---
sourceDocument: Yokohama IT Service Management
sourceDocumentLink: https://www.servicenow.com/docs/r/yokohama/it-service-management

 Release :

    - yokohama

ft:locale :

    - en-US

ft:publication_title :

    - Yokohama IT Service Management

ft:clusterId :

    - itsm

bundleId :

    - itsm

workflow :

    - Technology


---

# Suggest relevant Incidents for an Incident

# Suggest relevant Incidents for an Incident {#ariaid-title1}

Release version: Yokohama  
Updated January 30, 2025  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 minutes to read  
Use this template to recommend similar relevant incidents to help expedite your
incident investigation and resolution processes.

## Before you begin

Role required: piwb_manager or piwb_viewer

## About this task

This use case template helps you improve your ITSM first-call
resolution and reduce the time required to investigate resolution steps for incoming
incidents. The template also provides a link to the Predictive Intelligence
platform application and associated documentation.  
The use case uses a similarity-based predictive model that compares an incoming incident with past resolved incidents for smarter resolution. Similar past active incidents are displayed in Agent assist and also in the Incident form via related search.  
Note:  
You will need business users to validate the similar past active incident search results. Contact Customer Service and Support, if necessary, to configure sources for contextual search.

When the use case template shows the label Pretrained, you can
go directly to the Testing your models implementation
section. Otherwise, you will begin by creating a machine learning model.

## Procedure

1. If you started this procedure directly from the Predictive Intelligence Workbench product Create New from Template module, and clicked on the product documentation link to get here, skip this step.  
   Otherwise, navigate to Predictive Intelligence WorkbenchUse CasesCreate New from Template.
2. Select the Suggest relevant Incidents for an Incident template.  
   The Suggest relevant Incidents for an Incident pop-up opens featuring a link to this procedure and a link to the platform Predictive Intelligence product and associated documentation.
3. Create and train a machine learning predictive model.  
   Creating a model involves the following: creating a word corpus, defining a similarity prediction rule, defining the initial training frequency, and defining the refresh frequency.
   1. [Create a word
      corpus](https://www.servicenow.com/docs/access?context=create-word-corpus&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US).  
      When creating a word corpus on the Word Corpus Content form, select
      an incidents-related table, such as Incident \[incidents\] in the
      Table field and define the time frame
      that best describes the current usage of words in the
      Filter field. For example, if your IT
      system experienced a major infrastructure change six months back,
      use data from the last six months only. In the Field List field, define only the fields that best capture
      the words: Description, Short description, and Resolution notes. Defining these alone is typically enough,
      since the prediction rule is expected to find incidents based on the
      short description.

      Creating the word corpus prepares you for the next step, creating the
      similarity prediction rule.
   2. [Create and train
      a similarity solution](https://www.servicenow.com/docs/access?context=create-similarity-solution&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US).
   3. For this initial model creation, provide the similarity solution definition label <kbd class="ph userinput">Similar Incidents</kbd> in the Label field.
   4. Set the Training Frequency field to Run Once.  
      You can reset this configuration after you implement this use case into your business processes and have monitored its performance in the Predictive Intelligence Workbench dashboard.
   5. Set the Update Frequency field to Every 15 minutes.  
      This setting defines the frequency at which past incidents refresh in the search window.

      When possible, and if applicable, use an existing word corpus, created for another use case to reduce your overall word
      corpora and ease management of these records. In the Table field of the Similarity Definition form, select only those inputs for similarity that will be available at prediction time for
      incoming incidents. You can select more inputs in the Test Table field, if required, for comparison. It is best to start with similar fields for both the Table and
      Test Table fields.

      The Filter field conditions determine the search window for past resolved incidents. Configure the filter conditions to optimize for the best
      set of resolved incidents to search within. This includes many considerations, such as, time frame, location, category where incidents are relevant, and more.
   6. Click Submit \& Train to create your similarity solution record and train it.  
      Alternatively, you can click Submit to save your similarity solution record and return to train it later.
   {#itsm-piwb-suggest-relevant-incidents__substeps_ql4_h5r_xlb}
4. [Evaluation and tune your
   model](https://www.servicenow.com/docs/access?context=review-similarity-examples&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US).  
   If you have a similarity score above 60, but the two incidents do not look
   similar, you may want to create another model, word corpus, or both by
   changing inputs and filters. Keep in mind that modifying the solution
   definition will help you create a new solution, but it will invalidate the
   previous solution.

   If you want to revert back to the previous solution definition, you will have
   to reset the parameters and retrain the solution. Therefore, first try
   creating a new similarity model before creating a new word corpus.

   If you want to adjust the score for your similarity solution, refer to [Update your
   similarity score threshold](https://www.servicenow.com/docs/access?context=update-similarity-threshold&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US).
5. Once you have a satisfactory model, [test the similarity
   solution prediction](https://www.servicenow.com/docs/access?context=test-similarity-solution-prediction&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US).  
   You can manually provide inputs and select the top similar results outcome values.
6. Once you have tested the behavior, [configure the user
   experience](https://www.servicenow.com/docs/access?context=configuring-advanced-settings-ml-solutions&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US) layout to show attributed results and actions performed on the results.  
   You can configure these results and actions via Workspace UI for Agent assist or via the ServiceNow AI Platform for Contextual Search. Configure actions and search context though Tables configuration and user experience and card layout through Contextual SearchSearch Result Display Configuration.
7. [Integrate trained models
   by exporting them to production](https://www.servicenow.com/docs/access?context=implement-iterative-solution-updates&version=yokohama&pubname=yokohama-intelligent-experiences&ft:locale=en-US).  
   Note:  
   For details regarding trained use case integration implementation, refer to [Predictive Intelligence Workbench integration and customization](https://www.servicenow.com/docs/oPISzVagDnMgPlFA1M3wYQ "Predictive Intelligence Workbench uses scripted extension points to integrate a trained use case model for prediction.").
8. Monitor similarity results and ensure IT agents are providing useful feedback.  
   The base-system Predictive Intelligence experience includes a
   built-in feedback mechanism to discern if the similarity results are useful.
   Train your IT agents to provide feedback, both online and offline, to
   capture this data for future reporting. Since this is an unsupervised
   algorithm, you may need to acquire periodic feedback from the IT agents to
   check that the similarity model is still providing satisfactory results.
   This feedback is the only way to determine if the model has drifted and
   requires new training. Ensure that part of your implementation and
   integration strategy, as well as your change management process, includes
   training IT agents to provide similarity results feedback.
9. Communicate the value of Predictive Intelligence to your stakeholders by linking business key performance indicators (KPIs) to machine learning metrics.  
   Select one or more KPIs that you think is most beneficial to your IT agents.
   Create a Performance Analytics dashboard showing the trend of these KPIs. The
   "likes" you get from your IT agents via the feedback mechanism helps you
   communicate the value of Predictive Intelligence.

   For information regarding the Predictive Intelligence for Incidents
   dashboard, refer to [ITSM Predictive
   Intelligence Workbench dashboard](https://www.servicenow.com/docs/KlV1qVGGGEnRTpGWhqmwMQ "ITSM Predictive Intelligence Workbench provides the Predictive Intelligence for Incidents dashboard to enable you to measure the value of using machine learning to automate your IT business processes. Monitor use case models and view associated statistics. Effectively demonstrate business value to stakeholders with dashboard views.").
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*[\>]: and then