Conversation Content Augmentation Using Intent-Based Item Selection
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Solution Overview
Problem
Existing conversation systems require significant time and effort to create content items, especially for stories and unstructured conversations in digital environments, which many users lack the skills or time to manage, and existing digital environments lack easy interfaces for adding content.
Innovation Solution
A conversation augmentation system that uses machine learning to automatically select and add content items to conversations based on natural language inputs, interpreting user intents and applying constraints to match content items with the conversation context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually create content items for conversations, then content quality and engagement are improved, but time consumption and effort increase significantly
Solution Approach 1:
The system enables self-service by automatically generating content items from conversation transcripts without requiring manual user intervention. The machine learning model processes conversation data and produces content items autonomously, allowing the system to serve itself rather than requiring users to manually create each content item.
Solution Approach 2:
The patent replaces the mechanical manual creation process with an automated machine learning system. Instead of users manually creating content items through manual effort, the system uses ML models to automatically generate content items from conversation transcripts, substituting human mechanical action with automated computational processing.
2Adaptability or versatility
If users create detailed content items for stories, then storytelling capability is improved, but skill requirements and complexity increase
Solution Approach 1:
The system performs the complex task of story content creation automatically without requiring users to possess specialized skills. The machine learning model handles the complexity of generating coherent story content with multiple characters, settings, and sequential structures, while users simply need to provide conversation input.
Solution Approach 2:
The patent substitutes the complex manual process of creating detailed story content with an automated machine learning system. The ML model handles the sophisticated tasks of understanding story elements, generating appropriate content items, and maintaining narrative coherence, replacing the need for users to manually perform these complex creative tasks.
3Productivity
If automated systems select content items, then productivity is improved, but precision in matching conversation context may deteriorate
Solution Approach 1:
The system uses feedback mechanisms where the machine learning model continuously learns from conversation context and user interactions. The model analyzes the conversation transcript, identifies relevant segments, and selects content items while incorporating feedback from the conversation flow to improve context matching accuracy over time.
Solution Approach 2:
The patent replaces simple automated selection with an intelligent machine learning system that substitutes basic automation with sophisticated contextual understanding. The ML model analyzes conversation semantics, identifies relevant topics and entities, and selects content items based on contextual relevance rather than just keyword matching, maintaining precision while improving productivity.
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.