Centralized AI Sorting Network for Material Identification
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Solution Overview
Problem
Existing sorting facilities face challenges in efficiently identifying and harvesting diverse materials due to limited data capture by individual sensors and lack of centralized artificial intelligence, leading to performance and cost bottlenecks.
Innovation Solution
A cloud-based machine learning framework that enables communication across multiple sorting facilities and a cloud sorting server, allowing for the distribution of object recognition, material handling, and sorting across facilities, utilizing mass-market components and standards-based interconnections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized AI system is implemented across multiple sorting facilities, then sorting accuracy and efficiency are improved, but system complexity and communication infrastructure requirements increase
Solution Approach 1:
The patent combines multiple independent sorting facility systems into a unified networked system with a centralized AI server. Individual sorting facilities are merged through common communication networks, allowing shared access to centralized AI models and databases, thereby improving overall sorting efficiency while managing complexity through standardized integration protocols
Solution Approach 2:
The centralized AI system serves multiple sorting facilities simultaneously, providing universal material identification and sorting capabilities across the entire network. The AI server performs multiple functions including image processing, material classification, and coordination of harvesting operations across different facilities, reducing the need for duplicate systems at each location
2Measurement precision
If facilities operate independently with local sensors only, then system simplicity is maintained, but sorting accuracy and material identification capability are limited
Solution Approach 1:
The centralized AI server acts as an intermediary between local sensors at sorting facilities and the final sorting decisions. Local sensors capture image data which is transmitted to the centralized AI server for advanced processing and analysis, then results are returned to control local harvesting operations. This intermediary architecture enables enhanced accuracy without requiring complex local systems at each facility
Solution Approach 2:
The system transitions from two-dimensional local sensor data processing to multi-dimensional centralized AI processing that incorporates data from multiple facilities, multiple sensor types, and historical material information. This dimensional expansion enables more accurate material identification by analyzing patterns across the entire network rather than isolated local data
3Adaptability or versatility
If centralized AI coordination is implemented across facilities, then harvesting coordination and resource utilization are improved, but communication requirements and operational complexity increase
Solution Approach 1:
The centralized AI system performs preliminary analysis of sensor data to identify target materials and coordinate harvesting operations before materials reach collection points. By pre-processing and pre-coordinating sorting decisions, the system reduces real-time communication requirements and enables smoother harvesting operations across facilities without requiring constant information exchange
Data Source
AI summary
Maintaining a data structure corresponding to a target object is disclosed, including: determining that an identified target object from a sensed signal is a new target object, wherein the sensed signal is generated at a sorting facility; generating a new data structure corresponding to the new target object; and updating the new data structure with an attribute associated with the new target object, wherein the attribute is derived from the sensed signal associated with the new target object.