Automated Poll Generation from Conversation Context
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Solution Overview
Problem
In team collaboration settings, managing decision-making processes becomes cumbersome due to the volume of messages and diverse opinions across various communication platforms, leading to confusion and decreased productivity.
Innovation Solution
An automated system generates and presents polls based on conversation context using natural language processing and machine learning, ensuring polls are relevant and appropriate, improving decision-making efficiency by aggregating user messages from multiple platforms and utilizing a large language model to determine poll types and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual decision-making processes are used in team collaboration, then users can discuss and deliberate on decisions, but the process becomes cumbersome due to message volume and diverse opinions
Solution Approach 1:
The patent introduces an automated poll generation system as an intermediary between team members' communications and the decision-making process. The system monitors communication platforms, identifies decision points, and automatically generates structured polls to aggregate opinions, thereby reducing the complexity of managing diverse opinions manually while maintaining productive deliberation.
Solution Approach 2:
The patent replaces the mechanical process of manual decision-making coordination with an automated computational system. Natural language processing and machine learning models automatically analyze communications, detect decision opportunities, and generate polls, substituting the manual mechanical process of tracking and synthesizing diverse opinions with an automated information processing system.
2Productivity
If polls are generated frequently to capture all decision opportunities, then decision-making efficiency improves, but resource utilization decreases due to unnecessary poll generation
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on communication patterns and decision-making contexts before deployment. The models are pre-conditioned to recognize decision opportunities accurately, reducing the need for excessive poll generation and optimizing resource utilization from the outset rather than through trial and error.
Solution Approach 2:
The system implements feedback mechanisms where poll outcomes and user responses are fed back into the machine learning models to continuously improve their accuracy in identifying decision opportunities. This feedback loop reduces unnecessary poll generation over time as the models become more precise, thereby improving resource utilization while maintaining decision-making efficiency.
3Productivity
If automated poll generation is implemented, then decision-making efficiency improves, but system complexity increases due to machine learning model integration
Solution Approach 1:
The patent segments the automated poll generation system into distinct functional modules: communication monitoring components, natural language processing modules, machine learning model layers, and poll generation interfaces. This segmentation allows each component to be developed, maintained, and optimized independently, reducing overall system complexity while maintaining the benefits of automation.
Solution Approach 2:
The patent creates a universal poll generation system that can operate across multiple communication platforms and contexts using a single machine learning model framework. The system is designed to handle various types of communications (messages, emails, chat) and generate appropriate polls universally, reducing the need for platform-specific implementations and simplifying system architecture.
Data Source
AI summary
A method, computer system, and computer program product are provided for automatically generating polls. A message of a plurality of messages is received corresponding to a conversation between a plurality of users. One or more candidate polls are generated using a natural language processing model and determining a poll type for each of the one or more candidate polls based on the message and a context of the conversation. It is determined that at least one candidate poll of the one or more candidate polls is relevant according to the context of the conversation. In response to determining that the at least one candidate poll is relevant, the poll of the poll type is generated based on the message for presentation to the plurality of users.