AI Report Analysis

ARDI can analyse your report output and - with some training from your users - use machine learning to tell the difference between a 'normal' and an 'abnormal' report.

This helps combat report fatigue - people skimming over reports without really reading them - by highlighting those reports that appear to have unusual information.

We don't suggest pushing large amounts of data for AI analysis - sending noisy raw data will usually not give you a lot of value.

However, summary information such as minimum, maximum and average measurements can be extremely useful in AI analysis.

To use AI analysis, write a JSON file containing a dictionary of data point arrays. This file should have the same name as your report, but with the .json file extension.

For example, if we wanted to record the minimum, maximum and average value(s) of our temperature data, our file should look like the one below…

{
   "Temperatures": [80,200,185]
}

For multi-channel reports, you might choose to have several different items in the dictionary - this helps the AI explain which part of the report is unusual when you have a lot of data shown on the one page.

{
   "Temperatures": [80,200,185],
   "Pressures": [2000,2500,3000]
}

If you're using our reporting library, you can do this automatically using the AIChannel function.