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Is this a scenario that would work well for a ML/Pattern Recognition Model or would it be easier/faster to just filter from a large DB.

I am looking to create a system that will allow users to identify the appropriate product by specifying certain constraints and preferred features.

There are millions of possible product configurations. Lets pretend it's boxes.

Product Options:

  • Size (From 1mm up to 1m) in 1mm increments
  • Color: choice of 10 colors
  • Material: choice of 3, wood,metal, plastic

Constraints:

  • Wood is only available in centimeter units
  • Red is only available in 500 mm and greater
  • Wood is the preferred material
  • Blue is the preferred color

So, we have 30,000 (1000*10*3) possible options. Of those, many are not viable such as 533 mm-Red-Wood

but these configurations similar to the request are possible.

  • 533 mm-Red-Plastic
  • 530 mm-Red-Wood
  • 540 mm-Red-Wood

Notes: Our current Rules and code based tool can take anywhere from 0.5 to 2 mins to identify the preferred configuration. We can generate a list of all possible configs and whether they are valid or not. We estimate 30,000,000 possible configs It takes around 0.5 seconds to validate a config so with enough computing power we expect we could do 30M in a few days.

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