Time Zone Prediction via Communication Data Analysis

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Solution Overview

Problem

Conventional methods for identifying time zones of computer users are resource-intensive and difficult to implement, often requiring geographic positioning techniques and hardware sensors, which can be challenging for global digital marketing campaigns that need to schedule optimal electronic message deliveries.

Innovation Solution

A time-zone estimation model is trained using electronic communication data, such as message delivery and response data, to predict time zones without requiring hardware or sensor-related data, utilizing a combination of collaborative-based and individual-based models to generate probability scores for unknown time zones.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If geographic positioning techniques and hardware sensors are used to identify time zones, then time zone identification accuracy is improved, but computational resource consumption and system complexity increase

Engineering Contradiction:

Improvetime zone identification accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces hardware-based geographic positioning systems and sensor mechanisms with a software-based machine learning model that processes electronic communication data. This substitution eliminates the need for complex hardware infrastructure while achieving accurate time zone prediction through algorithms that analyze message delivery timestamps, read receipts, and user interaction patterns.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

Instead of directly measuring geographic location through hardware sensors, the system creates a virtual representation of user location by analyzing behavioral patterns in communication data. The machine learning model generates predicted time zone information as a copy of actual geographic data, derived indirectly from user interaction patterns rather than direct physical measurement.

Inventive Principle:

Principle #26Copying

2Measurement precision

If geographic positioning techniques are implemented to determine time zones, then time zone data accuracy is improved, but computational resource requirements increase

Engineering Contradiction:

Improvetime zone data accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system leverages existing electronic communication data that is already being collected and processed for other purposes. By mining this existing data for time zone information, the system avoids the need for separate data collection infrastructure and reduces overall computational overhead. The machine learning model processes data that would otherwise be discarded or stored anyway.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent transforms the approach from direct geographic parameter measurement (latitude, longitude) to behavioral parameter analysis (message delivery times, read patterns, interaction frequency). This parameter transformation enables time zone determination through software-based pattern recognition rather than hardware-based geographic measurement, reducing computational resource requirements.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If hardware sensors and geographic positioning are used for time zone identification, then location accuracy is improved, but ease of implementation decreases

Engineering Contradiction:

Improvelocation accuracyVSAvoidease of implementation

Core Design Contradiction:

Measurement precisionVSEase of manufacture

Solution Approach 1:

The machine learning model serves multiple functions: it predicts time zones, analyzes user engagement patterns, and optimizes message delivery timing. By consolidating these functions into a single software-based system, the patent eliminates the need for separate hardware components and simplifies implementation compared to dedicated geographic positioning systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent replaces complex hardware-based geographic positioning systems with a software-based machine learning approach that processes existing electronic communication data. This substitution significantly simplifies implementation by eliminating hardware installation, calibration, and maintenance requirements while maintaining accurate time zone identification.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20240323156A1Facilitating time zone prediction based on electronic communication data

Publication Date: 2024.09.26 ADOBE INC

AI summary

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.