AI Report Generation for ESG Questionnaire Automation
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Solution Overview
Problem
Preparing and responding to ESG-related questionnaires and sustainability reports is a complex, time-consuming task that requires significant human resources and is prone to subjective errors due to the need for accurate, reliable, and credible information across various environmental, social, and governance topics.
Innovation Solution
An automated method using an electronic device equipped with an AI model and deep learning models to convert questionnaire files into topic datasets, perform text analysis on historical document data to filter relevant reference datasets, generate response content for each topic, and produce objective, accurate questionnaire response reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual preparation of ESG reports is performed, then human judgment and flexibility can be applied, but the process becomes time-consuming and prone to subjective errors
Solution Approach 1:
The patent replaces the manual mechanical process of ESG report preparation with an automated system comprising an AI model and deep learning model. The processor automatically converts questionnaire files to topic datasets, performs text analysis on historical documents, and generates response content without human intervention, thereby eliminating subjective errors while maintaining efficiency
Solution Approach 2:
The system enables self-service by allowing the electronic device to automatically generate ESG reports using its own integrated AI models and deep learning capabilities. The processor independently completes the entire workflow from questionnaire conversion to report generation without requiring external human assistance, improving both objectivity and time efficiency
2Reliability
If comprehensive data collection and analysis are performed to ensure accuracy, then report reliability improves, but the complexity and resource requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing historical document data through the deep learning model before the actual report generation. The AI model converts questionnaire files into structured topic datasets in advance, and the deep learning model pre-analyzes historical documents to identify relevant information, ensuring data readiness and reliability while simplifying the final report generation process
Solution Approach 2:
The patent introduces an intermediary layer consisting of the AI model and deep learning model that mediates between the raw questionnaire files/historical documents and the final ESG report. This intermediary automatically performs data collection, conversion, analysis, and filtering, ensuring comprehensive and reliable data processing while reducing the apparent complexity for users
3Productivity
If multiple ESG topics are covered comprehensively, then the completeness of the report improves, but the workload and human resources required increase
Solution Approach 1:
The patent implements universality by designing a multi-functional system where the AI model and deep learning model can handle multiple ESG topics (environmental, social, and governance issues) through a single integrated process. The system universally processes various types of input data including questionnaire files, historical documents, and company information to generate comprehensive reports across all ESG dimensions without requiring separate processes for each topic
Solution Approach 2:
The system merges multiple functions into a unified automated process: the AI model combines questionnaire conversion and topic identification, while the deep learning model combines text analysis and information filtering. This merging of previously separate tasks into integrated automated functions improves productivity by handling multiple ESG topics simultaneously while reducing the resources and complexity associated with manual multi-topic report preparation
Data Source
AI summary
Disclosed are a method for automatically generating a report and an electronic device thereof. The method includes: converting a questionnaire file through an artificial intelligence (AI) model to obtain a topic data set, where the topic data set includes multiple topics identified from the questionnaire file; performing text analysis on historical document data through a deep learning model to filter out a reference data set that matches the topic data set from the historical document data; obtaining response content corresponding to each topic from the reference data set through the AI model; and generating a questionnaire response report based on the response content through the AI model and providing the questionnaire response report to a website.