generated. The one or more machine-learned models can be configured to generate the one or more context-based text segments based on recognition of one or more features of the content data and the context data. Furthermore, context-based text content based on the one or more context-based text segments can be generated.