Machine Vision System for Enterprise Activity Correlation
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Solution Overview
Problem
Current machine vision technologies have advanced significantly but lack integration with enterprise networks and communication infrastructure, limiting their application in providing timely and valuable information for commercial and municipal enterprises, particularly in managing human activities and interactions.
Innovation Solution
A system design that combines machine vision algorithms with sensor data and key activity identifiers to generate messages for real-time and delayed notification, enabling automated or semi-automated decision-making by correlating past and present sensor data, and applying decision logic for statistical significance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine vision algorithms are integrated with enterprise networks and communication infrastructure, then timely and valuable information can be provided for managing human activities, but system complexity increases
Solution Approach 1:
The system segments the machine vision pipeline into distinct functional modules: sensor data collection, event detection, activity identification, decision logic evaluation, and notification delivery. Each module operates independently and can be integrated with enterprise networks at appropriate granularity points, reducing overall integration complexity while maintaining timely information flow.
Solution Approach 2:
The patent introduces an intermediary processing layer that sits between raw sensor data and enterprise network systems. This intermediary layer pre-processes and filters events locally before transmitting to the enterprise network, reducing the complexity of direct integration while ensuring timely and valuable information is delivered.
2Reliability
If continuous sensor data analysis is performed to detect statistically significant events, then situational awareness is improved, but energy consumption and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-defining decision logic rules and statistical significance thresholds before actual event detection occurs. This allows the system to efficiently filter and prioritize sensor data without requiring continuous heavy processing, reducing energy consumption while maintaining reliable situational awareness through pre-configured detection criteria.
Solution Approach 2:
Instead of continuous analysis, the system employs periodic sampling and threshold-based detection. Sensor data is analyzed at predetermined intervals or when specific triggers occur, rather than continuously processed, reducing energy consumption while maintaining adequate situational awareness through strategic monitoring points.
3Productivity
If machine vision technology is applied to automated enterprise decision-making, then management efficiency is enhanced, but the difficulty of detecting and measuring human activities increases
Solution Approach 1:
The system applies local quality by tailoring detection algorithms and decision logic to specific human activities and contexts rather than using generic approaches. This allows the system to handle the complexity of human activity detection in localized, manageable ways, improving management efficiency while addressing the inherent difficulties of measuring human behavior through context-specific models.
Solution Approach 2:
The patent implements feedback mechanisms where detected human activities and their outcomes are fed back into the decision logic system. This allows the system to learn from and adapt to complex human patterns over time, improving its ability to detect and measure human activities accurately while enhancing management efficiency through continuous optimization.
Data Source
AI summary
A system for use in managing activity of interest within an enterprise is provided. The system comprises a computer configured to (i) receive sensor data that is related to key activity to the enterprise, such key activity comprising a type of object and the object's activity at a predetermined location associated with the enterprise, the sensor providing information from which an object's type and activity at the predetermined location can be derived, (ii) process the sensor data to produce output that is related to key activity to the enterprise, and (ii) store the information extracted from the processed data in a suitable manner for knowledge extraction and future analysis. According to a preferred embodiment, the object is human, machine or vehicular, and the computer is further configured to correlate sensor data to key activity to the enterprise and the output includes feedback data based on the correlation.