AI Object Sorting Using Multi-Property Identity Detection
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Solution Overview
Problem
Existing methods for identifying and sorting objects face challenges due to the large variety and variance of object properties, which are influenced by production variations, mechanical stress, and analysis-related factors, requiring extensive measurement campaigns and expert evaluation to determine object identities.
Innovation Solution
An AI-based system and method that utilizes detection modules to analyze object properties, including fluorescent codes, XRF codes, magnetic codes, and native properties, to calculate object identities, and sort objects using AI algorithms, with a learning phase that autonomously establishes correlations between object properties and identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive measurement campaigns and expert evaluation are used to determine object identities, then measurement precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary action by automatically collecting and storing reference measurement data for various object types during an initialization phase before actual sorting operations begin. This pre-collected data serves as the basis for rapid AI-based identification without requiring expert evaluation during operational phases.
Solution Approach 2:
The patent replaces the mechanical system of expert evaluation and manual measurement campaigns with an automated AI-based identification system. The AI algorithm processes measurement data automatically, substituting human experts and manual procedures with computational algorithms that rapidly determine object identities based on learned patterns from reference data.
2Measurement precision
If multiple detection modules analyzing various object properties are used, then measurement precision and object distinguishability are improved, but device complexity increases
Solution Approach 1:
The AI-based identification system serves as a universal processing platform that handles data from multiple different detection modules. Rather than requiring separate processing systems for each detection module, the single AI system can analyze various object properties (optical, magnetic, electrical, mechanical) through a unified algorithmic approach, reducing overall system complexity.
Solution Approach 2:
The patent merges the functions of multiple detection modules into a coordinated system where their outputs are integrated by the AI algorithm. The detection modules work together as a unified measurement system, with the AI system combining their respective data streams to form a comprehensive object characterization that exceeds the capability of any single module.
3Measurement precision
If reference properties for a large variety of objects are stored in a database, then object identity determination accuracy is improved, but storage requirements and teaching time increase
Solution Approach 1:
The AI algorithm transforms the approach to storing reference data by learning patterns and relationships from measurement data rather than storing complete reference profiles for every possible object variant. The system changes parameters from storing exhaustive reference libraries to storing compressed representations of object characteristics that the AI can interpret and match against new objects efficiently.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and accurate identification and sorting of objects by reducing the need for human intervention in teaching the system, allowing for a more reliable determination of object identities and improved material recycling through the use of diverse detection technologies and AI algorithms.
Implementation Method 1
The analyzed object properties can include material properties. Both native and inserted/applied object properties can be analyzed.
Implementation Method 2
The detection system can be used to analyze object properties of the objects to be identified. The analyzed object properties can include material properties.
Implementation Method 3
The detection system having one or more detection modules is used to analyze object properties of the objects to be identified.
Data Source
AI summary
The invention relates to a method for identifying and/or sorting objects, in particular for recycling materials, comprising the steps of: Linking at least one first object type to object identity information via a reference object type property uniquely identifying the first object type; performing at least one learning phase for teaching at least one KI algorithm, the learning phase comprising analyzing at least one object having the reference object type property for an object property; establishing a correlation between the object identity information and the at least one object property, the correlation comprising associating the at least one analyzed object with the first object type; analyzing at least one object for at least one object property; and calculating an object identity of the object to the first object type using the at least one KI algorithm. The invention further relates to a system for identifying and/or sorting objects based on artificial intelligence technologies.