PATENT CLAIM ANALYSIS

Application Number: 15892642
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["382", "135000"]

Abstract:
A system is described that uses “freckles” contained within printed content on Federal Reserve Notes (FRN), and the image-capture of such freckles, along with the printed Federal-Reserve-Note Serial Number (FSN) on each FRN, to detect and deter counterfeiting within physical currency systems of the U.S. and countries worldwide. Additionally, databases on Central Monitoring Agency servers (CMA servers) maintain a status on such captured data for FRNs in use, or potential use, by the U.S. Treasury or other currency issuing and oversight agency that maintains CMA servers. Devices that count and scan FRNs, at diverse point-of-sale and other currency-exchange and transaction locations (Transaction Locations), as well as those part of back-office operations at Transaction Locations, capture FRNs and freckle-related content from FRNs, for transmission to CMA servers via the Internet (Internet of Things Architecture or IOTA™). The system enables Big Data analytical methods to detect and dramatically reduce or eliminate the use of counterfeited FRNs.

Claim (Index 2):
The system of  claim 1 , wherein numeric ID, address, and geodetic information are used to detect the same bill being used at two, different point-of-sale and transaction locations, within an unreasonable short period of time, if the places are geographically dispersed, the system allowing trigger thresholds to be dynamically set much as an adaptive filter allows in detecting noise.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 88.0
- Lexical Diversity: 1.71429
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['10430025', '12544052', '10337183', '13785636', '14151646']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4461457199635955
- 35 USC 102 Novelty (BERT): 0.4876794703879588
- Combined Prediction Score: 0.4502990950060318
- Mean Citation Score: 139.71880199999998
- Max Citation Score: 151.71469
- Similarity Product: 91.142016641922

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test