Secure asset tracking system
First Claim
1. A system that tracks assets as they move from one location to another location in order to detect anomalies in the movement and/or handling of an asset comprising in combination:
- a tag attached to each of said assets that stores a code that uniquely identifies each of said assets;
a database in which is stored data for each of said assets to which data said code points;
a computer processor coupled to said data base;
an expert system program coupled to said processor;
said processor accessing said expert system program and said data base to classify said assets into classes with common proprieties;
a tag reader input device that reads said tag attached to an asset as said asset is moved from one location as it is received at another location, said input device reading the time and location of said one location and said another location;
said tag reader input device coupled to said database and storing in said database data parameters including location and time of each location of said assets in accordance with classification to which an asset has been assigned;
said processor accessing said expert system program and said data base to build a predictive model for a class from the stored data parameters for assets of that class;
said processor accessing said expert system program and said predictive model for a class and comparing said data parameters for an asset movement with said predictive model and generating a flag message when said data parameters do not match said predictive model within predetermined range.
1 Assignment
0 Petitions
Accused Products
Abstract
An asset tracking technology wrings from a scan, event, location, and personal data in combination, immense mounds of useful information about the assets by interacting this information with Points of Data via cloud processing and analytics. The system tracks each asset at each location starting at its initial location and thereafter at each succeeding location, including time taken and steps involved as reported by each player in the supply chain. Each asset and each critical embedded component has a tag or mark that uniquely identifies it. Each tag is registered in a cloud-hosted database. Each sending and each receiving location will input the tag information with a scanner. This data flowing from the tracking model is transmitted to the cloud-hosted database for processing using Big Data Analytics techniques and artificial intelligence expert systems tools to determine the probably of a deviation from a normative established by the expert system based on the collected data.
28 Citations
8 Claims
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1. A system that tracks assets as they move from one location to another location in order to detect anomalies in the movement and/or handling of an asset comprising in combination:
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a tag attached to each of said assets that stores a code that uniquely identifies each of said assets; a database in which is stored data for each of said assets to which data said code points; a computer processor coupled to said data base; an expert system program coupled to said processor; said processor accessing said expert system program and said data base to classify said assets into classes with common proprieties; a tag reader input device that reads said tag attached to an asset as said asset is moved from one location as it is received at another location, said input device reading the time and location of said one location and said another location; said tag reader input device coupled to said database and storing in said database data parameters including location and time of each location of said assets in accordance with classification to which an asset has been assigned; said processor accessing said expert system program and said data base to build a predictive model for a class from the stored data parameters for assets of that class; said processor accessing said expert system program and said predictive model for a class and comparing said data parameters for an asset movement with said predictive model and generating a flag message when said data parameters do not match said predictive model within predetermined range. - View Dependent Claims (3, 4, 5)
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2. A method that tracks assets as they move from one location to another location in order to detect anomalies in the movement and/or handling of an asset including the steps of:
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attaching a tag to each of said assets that stores a code that uniquely identifies each of said assets; storing in a database data for each of said assets to which data said code points; coupling a computer processor to said data base; coupling an expert system program to said processor; said processor accessing said expert system program and said data base to classify said assets into classes with common proprieties; reading said tag attached to an asset as said asset is moved from one location to and as said asset is received at another location and also recording the time of reading and location of said one location and the time of reading and location of said another location; storing in said database data parameters including location and time of each said assets at each location in accordance with classification to which an asset has been assigned; accessing said expert system program and said data base to build a predictive model for a class from the stored data parameters for assets of that class; accessing said expert system program and said predictive model for a class and comparing said data parameters for an asset movement with said predictive model and; generating a flag message when said data parameters do not match said predictive model within predetermined range. - View Dependent Claims (6, 7, 8)
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Specification