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Fingerprinting as an idea was initiated in a hackathon event in Richardson, Texas in early 2017.  Many contributed to furthering the concept, including NAME_1, NAME_2, NAME_3, NAME_4, NAME_5, NAME_6, and NAME_7. 
All ML based solutions begin with understanding of the problem to solve with ML.  The first problem for the team in the hackathon was NOT risk scoring, but instead global data lookups across millions of devices.  The existing non FP solution was frustratingly slow for engineers supporting customers.   More value could be realized from global anonymized global data, if:
•	Comparison could be done in a reasonable amount of time (Within normal expected “clickstream” windows)
•	Engineers (domain experts) were provided the ability to compare their own knowledge (Of their own customer devices) to other devices in other networks, individually or in groups.
To meet these requirements, the “fingerprint” ML based approach was explored.  The initial concept was based on simple metadata similarity.  It was proposed that if we could represent the network devices in mathematical ML format, then rapid similarity lookups could be very quickly provided.  This was solving the problem that we had no way to quickly compare devices within a single customer network, nor could we compare across customer networks, especially in the case of fuzzy match (devices not identical but extremely similar). Early use cases of this technology included:
•	If a critical CX support case is opened against a device, how can I notify others that they also have a similar device at risk?
•	If I had a crash in one device in my network, how can I quickly find out if I have any other devices that are very similar to that one, and may also be at risk?
•	If I have a set of devices that I intended to be identical in my environment, how can I determine, mathematically and visually, that they are indeed the same, or at least close?
An open-source search index solution was tested and proven to provide the desired capability.  The creation method of this search index is covered in this document.
After the workshop, simple data visualization techniques were developed by NAME_1 and NAME_8, and applied as a solution to look at a large group of devices at once.  Later, customized data visualization graphics and techniques developed by NAME_9 and NAME_10 were used to show that a generalized representation of hundreds or thousands of devices could be done all at once an Write exactly 4 paragraphs each separated with two new lines answering this question. The first paragraph must start with "peace".
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