IQ Bot technology breakdown

Hi @Dylan Lesperance​ ,

Please find the below,

Automate document-centric business processes, end to end, by using IQ Bot, a web-based, Cloud-native intelligent document processing solution that can read and process complex documents and email. This solution combines RPA with AI techniques to extract and classify semi-structured and unstructured data.

I'd recommend to you, please check out the below link: 👇

https://university.automationanywhere.com/training/rpa-learning-trails/iq-bot-developer//p>

Phases in IQ Bot business process:

1. Preprocess documents.
2. Receive text segmentation and optical character recognition (OCR).
3. Classify documents in groups.
4. Extract document data.
5. Validate and correct failed documents.
6. Complete validation and save.
7. Trigger approval.
8. Obtain final review and approval

Where Python used in IQ BOT: 👇

https://developer.automationanywhere.com/blog/how-to-increase-stp-in-iq-bot-using-custom-logic/p>

How the documents are classified :

https://developer.automationanywhere.com/blog/understanding-groups-creation-classification-in-iq-bot/p>

In the below link, you find the complete documentation about the IQ BOT and Training as well.

https://docs.automationanywhere.com/bundle/enterprise-v2019/page/enterprise-cloud/topics/iq-bot/cloud-iqb-process-overview_1.html/p>

Please let me know if you have any questions.

Thanks!

Hi @Dylan Lesperance​ ,

OCR helps us to extract the data from document on top of it if you want to do any data transformation we will use Python. Ex: if you want to change the date format from one format to another etc.

ML helps the LI to learn from the corrections the users we make via Validator, over a period of time the model gets matured and next time it handles by its own.

Hey @ChanduMohammad S​ 

Thanks for the context. It's my understand the only improvements made using ML is the IQ Bot's understanding of the document layout, not any improvements on the data returned in those fields. The data returned in fields are fully depended on the OCR provider selected?

Hey @Dylan Lesperance​ - it may be just semantics, but Document Automation (The newest version of document extraction capabilities from Automation Anywhere) does indeed do the learning that you're talking about as far as recognizing how to find additional data based on correction from operators - which will ultimately increase straight through processing. You can see this yourself in CE by processing a few documents, correcting them in AARI by clicking and dragging to draw zones over the missed data, then processing the same/similar forms again. You'll notice that data that previously was not auto-extracted is now extracted based on the corrective feedback provided by the operator