Terminology

The approach used by IADA® to provide self-management of workloads, and which uses a combination of business and environmental variables to manage the prioritization of work, the timing of its execution, and the scaling of available resources to execute work to meet service level parameters.

The name given to objects created in the Blue Prism Cloud Platform which defines a set of configuration rules which specify how a digital worker or workforce should interact with an application by replicating human interactions.

The name given to the capability which provides a digital agent presented through a conversational user interface (CUI) via a messaging channel and/or speech interface, which is commonly known as a chatbot.

The name given to the repository containing common and reusable automation utilities and procedures which can be used as part of an automation workflow.

The web-based management and control interface for the Blue Prism Cloud Platform used by automation designers and platform operators.

A secure AES-256 bit repository for retention or encrypted access credentials which may be required by a digital worker in the execution of a process.

The name given to the resource within the platform which executes the defined automated processes it receives, interacting with the various local and remote applications, systems, tools, and devices required to complete the process. The digital worker is a Windows runtime, it is the execution layer and emulates the activities of a human. It takes a process, an item and the necessary credentials and follows the procedural steps to complete the defined task(s).

The name for the collection of algorithms and functionality which serves as the central Artificial Intelligence “brain” of the Blue Prism Cloud platform, supporting the interaction between all components.

A shared instance of IADA® which allows access to an evolving range of proprietary and third party cognitive services which can be called as actions from within a workflow and configured by any automation designer or business user.

A sub-component of IADA® which manages the ingestion of work from a variety of channels and sources, and the translation of that work into a format to be executed by the platform.

Intelligent Automation, also referred to as Intelligent Process Automation (IPA) is a term used to reference a combination of robotic process automation and artificial intelligence to provide a more capable digital workforce.

A sub-component of IADA® responsible for the orchestration of work across the digital workforce (see also Autonomic Workload Management).

An extension of IADA OCR which uses Machine Learning to manage variation of extracted data into a form which recognizes, structures and interprets that data, and learns from both historic activity (heuristics) and human input (governed learning) to improve over time.

A sub-component of the platform which manages the extraction and translation of information from document images into machine usable data using optical character and image recognition approaches.

A self-service web application which allows the custom presentation of a service cartilage and associated web forms to allow internal or external users to interact with automated processes, triggering execution, providing input and/or observing outputs.

The industry term which refers to the ability to understand, interpret, and process human language as it is spoken or written, as utilized by IADA Chat, iOCR and IADA.ai. Sub-components of the definition include Natural Language Understanding, Natural Language Analysis and Natural Language Generation.

The name given to the digital workforce technology, provided as a subscription-based Software as a Service (SaaS) onto which customers or partners build business process workflows to automate and digitize work.

The name given to an object-oriented workflow configuration which links decision logic, Application Objects and other Processes to create an automated and digitized work process.

Blue Prism Cloud has one product, the one product is the Blue Prism Cloud Digital Workforce®. The Blue Prism Cloud Digital Workforce is made up of a number of components and sub-components.

Robotic Processing Automation is process of a digital worker mimicking a human’s actions in completing a rules-based task.

Recovery Point Objective refers to the amount of data at risk. It is the time between data protection events and reflects the amount of data that potentially could be lost during a disaster recovery.

Recovery Time Objective refers to the amount of time taken to recover the environment in the event of a disaster occurring.

The technique used by an Application Object which connects to an application presented through Citrix, RDP or another thin-client technology by employing image recognition.

Blocks are used for a variety of purposes, these are:

• Grouping variables, e.g. Inputs, Outputs for easier readability
• Exception Handling, to facilitate handling exceptions thrown by different Stages
• To avoid infinite loops raised due Exception handling

A Business Object is an object that models the applications that the digital worker interacts with. It normally has multiple pages to simulate the functions of an application such as:

• Opening and closing an application
• Writing into fields
• Reading messages on screen
• Clicking on buttons and menu items

Data Items are containers for variables. They are where values are stored that can be used with automations, they can support different types, including:

• Date
• DateTime
• Flag
• Number
• Password
• Text
• Time
• TimeSpan
• Image
• Binary

A Process is intended to obey instructions performing a repetitive task. Those instructions could be extracting data for example from a spreadsheet. Alternatively, a Process might be designed to launch an application and check a diary for available slots for patient appointments.

