Autocomplete Engine for Multi-Intent Email Extraction

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional technologies struggle with automating email and ticket extraction, as they fail to effectively understand natural language, particularly when dealing with multiple intents and entities in a single communication.

Innovation Solution

The implementation of an autocomplete prediction engine that processes communications to extract sets of intents and entities, using a language model to provide conversational or natural language understanding, thereby enabling automated communication mining.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional slot filling technology is used to extract intents and entities from emails, then the system can identify single-purpose emails, but it fails when emails contain multiple purposes and cannot determine relationships between purposes and portions

Engineering Contradiction:

Improveintent extraction accuracyVSAvoidhandling multiple intents

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent segments the email processing task into distinct components: intent detection identifies multiple purposes separately, and entity extraction identifies multiple entities for each intent. This segmentation allows the system to handle emails with multiple intents and entities independently, then combine them systematically, resolving the limitation of conventional slot filling that assumes single-purpose emails.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If extensive user input is required for email extraction, then the system can achieve accurate results, but automation capability is significantly reduced

Engineering Contradiction:

Improveextraction accuracyVSAvoiduser input requirement

Core Design Contradiction:

Measurement precisionVSExtent of automation

Solution Approach 1:

The system employs self-service through automated intent detection and entity extraction algorithms that process emails without extensive user input. The model autonomously identifies multiple intents, extracts corresponding entities, and determines relationships between them, achieving both high automation and accurate extraction results simultaneously.

Inventive Principle:

Principle #25Self-service

3Difficulty of detecting and measuring

If intensive processing power is allocated to natural language understanding, then the system can comprehend complex communications, but computational efficiency decreases

Engineering Contradiction:

Improvenatural language understanding capabilityVSAvoidprocessor cycles

Core Design Contradiction:

Difficulty of detecting and measuringVSUse of energy by moving object

Solution Approach 1:

The patent applies preliminary action by pre-training a language model on extensive communication data before deployment. This pre-training enables the model to comprehend complex natural language patterns efficiently during actual email processing, reducing the computational power required at runtime while maintaining high understanding capability.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250068834A1Autocomplete prediction engine providing automatic form filling from email and ticket extractions

Publication Date: 2025.02.27 UIPATH INC

AI summary

A method is provided. The method is executed by an autocomplete prediction engine implemented as a computer program within a computing environment. The autocomplete prediction engine executes automated communication mining on a communication. The method includes processing the communication to extract intents and entities related to each intent. The method includes providing the intents and the entities into forms using a language model to provide a conversational or natural language understanding of the communication.