AI Conversation Summarization for Consistent CRM Summary Notes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tools and systems for exploring large collections of digitally recorded conversations lack functionality in promoting insightful discovery of common goals, patterns, and results, and require significant agent time and resources for generating inconsistent summaries.
Innovation Solution
Automated systems and methods using artificial intelligence and machine learning to generate summaries of digitally recorded conversations, which can be stored in CRM systems, saving agent time and providing consistent notes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual summarization by agents is used, then summaries can be generated, but it requires significant agent time and results in inconsistent summaries
Solution Approach 1:
The system enables automatic summarization where the conversation data generates its own summary through AI processing, eliminating the need for manual agent intervention. The machine learning model autonomously extracts key information and generates consistent summaries, allowing the system to serve itself rather than relying on human agents for each summarization task.
Solution Approach 2:
The patent replaces the mechanical process of manual summarization by agents with an automated AI-based system. The machine learning model processes conversation data and generates summaries algorithmically, substituting human cognitive work with computational processes that provide both time efficiency and consistency through standardized processing procedures.
2Productivity
If manual summarization is used, then summaries are generated, but higher-level analysis is limited due to inconsistency
Solution Approach 1:
The system replaces manual summarization with automated AI processing, where machine learning models consistently extract and structure information from conversations. This substitution ensures reliable, standardized outputs that can be systematically analyzed at higher levels, improving both operational efficiency and the reliability of subsequent analysis through consistent data quality.
Solution Approach 2:
The system implements feedback mechanisms where the AI model continuously learns from processed conversations, refining its summarization capabilities. This feedback loop ensures that summaries maintain high reliability and consistency over time, enabling robust higher-level analysis as the system adapts and improves based on accumulated data patterns.
3Productivity
If automated summarization is implemented, then agent time is saved and consistency is improved, but system complexity increases
Solution Approach 1:
The AI-based summarization system is designed as a universal platform that can process multiple types of conversation data across different domains and contexts. By creating a multi-functional system that handles various conversation formats and styles through a single automated framework, the patent achieves high productivity while managing complexity through consolidation rather than proliferation of separate systems.
Data Source
AI summary
A computer-implemented method and computer system improvements for automatically generating a summary note related to a digitally-recorded interlocutor conversation, such as a chat transcript, including accessing a data corpus having at least one digitally-recorded conversation of text-based interlocutory conversation(s) including at least one labeled value, extracting at least one Conversation Feature from the labeled value(s), creating at least one Summary Feature from the extracted Conversation Feature(s), generating at least one Summary Note by combining one or more Narrative Structures with the Summary Feature(s), and digitally outputting the at least one Summary Note.