Social Post Timing Using Follower Viewing Activity

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Solution Overview

Problem

Existing systems for determining optimal post times on social networking sites rely on erroneous assumptions about user and follower posting patterns, failing to account for individual differences and influence, leading to suboptimal message visibility.

Innovation Solution

A system that uses intelligent algorithms to aggregate follower posting patterns, weighted by influence scores, to suggest ideal post times based on user-defined metrics, smoothing algorithms, and activity models to maximize message impact.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If post times are suggested based on when followers direct posts or rebroadcast posts, then the system captures posting patterns, but it captures the individual's posting pattern rather than the followers' viewing pattern

Engineering Contradiction:

Improveaccuracy of post time suggestionVSAvoidmisinterpretation of follower behavior

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent inverts the conventional approach by not using when followers post as the basis for suggestions, but rather using when followers are most active and likely to view content. This inversion corrects the fundamental misconception in existing systems that equate posting behavior with viewing behavior, thereby improving the accuracy of post time suggestions.

Inventive Principle:

Principle #13The other way round (Inversion)

2Device complexity

If all individuals are assumed to follow similar usage patterns, then the system simplifies data processing, but it ignores significant differences in posting patterns of different groups

Engineering Contradiction:

Improvesimplicity of analysis systemVSAvoidaccuracy of post time suggestion

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent segments the follower base into different groups or individuals and analyzes their activity patterns separately rather than treating all followers uniformly. This segmentation allows the system to capture significant differences in posting patterns of different groups while maintaining manageable complexity through automated analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by tailoring post time suggestions to specific follower groups or individuals based on their unique activity patterns, rather than applying a universal posting strategy. This allows the system to account for significant differences in posting patterns across different groups while maintaining overall system simplicity.

Inventive Principle:

Principle #3Local quality

3Quantity of substance

If messages are broadcast to anyone on the social networking site, then the reach is maximized, but the probability of an individual seeing the message decreases due to rapidly growing feeds

Engineering Contradiction:

Improveaudience reachVSAvoidmessage visibility

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The patent applies preliminary action by determining and scheduling posts for optimal times in advance, based on analyzed follower activity patterns. This ensures messages are posted when followers are most likely to be online and viewing content, thereby increasing the probability of visibility while maintaining broad reach through strategic timing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12380119B1System and methods for generating optimal post times for social networking sites

Publication Date: 2025.08.05 SPROUT SOCIAL

AI summary

A system and methods are disclosed for determining the ideal times for a person, software client, or other entity to post a message to a social networking site. An ideal time is a time when the post will have a maximum impact, where impact is some measure of success as defined by the posting entity. To determine the ideal times, the posting patterns of individual users are aggregated in a weighted fashion, taking into consideration both the entity's desired impact and the likelihood that the user will be online and able to view the post within a specified time frame.