Writing Assistance System Using Segmented Expert Routing
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Solution Overview
Problem
Electronic communications, such as emails and text messages, often suffer from miscommunication due to poor syntax, grammar, or inadequate conveyance of sensitive information, particularly when senders lack proficiency in writing clear and concise messages.
Innovation Solution
A system and method providing on-demand writing assistance services that include live expert help for editing electronic communications, selecting appropriate assistants based on category and tonal information, and offering certification of the edited message's quality, ensuring professional tone and language optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated grammar checking tools are used, then writing efficiency is improved, but the ability to handle nuanced tone and context is insufficient
Solution Approach 1:
The system segments the writing assistance task into multiple specialized components: automated grammar checking for mechanical errors, human experts for tone and context nuances, and different expert types (editors, subject matter experts, native speakers) for specific aspects. This segmentation allows both automated efficiency and human judgment to be applied where appropriate.
Solution Approach 2:
The system introduces an intermediary layer that connects automated tools with human experts. The platform receives the draft, applies automated checking first, then routes specific aspects to appropriate human experts who act as intermediaries between the automated system and the final polished message, ensuring both efficiency and nuanced understanding.
2Reliability
If multiple expert reviewers are assigned to ensure high quality, then message quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary automated grammar and syntax checking before human expert review. This preliminary action catches obvious errors early, allowing human experts to focus their time on more nuanced tone and context issues, thereby reducing overall processing time while maintaining high quality.
Solution Approach 2:
The system applies partial review by different expert types based on the specific needs of each message. Not every message requires all expert types; the system selectively applies editing, subject matter expertise, and native speaker review based on the message characteristics, avoiding unnecessary review steps while ensuring adequate quality control.
3Measurement precision
If detailed tonal information and category classification are collected, then assistant selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses a universal classification framework that categorizes messages by common dimensions (professional/personal, formal/informal, urgent/non-urgent) that apply across different contexts. This universal approach allows the same classification system to serve multiple purposes: selecting appropriate experts, determining review depth, and routing to the right specialists without requiring separate classification systems for each message type.
Data Source
AI summary
A system for providing assistance with electronic communications includes a network device configured to communicate with a client computing device, a processor, and a memory including instructions stored thereon. When the instructions are executed by the processors, the instructions cause the system to receive category information and tonal information of an electronic message and a certification level selected from a plurality of certification levels, from the client computing device via the network device, create a project for the electronic message, select an assistant from a plurality of assistants for the project based on the category information and the tonal information, and provide a message, which has been edited by the selected assistant based on the category information and the tonal information, to the client computing device.