Conversation Augmentation System Using ML for Content Relevance
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Solution Overview
Problem
Existing technologies face challenges in efficiently augmenting conversations with content items, particularly in creating and selecting visuals and other content items for storytelling and unstructured conversations, especially in digital environments, due to time constraints and lack of user expertise.
Innovation Solution
A conversation augmentation system that uses machine learning to interpret natural language and select appropriate content items based on user intents, context signals, and constraints, automatically generating and integrating content into digital platforms like video chats and virtual reality environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually create content items for conversations, then content quality and relevance can be ensured, but significant time and effort are required
Solution Approach 1:
The system enables automated self-service by using machine learning models to automatically generate, select, and insert content items into conversations without requiring manual user intervention. The system interprets natural language, determines user intents, and autonomously retrieves and integrates relevant content items, thereby eliminating the time-consuming manual creation process while maintaining content relevance through intelligent algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of content creation with an automated machine learning-based system. Instead of users manually searching, selecting, and inserting content items, the system uses natural language interpretation, intent determination, and automated content retrieval to substitute the manual mechanical process with an intelligent automated system that achieves both speed and relevance.
2Manufacturing precision
If users create content items for stories with multiple characters and settings, then narrative accuracy can be maintained, but the complexity beyond user ability arises
Solution Approach 1:
The system introduces an intermediary machine learning-based content generation system that mediates between the user's simple story input and the complex requirements of creating accurate story content. The intermediary automatically handles the complex tasks of tracking multiple characters, settings, and narrative elements, translating user intent into accurate story content without requiring the user to manually manage the complexity.
Solution Approach 2:
The automated story content generation system performs self-service by automatically tracking story elements, generating appropriate content items, and maintaining narrative accuracy without user intervention. The system independently manages the complexity of multiple characters and settings through its machine learning capabilities.
3Reliability
If content items are manually selected for unstructured conversations, then appropriateness can be ensured, but the selection difficulty increases
Solution Approach 1:
The system replaces the manual mechanical process of content selection with an automated machine learning system that interprets natural language, determines user intents, and automatically selects appropriate content items. This substitution maintains content appropriateness through intelligent analysis while dramatically improving ease of operation by eliminating manual selection efforts.
Solution Approach 2:
The system enables self-service by automatically selecting and inserting appropriate content items into unstructured conversations without requiring user intervention. The machine learning models independently analyze the conversation context and autonomously choose relevant content, thereby ensuring appropriateness while simplifying the user experience.
4Manufacturing precision
If advanced editing tools are used to create digital content, then content quality improves, but the skill requirement increases
Solution Approach 1:
The system enables automated self-service by using machine learning models to generate and select digital content items without requiring users to operate advanced editing tools. The system independently handles content creation and selection, delivering high-quality digital content while eliminating the need for specialized skills in content editing and digital production.
Data Source
AI summary
A conversation augmentation system can automatically augment a conversation with content items based on natural language from the conversation. The conversation augmentation system can select content items to add to the conversation based on determined user “intents” generated using machine learning models. The conversation augmentation system can generate intents for natural language from various sources, such as video chats, audio conversations, textual conversations, virtual reality environments, etc. The conversation augmentation system can identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. In various implementations, the conversation augmentation system can add selected content items to a storyline the conversation describes or can augment a platform in which an unstructured conversation is occurring.