Review what's new, and the fixes and limitations in Document Automation for the v.38 release.

What's new

Adaptive Search Queries

Automatically adjust to layout variability across thousands of formats and dynamically apply the correct query at runtime. Users can test and define specific search queries for documents with similar layouts within a single learning instance, enhancing data extraction accuracy for varied document formats.

The Adaptive Search Queries feature provides the following benefits:
  • Test and validate search queries at the cluster level.
  • Define different search queries for the same field across multiple clusters within a single learning instance.
  • Promote validated cluster-specific queries to production for robust and consistent extraction performance.
  • Minimize manual query tuning and improve extraction accuracy across varied document formats from within a single, unified learning instance.

Adaptive Search Queries
Azure AI Document Intelligence version 4.0 support in Standard Forms (Service Cloud Case ID: 02166473)

You can now create custom models in Document Automation using Azure AI Document Intelligence version 4.0. The new capabilities include improved custom models, improved accuracy in data extraction, and signature detection.

The following capabilities are included in the Control Room:

  • The Models page now displays the version number used to create each Azure AI Document Intelligence model.
  • Support for signature detection is introduced, enabling identification of handwritten and digital signatures in documents. This improves automation accuracy in workflows that require signature validation.

Create a learning instance for standard forms | Validation rules in Document Automation
Support for new languages in Automation Anywhere user-defined provider

You can now process documents in the following languages for the User-defined document type when using the Automation Anywhere (user-defined) provider and ABBY OCR or Google Vision OCR provider:

  • Czech
  • Danish
  • Finnish
  • Norwegian
  • Slovak
  • Swedish

Languages supported in Document Automation

Fixes

When you are using a proxy server and using generative AI provider for data extraction, the extraction now completes successfully without any errors.

Previously, document extraction failed in such a scenario.

Service Cloud Case ID: 02218893, 02214721, 02200518, 02232155
When you import a learning instance into a Control Room where the same learning instance already exists, all aliases from the exported learning instance are now correctly retained.

Previously, the imported learning instance missed some aliases in such a scenario.

Service Cloud Case ID: 02227635
When you remove data from table cells in the Automation Co-Pilot validator and use the Auto fill option subsequently, the auto-fill process will automatically update the manually emptied cells.

Previously, the manually emptied table cells were left empty.
When you have documents pending validation and update to the v.38 release, you will no longer see an error after validating the first document in the queue and clicking the Submit button.

Previously, an error was displayed in such a scenario.
After updating to the v.38 release, documents that were previously processed without the need for validation will continue to be processed without validation.

Previously, documents were sent for validation in certain scenarios.
When a user is assigned the Conversational Automation Co-Pilot User license, the user will now be able to process documents in public learning instances in Document Automation.
The Auto fill option in the Automation Co-Pilot validator now works as expected.

Previously, this option did not work as expected in certain scenarios.
You can now upload and process documents without errors for learning instances.

Previously, an error was displayed in certain scenarios.

Service Cloud Case ID: 02135184, 02135889, 02154359
You can now process the same documents subsequently without errors.

Previously, an error was displayed in the validator in certain scenarios.

Service Cloud Case ID: 02187660
You can now create Standard Forms models without seeing any performance issues.

Previously, users were experiencing performance issues in certain scenarios.

Service Cloud Case ID: 02219164
After training documents using Standard Forms Neural model, the Run analysis option will extract appropriate information from test documents.

Previously, data was not extracted for certain fields.

Service Cloud Case ID: 02185363, 02185363

The validation feedback field in the version history in test mode will only display fields that were validated by users.

Previously, fields extracted using generative AI were displayed in the validation feedback field.
When you have the Test mode enabled and validation feedback disabled for a learning instance, the cluster ID is now displayed correctly for a document processed in test mode.

Previously, incorrect cluster ID was displayed for the processed document in such a scenario.

Limitations

When you add an invalid regex pattern in the field and document rules for a learning instance and process documents, data extraction will fail.

Workaround: Validate the regex pattern before adding it to the field and document rules and then process the documents.

When using the Adaptive Search Queries feature in Cloud-Sandbox, if you add a custom query for a field, process a document using the custom query, and then open the search queries option to view the custom query, an error will be displayed.
When you have documents pending validation and update to the v.38 release, an error is displayed after validating the first document in the queue and clicking the Submit button.

Workaround: Perform one of the following workarounds:
Limitations from previous releases
You will not be able to use a custom table name in Japanese, Korean, or Chinese when creating a custom table.
When you restore a learning instance to any previous version from version history and reprocess a document, the document upload count is increased.
When you process documents with file names greater than or equal to 75 characters in test mode, you might not see improvements in the reprocessing time for such documents.
If you have disabled the OCR provider in the administrator settings and if you are using a language other than English for your Control Room, you will see an error to enable the OCR provider settings in English in the following scenarios:
  • When you create a learning instance that is using an OCR provider
  • When you change the OCR provider for an existing learning instance
When you use the Document Classifier actions (Classify, Classify documents, and Train Classifier) and Extract Data action in the Document Extraction package together in a bot, the bot will fail to execute.

Workaround: Ensure that you create separate bots when using any of the actions from the Document Classifier package and the Extract Data action of the Document Extraction package. If you need to execute these bots in a sequence, include these bots in an Automation Co-Pilot process.
If you copy a learning instance that is using a third-party parser configured in Document Automation and process documents using the copied learning instance, data extraction will fail.
When a user processes a document on a custom process learning instance, the validate document count is not updated post extraction. Also, if the user submits the document, the validate document count is updated with a negative value.
A user with the Automation Co-Pilot administrator permissions is unable to view the Document Automation tasks that are assigned or requested and are in pending or complete status.
When you are using the IQ Bot Pre-processor package actions and if the Output folder path contains Japanese characters, you will see an error when processing documents.

Workaround: Create an output folder in a folder path that does not contain any Japanese characters and provide the path in the output folder path field.

Updates to the interface

Document Automation
Use the Adaptive Search Queries feature to test and define specific search queries for documents with similar layouts within a single learning instance, enhancing data extraction accuracy for varied document formats:

Adaptive Search Query option

Adaptive Search Queries