Cognitive Message Action Recommendation in Multimodal Messaging
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Solution Overview
Problem
Users face challenges in managing and responding to a high volume of messages from various sources, making it difficult to track urgency and relevance, leading to delayed or inappropriate responses.
Innovation Solution
A computer-implemented method and system that analyzes prior messages to generate a list of recommended actions for new messages based on attributes such as sender, location, and time of receipt, using machine learning to emulate user responses and prioritize workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually review and respond to all messages, then response accuracy is improved, but time consumption and workload increase significantly
Solution Approach 1:
The patent introduces an automated message analysis system that acts as an intermediary between incoming messages and user responses. The system extracts attributes from messages, compares them with historical data, and generates recommended actions, thereby reducing the time users spend on manual message processing while maintaining response quality through AI-assisted recommendations.
Solution Approach 2:
The system enables self-service by automatically analyzing message attributes, identifying patterns from historical communications, and generating recommended responses without requiring manual user intervention for each message. The user only needs to review and select from pre-generated recommendations, significantly reducing time consumption while maintaining accuracy.
2Reliability
If users prioritize messages based on manual assessment, then response appropriateness is improved, but productivity decreases due to high cognitive load
Solution Approach 1:
The patent replaces the mechanical cognitive process of manual message prioritization with an automated computational system. The system uses attribute extraction, historical data comparison, and machine learning algorithms to automatically assess message urgency and generate prioritized action recommendations, thereby increasing productivity while maintaining appropriateness through data-driven insights.
Solution Approach 2:
The system changes the parameters of message assessment by automatically extracting and analyzing multiple message attributes (sender, timing, content keywords, historical interaction patterns) rather than relying on manual subjective judgment. This enables rapid automated prioritization that maintains appropriateness through comprehensive parameter analysis while significantly increasing processing speed.
3Measurement precision
If the system analyzes all message attributes in detail, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the message analysis process into distinct modules: attribute extraction, historical data retrieval, pattern matching, and recommendation generation. Each module handles specific aspects of analysis independently, allowing the system to achieve high recommendation accuracy through comprehensive attribute analysis while managing complexity through modular architecture and clear separation of concerns.
Data Source
AI summary
Technical solutions are described for action recommendation in a multimodal messaging system. An example method includes accessing a prior message received by a user. The method also includes identifying a first set of attributes associated with the prior message. The method also includes identifying a prior action selected by the user in response to the prior message. The method also includes receiving a new message directed for the user. The method also includes identifying a second set of attributes associated with the new message. The method also includes generating a list of recommended actions in response to the new message based on a comparison of the first set of attributes and the second set of attributes. The method also includes presenting the list of recommended actions to the user.