ML Playbook System for Enterprise Communication Theme Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprises face challenges in collecting and processing insights and themes from diverse communication channels like email, instant messages, and voice communications due to the resource and time-intensive nature of these processes.
Innovation Solution
A system utilizing two machine learning models to identify characterizations in communications, generating playbooks with scoring strategies and actions, and assigning incoming communications to themes, enabling efficient processing and response management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to collect and process communications from diverse channels, then comprehensive analysis of all communications is achieved, but the process becomes very resource and time intensive
Solution Approach 1:
The patent replaces manual mechanical processing of communications with automated machine learning models. The first ML model automatically identifies characterizations and themes across multiple communication channels (email, instant messaging, voice), while the second ML model scores communications against playbook criteria. This substitution eliminates the need for manual review of each communication, dramatically improving processing efficiency and reducing time requirements while maintaining comprehensive analysis capability.
2Productivity
If machine learning models are used to automatically identify themes and generate playbooks, then processing time is reduced, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex playbook generation process into distinct functional components: a first machine learning model for theme identification and characterization, a second machine learning model for scoring communications, and a playbook generation module that synthesizes results. This segmentation allows each component to specialize in a specific task, making the overall system more manageable and interpretable despite the automation complexity.
Solution Approach 2:
The patent introduces playbooks as intermediary artifacts that bridge the gap between automated theme identification and actionable responses. Playbooks contain pre-defined scoring criteria, thresholds, and response templates that mediate between the ML model outputs and final decision-making. This intermediary layer simplifies the system by providing structured rules that translate complex ML predictions into actionable business logic.
3Measurement precision
If playbooks are generated with detailed scoring strategies and actions, then response accuracy is improved, but the complexity of playbook management increases
Solution Approach 1:
The patent implements dynamic playbooks where scoring thresholds, criteria weights, and response actions can be adjusted based on performance feedback and changing business requirements. The system learns from actual communication patterns and playbook execution results, allowing organizations to refine scoring strategies over time without rebuilding entire playbooks. This dynamic capability maintains high scoring accuracy while reducing management complexity through adaptive optimization.
Solution Approach 2:
The patent incorporates feedback loops where the results of playbook execution and communication scoring are fed back into the system to continuously improve future scoring accuracy. The machine learning models are retrained on new data, and playbook performance metrics are analyzed to adjust scoring criteria and thresholds. This feedback mechanism ensures that increased playbook detail translates into improved accuracy rather than just increased complexity.
Data Source
AI summary
Methods and systems are disclosed herein for using machine learning models to identify insights and themes in communications of an enterprise and assign incoming communications to each theme or insight. One mechanism for identifying insights and themes in communications of an enterprise involves using two different machine learning models. The first machine learning model may identify characterizations (e.g., topics) associated with communications received by the enterprise and provides the different characterizations to a user. The system then receives groupings of those characterization to generate one or more playbooks to be used by the enterprise. The playbooks may be updated with scoring strategies and actions to aid the enterprise with addressing the themes and insights.