Cross-Channel Conversation Mapping via Semantic Classification
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Solution Overview
Problem
Current digital conversation platforms across different channels, such as SMS, email, and instant messaging, face challenges in efficiently searching and categorizing conversations related to specific transactions due to varying standards and lack of access to underlying data, making it impractical for humans to identify and associate conversations across multiple channels.
Innovation Solution
A system utilizing machine learning models to classify and map conversation segments based on communication channel types, extracting relevant data, and associating it with transaction candidates using attributes like channel type, participants, semantic content, and metadata, allowing for cross-channel conversation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human users manually search for conversation content across multiple channels, then they can identify relevant transactions, but the search process becomes impractical and time-consuming due to the large volume of data and multiple channels
Solution Approach 1:
The patent introduces an automated processing system as an intermediary between the conversation data and human users. This system automatically retrieves, classifies, and presents conversation segments related to transactions, eliminating the need for humans to manually search through multiple channels while maintaining accurate transaction identification.
Solution Approach 2:
The patent replaces the manual mechanical search process with an automated computational system. Machine learning models and automated retrieval mechanisms substitute for human manual searching, dramatically reducing search time while maintaining or improving identification accuracy through systematic processing of conversation data.
2Adaptability or versatility
If conversations are stored separately in different communication channels, then each channel maintains its own standards and format, but cross-channel conversation analysis becomes difficult and requires accessing underlying data from multiple sources
Solution Approach 1:
The patent creates a universal processing system that can handle multiple communication channel formats simultaneously. The automated retrieval and classification system is designed to work with various channel types (email, SMS, chat platforms) through their respective APIs, providing a unified interface for cross-channel analysis without requiring humans to navigate complex data access procedures.
Solution Approach 2:
The patent segments the complex multi-channel data access problem into manageable components: individual channel APIs are accessed separately, each conversation is classified independently, and results are aggregated systematically. This segmentation reduces overall complexity by breaking down the monolithic task of cross-channel searching into discrete, automated steps.
3Ease of manufacture
If key words or phrases are used to search for transaction-related content, then simple matching can be performed, but conversations without explicit key words cannot be identified as related to transactions
Solution Approach 1:
The patent changes the search parameter from simple keyword matching to semantic classification based on transaction context. Machine learning models analyze the meaning and context of conversation segments, allowing identification of transaction-related content even when explicit keywords are absent. This parameter change maintains ease of use while preventing information loss.
Solution Approach 2:
The automated classification system acts as an intermediary that bridges the gap between simple keyword search and complex semantic understanding. It processes conversation content through multiple classification stages, identifying transaction relationships that would be missed by keyword-only approaches while presenting results in a user-friendly format.
Data Source
AI summary
Techniques for extracting data from conversations across different types of communication channels are disclosed. A system applies a set of rules to extract data from conversations based, at least in part, on a type of communication channel used for conducting the conversation. The system applies a machine learning model to recognize semantic content in conversations. The system divides conversations into conversation segments and classifies the conversation segments based on the semantic content. The system selects conversation segments to be extracted based on the semantic content and the type of communication channel over which a conversation is conducted. The system maps conversation segments from different conversations conducted on different types of communication channels to a same set of transactions.