Intent-Based Messaging Responses via Dialog Manager and Action Server
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Solution Overview
Problem
Existing messaging platforms lack efficient methods for intent-based action recommendations and fulfillment, leading to increased burden on coaches, redundant responses, and suboptimal user experiences.
Innovation Solution
A system and method that includes a Dialog Manager and an Action Server to provide intent-based action recommendations and fulfillment, utilizing trained models to generate contextually relevant responses with minimal user input, reducing latency and improving response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional messaging platforms are used without intent-based action recommendations, then coaches can maintain full control over responses, but response time increases and handling capacity decreases
Solution Approach 1:
The patent introduces a Dialog Manager as an intermediary component between the user interface and the action server. This mediator automatically analyzes incoming messages, determines user intent, and generates appropriate response recommendations, thereby reducing the time coaches spend on manual response composition while maintaining controlled system complexity through modular architecture.
Solution Approach 2:
The system enables self-service through automated intent classification and response generation capabilities. The Dialog Manager autonomously processes incoming messages, classifies intents using trained models, and generates response recommendations without requiring manual intervention for each message, thus reducing response time while keeping the system manageable through automated operations.
2Productivity
If manual response composition is used, then response accuracy can be maintained, but coach burden increases and productivity decreases
Solution Approach 1:
The patent replaces the mechanical process of manual response composition with an automated system consisting of a Dialog Manager and Action Server. This substitution uses trained machine learning models to automatically classify intents and generate response recommendations, significantly increasing coach handling capacity while reducing operational burden through automation of repetitive tasks.
Solution Approach 2:
The system employs copying by using trained models that learn from existing high-quality responses and conversation patterns. These models generate response recommendations by copying and adapting proven effective response structures and language, enabling coaches to maintain accuracy standards while processing more messages with less effort.
3Loss of time
If generic response templates are used, then response speed increases, but response relevance and targeting decrease
Solution Approach 1:
The patent applies local quality by customizing responses based on the specific intent classification of each incoming message. The Action Server generates tailored response recommendations that are locally optimized for each identified intent type, ensuring high relevance and targeting accuracy while maintaining fast response generation speeds through pre-configured intent-specific response templates and structures.
Data Source
AI summary
A system for intent-based action recommendations and/or fulfillment in a messaging platform, preferably including and/or interfacing with: a set of user interfaces; a set of models; and/or a messaging platform. A method for intent-based action recommendations and/or fulfillment in a messaging platform, preferably including any or all of: receiving a set of information associated with a request; producing and sending a set of intent options; receiving a selected intent; generating a message based on the selected intent and/or the set of information; and providing the message.