Intent Stage Segmentation for Messaging Automation
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Solution Overview
Problem
Existing messaging systems face challenges in identifying and categorizing user intents within conversational text to provide relevant content items, as current computer-implemented techniques are inefficient in segmenting users based on their expressed interests and actions.
Innovation Solution
A messaging server establishes categories and stages for intents, expands the vocabulary of stages using machine learning models, and associates conversational text with intent groups to deliver targeted suggestions based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If computer-implemented techniques are used to identify content items, then automation is improved, but measurement precision of user intent is insufficient
Solution Approach 1:
The patent segments user intent recognition into multiple discrete stages (awareness, interest, evaluation, intent, action) with specific criteria for each stage. This segmentation allows the system to automatically classify user messages into precise intent categories, resolving the contradiction by making automated intent measurement as accurate as manual analysis through defined stage boundaries and transition rules.
Solution Approach 2:
The patent changes the parameter of intent measurement from binary (intent present/absent) to multi-stage (5 distinct stages with progression criteria). By introducing stage progression parameters and transition rules, the system achieves both automation and precision in measuring user intent strength and development over time.
2Ease of operation
If simple intent categorization is used, then ease of operation is improved, but adaptability to different user needs is insufficient
Solution Approach 1:
The patent implements dynamic intent staging where user messages can transition between 5 different stages based on content analysis. The system dynamically adjusts intent classification by evaluating message content against stage-specific criteria and tracking progression through the funnel, providing both operational simplicity through automated rules and adaptability through flexible stage transitions.
Solution Approach 2:
The patent adds a temporal and progressive dimension to intent categorization by introducing stage progression. Instead of static categories, the system evaluates user intent across multiple dimensions (stage level, progression direction, transition criteria), enabling simple operation through rule-based classification while achieving high adaptability through multi-dimensional intent assessment.
Data Source
AI summary
A messaging server provides conversational text subsets to a machine-learned model that analyzes the text subsets to identify intents expressed therein. The messaging server determines intent groups associated with the text subsets based on the expressed intents. An intent group describes a category representing a subject area in which a text subset may express intent, and also describes a stage of the category representing a strength of the expressed intent. The messaging server applies decay factors to the intent groups. The decay factors include decay rates that describe how long the types of intents represented by the intent groups are maintained. The messaging server has access to suggestions having associated targeting criteria including intent groups to which the suggestions are targeted. The message server uses the targeting criteria to select suggestions targeted to users associated with particular text subsets, and delivers the selected suggestions to the users.