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  • Release Notes
      • June 2024
      • May 2024
      • January 2024
  • Overview
  • Uploading data to Communications Mining
  • Using the Dispatcher Framework
  • Activities reference
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Communications Mining Activities
Last updated Oct 11, 2024

Frequently Asked Questions

In this section we provide answers to some common questions within the Communications Mining framework.

How do I read a user property?

For your stream result just use the syntax StreamResult.Comment.UserProperties(<NAME>).ToString().
Note:
The name of your user property should be prefixed with its data type, for example, Message ID or number:Order Count. So the complete expression will be StreamResult.Comment.UserProperties("string:Message ID").ToString().

How do I check if an extraction has exceeded a threshold?

There are two ways to check if an extraction has exceeded the threshold:

  1. Access the LabelExtraction via one of the ...OverOccurrenceConfidenceThreshold... helper functions, which will automatically filter out the LabelExtractions that have not exceeded your threshold
  2. Use the following syntax: LabelExtraction.OccurenceConfidence.HasExceededThreshold(<threshold name>)

How do I locate my email in exchange when using the Exchange Integration?

When using Communication Mining’s out of the box exchange integration, you can obtain the exchange message id by looking at the Message ID user property. The syntax is detailed in the How do I read a user property section. You can then use this Message ID with any of the Outlook activities to get the specific email.

How do I use my StreamResult in my downstream automation?

The dispatcher framework saves the StreamResult as JSON in your Orchestrator Queue when it is added. To use this data later, import the Communications UiPath.CommunicationsMining.Activities activity into your downstream process. Then use the syntax Newtonsoft.Json.JsonCovert.DeserializeObject(of StreamResult)(<json data here>) to convert the JSON string back into a StreamResult.

What’s a leaf label?

A label label is a label prediction that doesn’t have any children, these are the most granular labels in your taxonomy. In the following example the leaf label is Account Change > Address > Billing.
Account Change
Account Change > Address
Account Change > Address > BillingAccount Change
Account Change > Address
Account Change > Address > Billing

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