Video-Based POS System for Automated Service Billing
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Solution Overview
Problem
Current point of sale systems for personal services require merchants to interrupt their service to manually enter service details, leading to inefficiencies in tracking and billing.
Innovation Solution
A system utilizing video cameras and additional sensors to automatically analyze personal service sessions through convolutional neural networks, generating electronic invoices by matching sequences of frames to service types and durations, and comparing them to pre-programmed service packages for accurate transaction valuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual entry of service details is used in current POS systems, then transaction accuracy can be maintained through direct input, but service efficiency deteriorates due to interruptions in service delivery
Solution Approach 1:
The system enables automatic service tracking where the POS system itself monitors and records service details without requiring merchant intervention. Video cameras and sensors automatically detect service activities, performances, and durations, eliminating the need for manual data entry while maintaining accurate transaction records.
Solution Approach 2:
The patent replaces manual mechanical data entry with automated electronic monitoring systems. Video cameras, motion sensors, and audio sensors substitute for manual input methods, automatically capturing service information and transmitting it to the POS system for processing.
2Measurement precision
If video cameras and sensors are deployed for automatic service tracking, then service monitoring accuracy is improved, but system complexity increases
Solution Approach 1:
The POS system is designed as a multi-functional platform that integrates video surveillance, sensor data acquisition, artificial intelligence processing, and transaction management. This universal system performs multiple functions including service detection, performance monitoring, duration tracking, and automatic billing, reducing the need for separate dedicated devices for each function.
Solution Approach 2:
The patent introduces an AI processing layer that acts as an intermediary between the raw sensor/video data and the POS system. This intermediate layer processes and interprets data from multiple sensors, converts it into structured service information, and presents it to the POS system, thereby simplifying the overall system architecture and data flow.
3Extent of automation
If continuous video monitoring is implemented, then automatic service detection is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic sampling of video and sensor data at strategically determined intervals. The AI processor analyzes data at specific time points and triggers service detection events based on predetermined criteria, reducing overall energy consumption while maintaining effective service monitoring capability.
Solution Approach 2:
The system pre-configures service detection criteria, performance thresholds, and monitoring parameters before operation begins. This preliminary setup allows the system to efficiently process data with pre-programmed decision logic, reducing computational energy requirements during actual service monitoring and enabling faster processing with lower power consumption.
Data Source
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AI summary
A method for automatically analyzing a personal service session and completing a transaction therefor includes monitoring a service location with a video camera in communication with a processor, determining that the personal service session has begun based on an identification of at least one start trigger event, analyzing an output from the video camera including a plurality of frames of video data collected during the personal service session to identify a sequence of frames of the video data, automatically matching the sequence of frames to at least one type of service that was provided during the personal service session, determining that the personal service session has ended based on an identification of at least one end trigger event, determining a transaction value associated with the types of service that have been provided during the personal service session, and generating an electronic invoice based on the determined transaction value.