Scheduled Message Sending With Criteria-Based Timing Adaptation
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Solution Overview
Problem
Existing electronic messaging systems lack the ability to dynamically adjust the timing, content, or type of scheduled communications based on changing circumstances, leading to unnecessary transmissions or outdated information.
Innovation Solution
Employing a trained large language model to analyze message-related criteria, such as user inboxes, communication channels, and recipient statuses, to automatically modify or suggest changes to scheduled electronic communications, including altering send times, content, or communication type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a message is scheduled for sending at a fixed future time, then the sending time is predetermined and simple to manage, but the message may be sent unnecessarily or with outdated information when circumstances change
Solution Approach 1:
The patent implements dynamic scheduling by allowing the scheduled send time to be automatically adjusted based on changing circumstances. The system monitors criteria such as new messages in threads, recipient availability, and user preferences, then dynamically modifies the send time or cancels the scheduled message if circumstances change, ensuring message relevance without requiring complex manual intervention
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring specified criteria (new messages in thread, recipient status, communication channel state) and using this information to determine whether to adjust or cancel the scheduled send. This feedback loop ensures messages are only sent when appropriate circumstances exist, maintaining reliability while managing complexity through automated decision-making
2Loss of time
If the system automatically adjusts scheduled send times based on changing circumstances, then message timeliness is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring the criteria that will trigger send time adjustments before the scheduled send occurs. Users set up their preferences and monitoring criteria in advance (e.g., monitor for specific keywords, check recipient availability), allowing the system to automatically respond to circumstances without requiring complex real-time decision-making algorithms
Solution Approach 2:
The system provides self-service functionality by automatically monitoring criteria and adjusting scheduled sends without requiring user intervention. The system independently evaluates whether circumstances have changed based on pre-configured criteria and automatically modifies or cancels sends, reducing the need for complex user-system interaction while maintaining timing accuracy
3Measurement precision
If the system monitors multiple criteria such as inbox state, communication channel state, and user status, then the accuracy of send timing determination is improved, but the computational resources required increase
Solution Approach 1:
The system applies local quality by selectively monitoring only the specific criteria relevant to each scheduled message and user preferences. Rather than uniformly monitoring all possible criteria for all messages, the system tailors monitoring to individual needs (e.g., only monitoring thread messages for important communications, only checking status for specific recipients), improving detection accuracy while reducing overall computational load
Solution Approach 2:
The system manages computational resources by dynamically adjusting the depth and frequency of criteria monitoring based on message priority and user-defined parameters. High-priority messages may trigger more comprehensive monitoring, while routine messages use lighter monitoring, allowing the system to maintain precision for critical communications while conserving processing energy for less important sends
Data Source
AI summary
The technology provides mechanisms to set criteria for when a scheduled send of a communication, such as an email, text message, chat message, etc., should occur. The criteria can be based on the state of a user's message inbox, their communication channels, and/or the status or actions of others to be communicated with. Machine learning can be used to either generate the criteria for when or how to respond, or to generate a recommended response. The system can reformat a communication by changing the tone of a message, or rework the communication for transmission in an alternative format/medium via a different messaging app. This can involve changing from an email message to a chat or audio message, replacing text with emoji or the like, adding visual indicia to the communication, and/or adding audible information to the communication.