Machine Learning Report Summarization to Reduce Manual Effort
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generating summary reports from extensive base reports in organizations involves significant manual effort and time-consuming processes, as managers often need to manually reduce the volume of reported information.
Innovation Solution
Applying a trained machine learning model to identify and generate summary reports by selecting relevant content from base reports, using data classification and generative AI to create natural language summaries, charts, and graphs tailored to different hierarchical levels in an organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual summarization is used to reduce information volume, then report accuracy and relevance are improved, but time consumption and manual effort increase significantly
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the base reports and the summary report. The model automatically identifies and extracts relevant content from multiple base reports, serving as a mediator that reduces manual effort while maintaining information quality. This resolves the contradiction by automating the summarization process without sacrificing relevance.
Solution Approach 2:
The patent replaces the mechanical manual summarization process with an automated machine learning system. The ML model performs content identification, extraction, and synthesis tasks that were previously done manually, thereby eliminating the trade-off between manual effort and time consumption while maintaining or improving information quality.
2Loss of information
If extensive base reports are compiled to ensure comprehensive information, then report completeness is improved, but processing complexity and time increase
Solution Approach 1:
The patent extracts only the essential and relevant content from extensive base reports using a machine learning model. Instead of processing and compiling all information from base reports, the system identifies and extracts key elements that maintain completeness while reducing processing complexity. This selective extraction resolves the contradiction between comprehensive information and processing complexity.
Solution Approach 2:
The patent applies different processing qualities to different parts of the base reports. The machine learning model identifies regions of high importance and extracts detailed information from those areas while summarizing or omitting less critical sections. This localized quality approach ensures comprehensive coverage of important information while reducing overall processing complexity.
3Reliability
If manual report assembly is performed to maintain data confidentiality and customization, then data security and user needs alignment are improved, but productivity decreases
Solution Approach 1:
The patent implements a system where the machine learning model automatically handles content selection and summary generation, making the system self-sufficient for the summarization task. The model inherently maintains data confidentiality by processing only authorized base reports and generates customized summaries without requiring manual intervention. This automation resolves the contradiction between data security, customization, and productivity.
Data Source
AI summary
Techniques are described herein that provide machine learning-augmented report summarization. One or more embodiments train and apply a machine learning model to generate a summary report for an entity that is associated with a particular hierarchical level in an organization utilizing base reports from entities at another hierarchical level in the organization. A training data set used for training the machine learning model includes base reports at a particular hierarchical level in the organization and identification of content from the base reports that is to be used for generating a summary report. The machine learning model may then be applied to any set of base reports to generate a corresponding summary report.