UiPath UiPath-SAIAv1 - UiPath Specialized AI Associate Exam (2023.10)

Data Extraction identifies and extracts specific information from documents to be processed by automations. This is a key part of UiPath's Document Understanding Framework, enabling bots to process structured and unstructured documents effectively.

According to the UiPath documentation, AI Center provides three types of out-of-the-box model types that can be used for different purposes and scenarios1:

Pre-trained: These are models that are already trained on a large and diverse dataset and can be used as-is for inference or prediction. They do not require any additional data or training from the user. Examples of pre-trained models are Language Detection, Sentiment Analysis, and Question Answering.

Custom training: These are models that allow the user to train them on their own data using the AI Center UI or API. They require the user to upload a dataset, configure the training parameters, and monitor the training progress and results. Examples of custom training models are Image Classification, Text Classification, and Named Entity Recognition.

Fine-tunable: These are models that are pre-trained on a general dataset but can be further trained or fine-tuned on a specific dataset provided by the user. They offer the best of both worlds: the benefit of using a pre-trained model and the flexibility of customizing it to the user’s needs. Examples of fine-tunable models are Object Detection, Text Summarization, and Language Translation.

When setting up a classification label in UiPath's Out Of the Box (OOB) labeling templates, you need to provide several key details: the name of the label, the classification type (which defines the kind of label), the input to be labeled, the attribute name that describes the label's context, a shortcut for quick access, and a color for visual distinction. These fields ensure the label is fully defined and easy to manage in workflows.

(Source: UiPath Document Understanding documentation)

UiPath Communications Mining uses Natural Language Processing (NLP) to analyze and process unstructured communication data such as emails, chat logs, and other textual inputs. This enables automation to identify actionable insights from large volumes of communication data.

| Step | Description |

| ---- | -------------------------------------------------------------------------------------- |

| 1 | Click "Taxonomy Manager" in the Studio Design ribbon then click Taxonomy. |

| 2 | Create a Category by clicking "+ Category" and then click the created Category. |

| 3 | Create a Group by clicking "+ Group" and then click the created Group. |

| 4 | Create a Document Type by clicking "+ Document Type" and then click the Document Type. |

To define a document structure that theDocument Understanding frameworkcan classify and extract from, the following hierarchical structure must be created in theTaxonomy Manager:

Taxonomy Manager– Launch from theDesign ribbonin Studio.

Category– Top-level classification (e.g., Financial, HR, Legal).

Group– Sub-division under a category (e.g., Invoices, Contracts).

Document Type– Specific type of document (e.g., Vendor Invoice, NDA).

This structure is required for theClassify Document Scopeactivity to function properly.

🔗UiPath Documentation Reference:

Taxonomy Manager – UiPath DU

Document Types and Categories

The Data Extraction Scope activity provides a scope for extractor activities, enabling you to configure them according to the document types defined in your taxonomy. The output of the activity is stored in an ExtractionResult variable, containing all automatically extracted data, and can be used as input for the Export Extraction Results activity. This activity also features a Configure Extractors wizard, which lets you specify exactly what fields from the document types defined in the taxonomy you want to extract1.

The extractors that can be used for Data Extraction Scope activity are:

Regex Based Extractor: This extractor enables you to use regular expressions to extract data from text documents. You can define your own expressions or use the predefined ones from the Regex Based Extractor Configuration wizard2.

Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the position and relative distance of the fields. You can define the templates for each document type using the Form Extractor Configuration wizard3.

Intelligent Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the labels and values of the fields. You can define the fields for each document type using the Intelligent Form Extractor Configuration wizard.

Machine Learning Extractor: This extractor enables you to extract data from any type of document, using a machine learning model that is trained on your data. You can use the predefined models from UiPath or your own custom models hosted on AI Center or other platforms. You can configure the fields and the model for each document type using the Machine Learning ExtractorConfiguration wizard.

Comprehensive and Detailed Explanation From Exact Extract:

When debugging in UiPath Studio:

TheImmediate Panellets youevaluate or change variables and expressionsduring runtime.

TheLocals Panelshows current variable values in scope but doesnot allow editing.

Toassign new values to variables, theImmediate Panelmust be used.

UiPath Documentation Reference:Debug Panels – UiPath Docs

In the Document Understanding framework, the dispatcher is responsible for ensuring that one job is created for each input file. It works by submitting files to be processed individually, ensuring that each document or group of documents is handled as a separate transaction. This allows for more efficient processing and better tracking of each file, especially in high-volume workflows where managing each file as a separate job is critical for performance and error handling.

(Source: UiPath Documentation on Document Understanding)

The Document Object Model (DOM) is a data representation of the objects that comprise the structure and content of a document on the web1. In the context of Document Understanding, the DOM is a JSON object that is generated by the Digitize Document activity, which uses the UiPath Document OCR engine to extract the text and layout information from the input document2. The DOM contains the following properties for each document3:

name: The name of the document file.

contentType: The MIME type of the document file, such as application/pdf or image/jpeg.

textLength: The number of characters in the document text.

pages: An array of objects, each representing a page in the document. Each page object has the following properties:

pageNumber: The number of the page, starting from 1.

rotation: The angle of rotation of the page, in degrees. A positive value indicates clockwise rotation, and a negative value indicates counterclockwise rotation.

language: The language code of the page, such as en or fr.

content: An array of objects, each representing a word or a line in the page. Each content object has the following properties:

type: The type of the content, either word or line.

text: The text of the content.

boundingBox: An array of four numbers, representing the coordinates of the top-left and bottom-right corners of the content, in the format [x1, y1, x2, y2]. The coordinates are relative to the page, with the origin at the top-left corner, and the unit is pixel.

confidence: A number between 0 and 1, indicating the confidence level of the OCR engine in recognizing the content.

The DOM can be used as an input for other activities in the Document Understanding framework, such as Classify Document Scope, Data Extraction Scope, or Export Extraction Results. The DOM can also be manipulated using programming code, such as JavaScript or Python, to perform custom operations on the document data.