Quantum-Assisted Draft Email Carbon Analysis Before Sending
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Emails contribute significantly to carbon emissions due to energy consumption in data centers and printing, and users lack real-time awareness and guidance to reduce these emissions during drafting.
Innovation Solution
A system utilizing machine learning and quantum computing analyzes draft emails to calculate carbon emissions and provides immediate recommendations or automatic modifications to reduce emissions, including classification of email addresses, attachments, and spam detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple classical machine learning processes are applied sequentially to email log descriptions, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The patent converts email log descriptions to text feature vectors in advance, before applying multiple classical machine learning processes. This preliminary conversion is performed once and reused across multiple classification models (spam detection, attachment detection, carbon emission calculation), eliminating redundant conversions and reducing overall processing time while maintaining classification accuracy.
Solution Approach 2:
The patent merges multiple classification processes by applying them to a shared text feature vector representation. Instead of converting email logs separately for each model, the system creates a single feature vector that serves as input for multiple classical machine learning processes simultaneously, reducing computational overhead and processing time.
2Productivity
If quantum computing is applied to detect attachments in draft emails, then processing speed is improved, but system complexity increases
Solution Approach 1:
The patent uses an intermediary approach by converting the draft email image to a text feature vector representation before applying quantum computing. This intermediate step transforms visual data into a format suitable for quantum processing, enabling faster attachment detection while managing system complexity through standardized data transformation procedures.
3Object-generated harmful factors
If real-time carbon emission analysis is provided to users before sending emails, then carbon emission reduction is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary classification of email characteristics (spam detection, attachment detection, recipient analysis) before calculating carbon emissions. By pre-processing and categorizing email features using multiple machine learning models, the system reduces the computational scope required for carbon emission analysis, enabling real-time feedback with reduced energy consumption.
Solution Approach 2:
The system automatically analyzes draft emails and provides carbon emission recommendations without requiring user intervention. Users simply draft their emails and receive automated feedback, eliminating the need for manual carbon calculation and reducing overall computational resource requirements while maintaining real-time emission reduction capabilities.
Data Source
AI summary
The present disclosure describes a system and method for applying classical machine learning together with quantum machine learning to extract features from a draft email and from metadata of the draft email to identify issues that can influence the carbon emissions caused by the draft email upon sending. The system and method can further determine specific modifications for the draft email that can reduce the carbon emissions caused by the draft email upon sending. The system and method can offer the user with a selection to have the draft email automatically modified to reduce carbon emissions. The method may further include analysis of job profiles and behaviors of individual employees to determine whether the draft email is relevant to the recipients in the “to:” field of the draft email, as well as to provide analytics related to carbon emissions associated with emailing and printing behaviors of employees.