Dynamic Messaging Bot Control via Machine Learning Intent Detection

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Solution Overview

Problem

Current messaging systems lack efficient methods to integrate messaging bots seamlessly into user interactions, leading to friction and clutter, and fail to personalize the experience of engaging with network-accessible services within a messaging context.

Innovation Solution

The implementation of a messaging bot control system that uses machine-learning to detect user intent, allowing for dynamic configuration of messaging bot options and menus based on the context of user interactions, thereby enhancing the user experience and streamlining service interactions within messaging threads.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If messaging bots are integrated into messaging systems, then service interactions are enhanced, but friction and clutter increase

Engineering Contradiction:

Improveservice interaction capabilityVSAvoiduser interaction smoothness

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The messaging bot system automatically detects user intent using machine learning and dynamically configures bot options without requiring manual user setup. The bot monitors messaging interactions, determines user intent, and autonomously adjusts its behavior and menu configurations to serve the user's needs directly within the conversation flow.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The messaging bot configuration is made dynamic through real-time intent detection. The bot's menu options and behavior adapt automatically based on the detected user intent from the conversation context, allowing the system to transition from static pre-configured bots to dynamic intent-responsive bots that adjust their functionality during interactions.

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If messaging bot options are dynamically configured based on user intent, then personalization is improved, but system complexity increases

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidbot control system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces manual configuration mechanisms with automated machine learning-based intent detection. Instead of requiring users to manually configure bot options or developers to create multiple static bot versions, the system uses natural language processing and machine learning algorithms to automatically detect user intent and configure appropriate bot responses, substituting mechanical configuration with intelligent automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning intent detection component serves as an intermediary between the user's natural language messages and the bot's configuration system. This intermediary layer automatically translates user intent into appropriate bot menu configurations, shielding the complexity of bot control from both users and developers while enabling personalized interactions.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If machine-learning intent detection is implemented, then user experience is improved, but processing requirements increase

Engineering Contradiction:

Improveuser experience qualityVSAvoidcomputational processing energy

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing machine learning processing only on the necessary portions of messaging interactions. Instead of analyzing all possible aspects of every message, the intent detection system processes only the relevant linguistic features and context needed to determine user intent, reducing computational overhead while maintaining accurate personalization.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11552910B1Techniques for messaging bot controls based on machine-learning user intent detection

Publication Date: 2023.01.10 META PLATFORMS INC

AI summary

Techniques for messaging bot controls based on machine-learning user intent detection are described. In one embodiment, an apparatus may comprise a message queue monitoring component operative to monitor a messaging interaction, the messaging interaction exchanged via a messaging system, the messaging interaction involving at least one client device; an interaction processing component operative to determine a user intent for the messaging interaction; and a bot management component operative to determine a messaging bot options configuration for the client device based on the user intent; and send the messaging bot options configuration to the client device. Other embodiments are described and claimed.