Automated Narrative Workflow for Audience Tailoring
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Solution Overview
Problem
Conventional AI-based narrative creation systems are inefficient and ineffective due to the high effort required from users to gather and format data, leading to narratives that may lack important details and fail to engage the intended audience.
Innovation Solution
A flexible end-to-end workflow that integrates machine learning (ML) techniques, natural language processing (NLP), and graph-based retrieval-augmented generation (RAG) to automatically collect user inputs, stories, and document insights, and generate cohesive, accurate, and effective narratives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional AI-based narrative creation systems are used, then narrative generation is automated, but user effort for data gathering and formatting remains high
Solution Approach 1:
The system performs preliminary actions by proactively gathering data from multiple sources (databases, APIs, user inputs) and formatting it before the user requests narrative generation. This eliminates the need for users to manually collect and format data, resolving the contradiction between automation extent and ease of operation.
Solution Approach 2:
The system serves itself by automatically identifying data requirements, collecting data from appropriate sources, and preparing it in the necessary formats. This self-service capability removes the burden of data gathering and formatting from users while maintaining high automation levels.
2Extent of automation
If conventional AI-based narrative creation systems are used, then narrative generation is automated, but narratives may lack important details
Solution Approach 1:
The system is designed to handle multiple data types and sources universally, collecting not only structured data but also unstructured data from various repositories. This multi-functional data collection capability ensures comprehensive information gathering, preventing loss of important details while maintaining automation.
Solution Approach 2:
The system deliberately collects excessive or partial data beyond what is immediately apparent as necessary, gathering additional context and details that may be relevant. This approach ensures that no important information is lost during automated narrative generation.
3Extent of automation
If conventional AI-based narrative creation systems are used, then narrative generation is automated, but narratives fail to engage the intended audience
Solution Approach 1:
The system applies local quality by customizing different portions of the narrative based on the specific audience characteristics. It adjusts tone, style, and content emphasis for different audience segments within the same automated process, resolving the contradiction between automation and adaptability.
Solution Approach 2:
The system dynamically adapts narrative parameters based on audience profiles and context. This dynamic adjustment capability allows the automated system to produce audience-specific narratives, maintaining versatility while preserving automation benefits.
4Adaptability or versatility
If manual narrative creation is used, then narratives can be tailored to specific audiences, but the process requires diverse skills and is time-consuming
Solution Approach 1:
The system replaces the mechanical process of manual narrative creation with an automated AI-based system. This substitution maintains audience tailoring capabilities through intelligent algorithms while dramatically improving productivity by eliminating the need for diverse human skills and reducing time consumption.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for narrative creation. A method generally includes receiving a selection of a first narrative type for generation, obtaining: a plurality of user responses to a plurality of prompts associated with the first narrative type; and at least one of: one or more stories from one or more users stored in a repository; or one or more insights associated with one or more documents stored in the repository, and processing, by one or more machine learning (ML) models, the plurality of user responses and at least one of the one or more stories or the one or more insights to generate an output associated with the first narrative type.