Automated Time Recording Using ML-Based Multi-Employee Recognition
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Solution Overview
Problem
Existing time recording systems are inefficient when multiple employees attempt to clock in or out simultaneously, causing technical inefficiencies due to limitations in processing a volume of employees at once.
Innovation Solution
Implementing machine learning techniques to identify and record time for multiple employees using a scene capturing device, which includes extracting features from images to recognize employee identities and time recording actions, and executing automated electronic time recording events based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional time clocks are used to register time for employees, then the system is simple and easy to operate, but it can only process one employee at a time causing long wait times and low productivity
Solution Approach 1:
The patent replaces the mechanical/electronic time clock system with a machine learning-based automated recognition system. The system uses image capturing devices to capture images of employees performing time recording actions, and machine learning models to automatically recognize and identify these actions, eliminating the need for physical time clocks and manual punching/swiping operations.
Solution Approach 2:
The system enables employees to perform time recording actions automatically without requiring interaction with physical time clock devices. The automated recognition system independently identifies and processes time recording events, allowing multiple employees to be processed simultaneously as they naturally perform their time recording actions.
2Loss of time
If traditional time clocks process employees sequentially, then the system remains simple, but it causes long wait times for employees and reduces productivity during peak periods
Solution Approach 1:
The patent merges multiple time recording processing operations into a single parallel system. Instead of processing employees one at a time through sequential time clock operations, the system captures images of multiple employees simultaneously and processes them in parallel through the machine learning recognition system, thereby reducing individual wait times and increasing overall throughput.
Solution Approach 2:
The system transitions from temporal processing (one employee at a time in sequence) to spatial processing (multiple employees simultaneously in parallel). By capturing images of multiple employees performing time recording actions at the same time and processing them concurrently through the machine learning system, the patent enables parallel processing that reduces wait times and increases productivity.
Data Source
AI summary
A system and method for a machine learning-based automated electronic time recording for personnel includes identifying, via a scene capturing device, a representation of a time recording space; identifying a body having a time recording pose within the time recording space based on an assessment of the representation of the time recording space; extracting a plurality of distinct features from the representation of the time recording space based on identifying the body having the time recording pose; executing automated user-recognition based on the extracting of the plurality of distinct features; executing automated time recording recognition based on the extracting of the plurality of distinct features; and executing automated electronic time recording, via a time recording application based on the automated user-recognition and the automated time recording recognition.