User-Query Data Stories and Summaries with Feedback-Based Fact Refinement
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Solution Overview
Problem
Existing data analytics and visualization tools are difficult for non-experts to use due to the requirement of data science knowledge and graphical design skills, leading to inefficient and resource-intensive manual workflows for generating meaningful visualizations and summaries.
Innovation Solution
A system that facilitates automated generation of data stories and summaries via natural language processing, using user queries to identify relevant facts and generate visual designs and summaries through a machine learning model, allowing users to provide feedback for refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated data story generation is implemented, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system implements feedback loops where user interactions with generated data stories (such as liking, sharing, or correcting facts) are used to refine and improve the accuracy of future automated generations. This allows the system to maintain ease of operation while progressively improving precision through learned adjustments.
Solution Approach 2:
The patent replaces manual mechanical processes of data verification and story creation with automated computational systems using AI and machine learning algorithms. This substitution maintains operational simplicity while achieving precision through algorithmic analysis rather than human expertise.
2Measurement precision
If manual data verification is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary automated fact-checking and data verification before presenting data stories to users. By pre- validating the accuracy of generated content through automated processes, the system reduces the need for time-consuming manual verification while maintaining precision standards.
Solution Approach 2:
Manual data verification processes are replaced with automated computational verification systems that use algorithms to check data accuracy, validate sources, and verify facts. This substitution dramatically reduces verification time while maintaining or improving precision through consistent algorithmic application.
3Productivity
If extensive data analysis is performed, then productivity is improved, but use of energy increases
Solution Approach 1:
The system performs partial data analysis by focusing computational resources only on the specific data elements and facts most relevant to the user's query and context. Rather than analyzing entire datasets, the AI selectively processes only necessary portions, maintaining high productivity while reducing energy consumption through targeted computation.
Solution Approach 2:
The system dynamically adjusts computational parameters such as analysis depth, data sampling rates, and processing complexity based on query importance and resource availability. This allows the system to optimize the balance between productivity and energy usage by scaling computational effort to match actual needs rather than performing exhaustive analysis in all cases.
Data Source
AI summary
Methods, computer systems, computer storage media, and graphical user interfaces are provided for facilitating generation of data stories and data summaries in accordance with user queries. In one implementation, a user query is obtained in association with a dataset. Thereafter, a set of facts relevant to the user query are identified. A data story is generated using a portion of the set of facts relevant to the user query. The data story includes a set of visualizations corresponding with the portion of the set of facts relevant to the user query. The set of facts relevant to the user query is used to generate a data summary of the set of relevant facts. The data story and/or the data summary are provided for display.