Carbon Credit Q&A Engine for Procurement Decision Support
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Solution Overview
Problem
It is difficult for entities seeking to procure carbon credits to determine the appropriate procurement strategies due to the complexity of available data and regulations.
Innovation Solution
An information processing apparatus equipped with a receiving unit to receive questions related to carbon credits and a generating unit to generate answers based on base data including information on greenhouse gases and carbon credits, utilizing a database to accumulate relevant data and implement a sentence generation model for interactive responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed base data on greenhouse gases and carbon credits is accumulated in a database, then the quality and relevance of information for procurement decision-making is improved, but the complexity of the information system increases
Solution Approach 1:
A natural language processing system acts as an intermediary between the accumulated base data and users seeking carbon credit procurement information. The system receives questions in natural language, processes them through the database of base data, and returns relevant information in an accessible format, thereby maintaining high information quality while shielding users from system complexity.
Solution Approach 2:
The system creates simplified copies or representations of complex base data through generated answers. Instead of requiring users to navigate complex databases directly, the system generates tailored responses that copy only the relevant information needed for specific procurement decisions, reducing perceived complexity while preserving information quality.
2Ease of operation
If a sentence generation model is used to provide interactive responses about carbon credits, then the ease of operation for users is improved, but the device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or manual information retrieval systems with an automated sentence generation model based on artificial intelligence. Users interact through natural language questions rather than navigating complex interfaces or querying databases directly, significantly improving ease of operation while the underlying processing complexity is managed through automated algorithms.
Solution Approach 2:
The sentence generation model enables users to obtain information through self-service interactions. Users can ask questions in their own words and receive relevant answers without requiring assistance from operators or knowledge of the underlying data structure, improving ease of operation while the system autonomously handles the processing complexity.
3Reliability
If comprehensive base data is provided to answer questions about carbon credit procurement, then the reliability of procurement decisions is improved, but the loss of time for processing and retrieving information increases
Solution Approach 1:
The system performs preliminary actions by pre-accumulating and organizing comprehensive base data on greenhouse gases and carbon credits in a structured database before queries are submitted. This preliminary organization of information allows the system to quickly retrieve and process relevant data when questions are asked, ensuring reliable decision-making without excessive retrieval time during actual use.
Solution Approach 2:
The sentence generation model provides feedback mechanisms that learn from user interactions and refine information retrieval. By analyzing question patterns and answer effectiveness, the system optimizes its querying and response generation processes over time, maintaining high decision reliability while progressively reducing information retrieval time through improved efficiency.
Data Source
AI summary
An information processing system of the present disclosure includes: a receiving unit configured to receive question data representing a question related to carbon credit; and a generating unit configured to generate an answer corresponding to details of the question data based on base data including information on greenhouse gas or carbon credit, and output the answer.