Intelligent Messaging Delivery Timing
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Solution Overview
Problem
Existing message delivery systems lack the ability to automatically manage delivery based on a user's dynamic availability and receptivity, often sending messages at inconvenient times and requiring users to actively engage and disengage 'do not disturb' features.
Innovation Solution
An intelligent messaging system that uses user profile data and real-time monitoring of availability and receptivity, employing AI and machine learning to determine the optimal time for message delivery, allowing for delayed delivery and batch processing of messages, and providing senders with feedback and alternative communication options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If messages are delivered immediately upon receipt, then message delivery speed is improved, but message delivery timing appropriateness deteriorates
Solution Approach 1:
The system performs preliminary analysis of user status data (availability, receptivity, context) before delivering messages. It predicts future user states and determines optimal delivery timing in advance, rather than delivering immediately or waiting passively for user initiation.
Solution Approach 2:
The intelligent messaging system acts as an intermediary between the message sender and recipient, analyzing user status data and making autonomous decisions about message delivery timing. This mediator resolves the contradiction by decoupling message receipt from message delivery, using user context information to determine appropriate delivery moments.
2Ease of operation
If basic 'do not disturb' features are used to control message delivery, then user control over message delivery is improved, but system complexity and user burden deteriorates
Solution Approach 1:
The system automatically monitors user status data (calendar events, location, device usage patterns, biometric data) and autonomously determines optimal message delivery timing without requiring user intervention. The system serves itself by making intelligent delivery decisions based on inferred user context, eliminating the need for users to manually configure and manage delivery rules.
Solution Approach 2:
The system dynamically changes message delivery parameters (timing, channel, batching) based on real-time analysis of user status data. Instead of fixed user-configured rules, the system continuously adapts delivery behavior by processing multiple parameters including user availability, receptivity scores, message priority, and contextual information.
3Device complexity
If messages are delivered without considering user availability and receptivity, then message delivery simplicity is improved, but message effectiveness deteriorates
Solution Approach 1:
The system continuously monitors user responses to delivered messages and uses this feedback to refine future delivery decisions. By analyzing whether users engage with, ignore, or mark messages as unwanted, the system learns and adapts its delivery timing and channel selection to improve effectiveness while maintaining automated operation.
Solution Approach 2:
The system replaces simple mechanical message delivery (immediate transmission upon receipt) with an intelligent system that processes user status data, predicts future states, and makes autonomous delivery decisions. This substitution enables consideration of user availability and receptivity without significantly increasing perceived complexity for the end user.
Data Source
AI summary
The disclosed technology is directed towards delivering electronic messages from senders to recipients in an intelligent way, based on the determined likelihood of each recipient acting on a message. Message delivery can be delayed upon delayed message delivery criterion being satisfied, based on user profile data the user establishes for each messaging application. Predicted recipient user availably, predicted recipient user receptivity and/or the identity of the sender, for example, can be used as factors in determining delivery data representing the likelihood of a recipient acting on a message. Delivery of the message is to a recipient's device is delayed when the delivery data satisfies delayed delivery criterion. The delayed delivery time can be determined from the recipient user's predicted availably and/or receptivity data, and/or the identity of the sender. The sender is notified of a delayed message delivery, and can be presented with options as to other actions to take.