---
sourceDocument: Xanadu Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/xanadu/intelligent-experiences

 Release :

    - xanadu

ft:locale :

    - en-US

ft:publication_title :

    - Xanadu Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Create a pattern entity

# Create a pattern entity {#ariaid-title1}

* Release version: Xanadu
* 
* Updated August 1, 2024
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 1 minute to read

Create a pattern entity from a word or phrase with repeatable patterns, such as email
addresses and phone numbers. These patterns help the system to recognize similar utterances
based on the patterns.

## Before you begin

* Make sure that the NLU Workbench plugin, NLU Workbench - Core plugin, NLU Common Model plugin, and Predictive Intelligence plugin are all installed and activated on your instance.
* Create or use an existing NLU model for Virtual Agent or AI Search.
* Create or use an existing intent.
* Role required: nlu_editor, nlu_admin, or admin. The nlu_editor must be assigned to the model.
{#create-pattern-entity__ul_mk3_cb4_rhb}

## About this task

Pattern entities help your model identify, contextualize, and govern similar types of content. Inputs such as phone numbers and email address follow the same pattern. By creating a pattern entity, you can train the model to recognize all variations of those inputs.  
Note:  
Pattern entities use regular expressions (regex). The regex field value is a Java regular expression. For more information on how to use regex, see [Using regular
expressions in entities](https://servicenow-prod.fluidtopics.net/MLF7GQKcQp5lQTgeBdhGfw "Learn how to use regular expressions in your NLU entities to establish patterns that help the system locate, match, and manage text.").

In this example scenario, you've created an intent that's titled
#CheckITTicketStatus. In this example procedure, you're creating a
pattern entity for incident record numbers.

## Procedure

1. Navigate to AllNLU WorkbenchModels.  
   The Virtual Agent tab opens by default.
2. Select the tab for your model's application, then the name of the model.
3. On the model details page, select Intents.
4. Select the name of an intent.  
   In this example scenario, you select #CheckITTicketStatus.
5. Select one of the words of an utterance.  
   In this scenario, you click INC1234567.

6. Select Create New Entity.
7. In the Create a new entity window, configure the fields.  
   For this example procedure, use these configurations:
   * Entity Name: Enter <kbd class="ph userinput">incidentnumber</kbd>
   * Type: Select Pattern
   * Model Availability: Select this box if you want this entity to be included in all intents in your model
   * Regex: Enter <kbd class="ph userinput">INC\d{7}</kbd>
   {#create-pattern-entity__ul_n2r_s4b_jsb}
8. Click Save.  
   The pattern entity saves and appears in the Associated Entities tab. The model can use the pattern entity to interpret variations of the annotated utterance. For example, the model can match the #CheckITTicketStatus intent if a user enters any incident record number.
**Related concepts**   

* [Using regular expressions in entities](https://servicenow-prod.fluidtopics.net/MLF7GQKcQp5lQTgeBdhGfw "Learn how to use regular expressions in your NLU entities to establish patterns that help the system locate, match, and manage text.")  
**Related tasks**   

* [Create a simple entity](https://servicenow-prod.fluidtopics.net/N~5DJDKjarceXuhwMKE6fg "Create one or more simple entities from words in your utterance examples. An entity is an object of, or context for, an action.")
* [Create a mapped entity](https://servicenow-prod.fluidtopics.net/L36lfvGw3Fx2eblSAc6lRA "Create an entity mapped to a vocabulary source, or to a list of values you manually create for the entity. Mapped entities can help provide multiple values the model can use as context when interpreting utterances.")
* [Create a system-derived entity](https://servicenow-prod.fluidtopics.net/L1zE3lTZQATqQxa52hzljg "Create a custom entity that's derived from a default system entity such as date, time, duration, or location.")
* [Create an open-ended entity](https://servicenow-prod.fluidtopics.net/ZSZ60xAPMEW~bNeORY2HfA "Use an open-ended entity when you want to improve intent prediction accuracy. Open-ended entities help your model focus on the context of the utterances.")
* [Import entities](https://servicenow-prod.fluidtopics.net/iz1K~UJviKCvsN2yRoYoCQ "Reuse entities that you have created across your other Natural Language Understanding (NLU) models. Importing entities saves time and helps improve the intents in your model.")

*[\>]: and then