Considerations when creating learning instance in Document Automation
- 最終更新日2024/10/31
Considerations when creating learning instance in Document Automation
While creating a learning instance, keep in mind these considerations for form and table fields, search fields, additional information about output folder after creating a learning instance, and so on.
View and search fields
Document Automation offers a standard set of form and table fields, many of which are not initially visible. You can search for a field by field name, field label, or data type.
To see the full list of fields, click Show unused fields. See the following video for a demonstration:
Guidelines to edit the fields and create custom aliases
- You can edit most attributes of a field.
- You cannot edit the name and default aliases. Document Automation assigns default aliases, which are hard coded keywords, to standard fields to help with extraction.
- You cannot modify or delete default aliases, but you can add aliases in the
Custom aliases field.
See the following video for a demonstration of creating a custom alias:
Considerations for form and table fields
Option | Description |
---|---|
Field name | Enter a field name that begins with an alphabetical character
(A-Z or a-z). In standard fields, the field name is hardcoded and cannot be changed. |
Field label | Enter a user-friendly name to help validators. For example, you can rename Organization tax number to a localized name, such as VAT number. The field label does not affect extraction. |
Confidence | Set a threshold to reduce potential false positives. At the time of processing, the Document Automation engine assigns a score to each field in a document to indicate the certainty that the data was correctly extracted. If the document contains fields with a score that is lower than the confidence threshold, the document is sent to the validation queue. If you enter a high confidence threshold, more documents will be sent to the validation queue. If you enter a low confidence threshold, fewer documents will be sent to the validation queue. Supports values from zero to 100. |
Data type | Choose from
Address*,
Text, Number, and
Date. If the data in the field does not match the data type, the document is sent to the validation queue. Document Automation supports date and number format variations.* If you are configuring a learning instance with a user-defined document type, the form fields include the address data type, which extracts the entire structure of an address. |
Format Date/Number | Set a standard appearance to convert extracted dates and
numbers to a specific format. This ensures consistency and
accuracy in your databases and other systems of record. For
example, if you select to standardize dates to
If you select to standardize numbers to the English (United
States) locale and a processed document contains a number
that appears as |
Required | Select one of the following:
|
Use validation feedback to improve accuracy | Disable or enable validation feedback for the field. When you select the
Improve accuracy using validation
option on the Create Learning Instance
page, this option is enabled for all fields by default. When
this option is enabled, validation feedback provided for the
field is used to improve data extraction accuracy. However, if
you see that a field value is extracted as per your requirements
even without the need of validation feedback or when you need to
use alternate solutions such as pre-trained model or 生成 AI providers to improve
data extraction, you can clear the check box for this option to
disable validation feedback for the field. 注:
|
Default aliases | No action is necessary for this field. Document Automation assigns default aliases, which are hardcoded keywords, to standard fields to help with extraction. |
Custom aliases | Additional keywords to help Document Automation locate the field. For
example, add country or region-specific names for fields such as
VAT number as an alias to an Organization tax
number custom field. You can add up to 30 unique custom
aliases. 注: Custom aliases must be
unique. They cannot duplicate the default alias of another
field. Exception: Form fields can have duplicate
custom aliases as the table fields and vice
versa.
|
Validation rules | Depending on data type, create rules using patterns, formulas, lists, and statements such as starts or ends with. |
Guidelines to create or edit the custom multi-table in a learning instance
- This feature is applicable to the document types such as Automation Anywhere (Pre-trained), Automation Anywhere (User-defined), IQ Bot to Document Automation Bridge パッケージ, and unstructured (生成 AI).
- You can set up the rules with only one table field at a time and it cannot be setup across fields that belongs to different tables.
- All fields names must be unique.
- Advanced training settings is applicable to multi-tables. It will work on a per table basis and not across the tables.
- You can import, export, and copy learning instances that contain multi-tables.
- You can delete the custom table created in learning instance but the default table cannot be deleted.
- The maximum number of characters for custom multi-table name are 50 characters and 200 characters for column name.
- Only alpha numeric, underscore, spaces, and hyphen characters are allowed in for the table name field.
- You cannot rename the table name while creating or editing a learning instance.
- Multi-table support is not applicable for 標準フォーム, Google Document AI, bill of lading, waybill, arrival notice, and packing list document types.
- The output (CSV) file will indicate all the table references (default and custom multi-table) in the table_name: field name format.
Bot output file and folder structure
- Process: Manages the process using if/else scenarios through which Document Automation extracts data from uploaded documents, assigns documents to users for validation, and downloads the extracted data. To learn more, see Automation Co-Pilot for Business UsersのプロセスDocument Automation
- Extraction bot: Extracts data from defined fields in the uploaded documents.
- Download bot: Downloads the extracted data to a specific folder on the device or shared network.
- Form: Defines the input parameters that are sent to the process. Input parameters include the learning instance name, uploaded file, and output file path.