Document Automation v.39 release

Review what's new and changed, and the fixes and limitations in Document Automation for the v.39 release.

What's new

Introducing Google Gemini models on AWS and GCP for data extraction in Document Automation

You can use the Google Gemini 생성형 AI models that are available via Amazon Web Services (AWS) and Google Cloud Platform (GCP) deployments for data extraction in Document Automation.

Google Gemini provides the following advantages:

  • Efficient processing of large, unstructured documents
  • Handle documents in both English and other languages
  • Handle complex document layouts
  • Faster processing of documents with better data extraction accuracy
Note: Bring your own license (BYOL) is not supported for Google Gemini models.

Document Automation에서 학습 인스턴스 생성

What's changed

Enhancements to version history in test mode

The following enhancements are made to the version history in test mode:

  • When you make changes to a learning instance and the version selected in the version history is the latest, you will be allowed to save the changes made to the learning instance.
  • When you make changes to a learning instance and the version selected in the version history is not the latest, you will see an error when you try to save the changes made to the learning instance. Click Go to latest version, make changes to the learning instance again, and then save the changes.
  • The version history drop-down will display all the available versions.

학습 인스턴스의 버전 이력 보기

Fixes

When you enable the test mode option for a learning instance in the public mode, the learning instance will no longer be moved to the private mode and will continue to be in the public mode.

Previously, the learning instance was moved to the private mode in certain scenarios.

Service Cloud Case ID: 02273055, 02291142
Data extracted from tables using 클라우드 추출 서비스 and Task Bot now generates similar outputs.

Previously, the outputs were different in certain scenarios.
Table extraction using the 클라우드 추출 서비스 now works for User-defined learning instances.

Previously, table extraction did not work in such a scenario.
Advanced training settings such as table headers and end-of-table indicators are now preserved after applying the end-of-table value.

Previously, applying a new end-of-table indicator reset all advanced training settings.
You can now delete custom tables when editing or creating learning instances.

Previously, users were unable to delete custom tables in such scenarios.
The Learning Instances page now displays options in the Actions column correctly when the Control Room language is set to German or French.

Previously, the options were misaligned in such a scenario.
You will no longer see an error when moving learning instances from IQ Bot to Document Automation using the IQ Bot - DA Bridge package.

Previously, an error was displayed in such a scenario in certain instances.

Service Cloud Case ID: 02284063
You can now import a learning instance without errors when the .dw file contains a different version than the existing learning instance in the target environment, provided the test mode option is disabled in the existing learning instance.

Previously, an error was displayed in such a scenario.

Service Cloud Case ID: 02282032, 02288219
For a learning instance, if you keep the default table prompt used in the learning instance and add a custom prompt for any table field in the 적응형 검색 쿼리, the table prompt in the 적응형 검색 쿼리 no longer resets and the custom prompt is retained.

After updating to the v.39 release, all blank or empty table prompts in 적응형 검색 쿼리 will be treated as if the user clicked the Use Default option, resulting in the prompt values reverting to default table prompts. In such a scenario, you need to manually clear the table prompts in 적응형 검색 쿼리 after the update to set the prompt values to blank.

When you move a learning instance that was used to process documents in German from IQ Bot to Document Automation using the IQ Bot - DA Bridge 패키지 and process documents in Document Automation, data containing certain German characters is extracted correctly.

Previously, certain German characters were not extracted correctly.

Service Cloud Case ID: 02231391, 02273919
You can now use a custom table name in Japanese, Korean, or Chinese when creating a custom table.

Previously, you were not able to use a custom table name Japanese, Korean, or Chinese.
When you restore a learning instance to any previous version from version history and reprocess a document, the document upload count is not increased.

Previously, the document upload count increased in such a scenario.
You can now add fields to existing 표준 양식 projects, create new models, and train the models without any errors.

Previously, an error was displayed in such a scenario.
The Automation Co-Pilot validator now correctly displays a validation error when a required signature field is not found in 표준 양식 documents.

Previously, no error was displayed in such a scenario.
When you create a model in 표준 양식, any leading or trailing spaces in table names will automatically be removed.

Previously, an error was displayed when training the model in such a scenario.
After performing a search for a learning instance, you can now create a learning instance in Community Edition if the limit allows.

Previously, users were not able to create a learning instance in such a scenario.

Limitations

In the Advanced training settings option, you might see table fields listed in the Primary Column drop-down even when those fields are not selected for validation feedback.

Workaround: Avoid selecting any table field in the Primary Column drop-down if that field is not selected for validation feedback.
When you process documents containing multiple tables in 표준 양식, the order of the tables extracted is reversed.
When you use a regular expression for a table field without defining an alias, the regular expression might not be considered for extracting the data.

Workaround: Define an alias for the table field before using a regular expression.
Limitations from previous releases
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 문서 분류기 작업 (Classify, Classify documents, and Train Classifier) and Extract Data 작업 in the 문서 추출 패키지 together in a 봇, the 봇 will fail to execute.

Workaround: Ensure that you create separate Bot when using any of the 작업 from the 문서 분류기 패키지 and the Extract Data 작업 of the 문서 추출 패키지. If you need to execute these Bot in a sequence, include these Bot 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 패키지 작업 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.

IQ Bot

The IQ Bot 온프레미스 installer now displays the correct release version in the installation directory.

Previously, the release version displayed in the installation directory was incorrect.

Updates to the interface

Document Automation
You can now use Google Gemini 생성형 AI provider when creating learning instances:Create learning instance using the Google Gemini 생성형 AI provider

Introducing Google Gemini models on AWS and GCP for data extraction in Document Automation