AI Response Prioritization for Context-Aware Workplace Replies
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Solution Overview
Problem
Modern communication systems struggle to effectively convey nuanced responses in text-based interactions, particularly in work scenarios, due to the lack of clarity in emphasis and loss of contextual cues from body language, leading to misunderstandings and complex social dynamics.
Innovation Solution
A machine learning model trained with Long Short-Term Memory (LSTM) and Layer-wise Relevance Propagation (LRP) is used to analyze conversations and assist users in crafting tactful responses to work-related requests, utilizing a priority request response program to identify and prioritize responses based on contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If text-based communication is used for global instant communication, then communication speed and accessibility are improved, but social nuance and contextual clarity are lost
Solution Approach 1:
The patent introduces an AI intermediary system that analyzes text communications and generates suggested responses incorporating appropriate social nuance, tone, and contextual information. This intermediary layer translates between raw text input and socially-aware responses, helping users navigate the information loss inherent in text-based communication.
2Measurement precision
If machine learning model is trained on conversation data, then response accuracy and social appropriateness are improved, but data processing complexity and training requirements increase
Solution Approach 1:
The patent implements preliminary training of the machine learning model on historical conversation data before actual use. This pre-training phase allows the model to learn patterns of appropriate responses and social nuance in advance, so that during actual communication the model can quickly generate accurate responses without requiring complex real-time training.
3Productivity
If AI assistance is provided for response generation, then communication effectiveness is improved, but dependency on automated systems increases
Solution Approach 1:
The patent implements a system where users maintain control over their communications while receiving AI assistance. The machine learning model provides suggested responses that users can review, modify, or reject entirely. This self-service approach allows users to benefit from AI's pattern recognition while maintaining autonomy and accountability for their communications.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for assisting with priority-based responses to requests is provided. The embodiment may include analyzing one or more conversations, wherein analyzing includes identifying data from within the one or more conversations, wherein the data includes at least one request and at least one response. The embodiment may also include training a machine learning model on the identified data using long short-term memory with layer-wise relevance propagation. The embodiment may further include assisting a user in responding with a new response to a new request using the trained model.