ML Time Record Prediction for Clock-in Automation
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Solution Overview
Problem
Current timeclock systems require employees to manually clock in and out, which is a repetitive, time-consuming task that burdens employees and infrastructure during peak hours, leading to inefficiencies and system performance issues.
Innovation Solution
A computer-implemented method using machine learning to collect and model time record events and geolocation data, predicting suggested clock-in and clock-out times, and sending notifications to employees for approval, thereby automating the process and reducing manual interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual clock in and clock out operations are implemented, then employees can track their work hours accurately, but employees spend excessive time on repetitive tasks and system infrastructure is heavily impacted during peak hours
Solution Approach 1:
The system uses machine learning models to automatically detect and record clock in and clock out events based on employee behavior patterns and geolocation data, eliminating the need for employees to manually initiate these operations. The system serves itself by autonomously processing time record events without human intervention for routine operations.
Solution Approach 2:
The patent replaces the mechanical manual interaction system with an automated machine learning-based system that uses sensors, geolocation data, and predictive models to detect and process time record events automatically, substituting human manual operations with intelligent automated processing.
2Ease of operation
If manual clock in and clock out operations are implemented, then employees can control their time recording, but the system infrastructure experiences significant impact during peak hours due to high volume operations
Solution Approach 1:
The system autonomously processes time record events by automatically detecting clock in and clock out moments through machine learning models that analyze employee behavior patterns, geolocation changes, and historical data, eliminating the need for employees to manually submit time records and reducing system load from high-volume manual operations.
Solution Approach 2:
The system performs preliminary analysis of employee behavior patterns and historical time record data to predict upcoming clock in and clock out events, preparing and processing these events before they occur in real-time, thereby distributing system load over time rather than concentrating it during peak hours.
3Productivity
If automated machine learning prediction is implemented, then employee time is saved and infrastructure impact is reduced, but the system complexity increases
Solution Approach 1:
The machine learning system performs multiple functions using a unified approach: it detects clock in events, detects clock out events, predicts timing patterns, validates geolocation data, and generates time record events, all through a single multi-functional predictive processing framework that reduces overall system complexity compared to separate specialized systems.
Data Source
AI summary
A method, computer system, and computer program product are provided for managing time record events. Time record events are collected for a number of users. Each time record event includes a geolocation of one of a number of users. The time record events and geolocations for each of the number of users are models via machine learning. A current geolocation for a given user is identified. A suggested event is predicted based on the current geolocation and a current time. The suggested event is pushed to the user.