In UiPath, the Verbose log level offers the most detailed information about the execution of activities. It logs every possible detail about the automation operations, including variable changes, function calls, and external responses. This level is particularly useful for in-depth debugging and analysis.

UiPath Documentation

The hierarchy of log levels in ascending order of priority is as follows:

Off : No logs are stored.

Verbose : Logs all details about automation operations.

Trace : Logs finer-grained informational events than the Debug level.

Information : Logs informational messages that highlight the progress of the application.

Warning : Logs potentially harmful situations.

Error : Logs error events that might still allow the application to continue running.

Fatal : Logs very severe error events that will presumably lead the application to abort.

Therefore, setting the log level to Verbose ensures that all possible details about the execution are captured, aiding in thorough diagnostics.

The Label Trends table in UiPath Communications Mining shows the trend of the top 10 highest volume labels over the selected time period, as well as their percentage change and rank change compared to the previous period 1 . The table allows users to quickly identify which labels are increasing or decreasing in volume, and by how much, over time.  The table also shows the net sentiment score for each label, which is calculated as the difference between the positive and negative sentiment probabilities for each verbatim 2 . The table can be filtered by data type, source, date range, and label category.  Users can also sort the table by label name, volume, percentage change, rank change, or net sentiment 1 .

References :  1 :  Trends   2 :  Sentiment Analysis

In UiPath ' s Taxonomy Manager , field rules are used to optimize extraction results and perform automatic validation of extracted data. These rules can be applied to specific fields within the document understanding process to ensure that extracted data meets certain predefined conditions or constraints, such as formats, patterns, or value ranges. This allows for higher accuracy in the extraction process and reduces errors, as invalid data can be flagged or corrected based on these rules.

Field rules help automate the validation of data by setting criteria that extracted data must meet, thus enhancing the extraction quality and ensuring that only valid and structured data is processed further in the automation workflow.

For more details, refer to:

UiPath Taxonomy Manager Documentation: Field Rules in UiPath

UiPath Document Understanding Framework : Taxonomy Manager Overview

The correct execution order of the Document Understanding template stages is:

Taxonomy

Digitize

Classify

Extract

Extraction Validation

Export

Comprehensive and Detailed Explanation: The Document Understanding template stages are based on a document processing flowchart that follows these steps:

First, you need to define the Taxonomy of the document types and fields that you want to process and extract information from.  This is done using the Taxonomy Manager in UiPath Studio 1 .

Next, you need to Digitize the input documents, which can be in various formats such as PDF, image, or text.  This is done using the Digitize Document activity, which converts the documents into a machine-readable format and performs OCR if needed 2 .

Then, you need to Classify the digitized documents into the predefined document types in your taxonomy.  This is done using the Classify Document Scope activity, which can use various classifiers such as Keyword Based Classifier, Machine Learning Classifier, or Intelligent Form Extractor 3 .

After that, you need to Extract the relevant information from the classified documents based on the fields in your taxonomy. This is done using the Data Extraction Scope activity, which can use various extractors such as Regex Based Extractor, Machine Learning Extractor, or Form Extractor.

Next, you need to perform Extraction Validation to review and correct the extracted information, either manually or automatically. This is done using the Present Validation Station activity, which can use either the Validation Station or the Action Center for human-in-the-loop validation.

Finally, you need to Export the validated information to the desired output location, such as a file, a database, or a queue. This is done using the Export Extraction Results activity, which can use various exporters such as Excel Exporter, CSV Exporter, or Queue Item Exporter.

References:

UiPath Studio - Taxonomy Manager

UiPath Activities - Digitize Document

UiPath Activities - Classify Document Scope

In UiPath ' s Document Understanding Framework , UiPath Document OCR is recommended as the initial OCR engine for processing documents. This OCR option is optimized for extracting text from a wide variety of document types and provides good accuracy with structured, semi-structured, and unstructured documents. It is designed to work seamlessly within the UiPath Document Understanding process and is highly compatible with other UiPath activities and models, making it a preferred choice for starting document processing in most projects.

For more details, refer to:

UiPath Document Understanding Framework : UiPath OCR Engines

UiPath Document OCR : UiPath OCR Guide

 Data Manager is a web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models. Data Manager enables users to create and manage datasets, label data, validate and export data, and monitor data quality and progress. Data Manager supports various types of data, such as documents, images, text, and tables.  Data Manager is integrated with AI Center, where users can train and deploy Machine Learning models using the datasets created or modified in Data Manager 1 2 .

References :  1 :  Data Manager Overview   2 :  AI Center - About Datasets

In UiPath Communications Mining , Dashboards are fully customizable pages that allow users to display relevant charts, visualizations, and insights for a specific dataset. These dashboards provide critical insights into the performance of models, labels, and other metrics relevant to the communication mining process. Users can create and adjust these dashboards to reflect specific data points, making them a powerful tool for tracking and improving model performance over time.

For more details, refer to:

UiPath Communications Mining Dashboards : Customizing Dashboards

UiPath AI Center Documentation : Dashboards and Visualization