A Queue Item is a job that is in a Work Queue waiting to be processed.

The Queue Item Key is a unique identifier of the Queue Item within the Work Queue.

The Stages are items that can be used within an automation to simulate the interactions of a human performing a task. The Stages are:

• Block
• Process
• Page
• Action
• Decision
• Choice
• Calculation
• Multi-Calc
• Data Item
• Collection
• Loop
• Note
• End
• Alert
• Exception
• Recover
• Resume

This is an “unplanned” exception typically caused by a failure of a server, service, network or infrastructure resource. These may be recoverable if the event recovers in a predictable time and the automation has waited for the resource to become available. Otherwise these are unrecoverable exceptions.

A Work Queue is an internal configurable list that enables a Process to manage its workload. A process can use different work queues and a work queue can be shared by multiple processes if required.

Custom made roles that include Hub plugins, roles will give different level of access to plugins.

A piece of work which is associated to a process, e.g. Joe Bloggs would be an item which is associated to the new starter process. You may have 5 items (5 people) that need to be onboarded, so the new starter process would iterate 5 times, one for each user. An item will also have a status associated with it, see Status.

Self-contained features that can be individually installed to provide information about your digital workforce.

A collection of procedural steps which allow the digital workers to follow a business process, e.g. a New Starter Process, allowing a digital worker to understand how a user is onboarded into the organization.

A container for items. Queues do not need to have an association to a process (new starter queue) but usually do.

A schedule is a rule around when a process should run and on what digital worker it should be run on. This creates a session per digital worker alignment.

Can be Pending, Locked, Completed or Exception.

Automatically generated roles based on plugins that have been installed. Roles are based on Blue Prism Cloud’s Center of Excellence and will give users certain access.

The application is a container for a specific process or group of processes. The NLU application is what a digital worker connects to, to have a data item processed in order to understand unstructured language. The input (data item) is referred to as an utterance. Intents and entities are returned to the digital worker or to a queue dependent on the process configuration.

There are four types of custom entity types within an NLU Application; Simple, List, Hierarchical and Composite.

A composite entity is made up of other entities that form parts of a whole. A composite entity can consist of up to 20 child entities. These count towards the maximum number of entities you may use.

A hierarchical entity defines a category and its members. It is made up of child entities that form the members of the category. A hierarchical entity can consist of up to ten child entities. These count towards the maximum number of entities you may use.

A list entity represents a fixed set of synonyms or related words in your process. Each list entity may have one or more synonyms. They are not machine-learned and are best used for a known set of variations on ways to represent the same concept. List entities do not have to be Annabelle in utterances or trained by the system. A list entity is an explicitly specified list of values. Unlike other entity types, a NLU Application does not discover additional values for list entities during training. Therefore, each list entity forms a closed set. Your NLU Application may use up to 50 list entities, and they do not count toward the maximum number of list entities you may use, which is 30. Each list can contain up to 20,000 items.

A simple entity is a generic entity that describes a single concept. These count towards the maximum number of entities you may use in your NLU Application. An example would be company, travel agency, and so on. “Book my travel through Gobibo travel agency.” or “Please book my travel with MakeMyTrip agency.” or “Arrange my travel with Yatra travel agency.” or finally “Prefer to book my travel through ClearTrip.”.

If intents can be described are verbs, then entities are nouns. Entities are parameters required to process the user request and may also be data items in their own right to be used within an automation. In the utterance, “I would like to change my address to my new residence in Swanley” “Swanley” is an entity of type location. By recognizing the entities that are mentioned in the user’s input, the NLU Application helps you choose the specific actions to be executed to fulfil an intent. A maximum of 30 entities is allowed in a NLU Application. Entity Types fall into two categories, Prebuilt and Custom.

You can add features to your NLU Application to improve its performance. Features help a NLU Application recognize both intents and entities by providing hints that certain words and phrases are part of a category or follow a pattern. When your NLU Application has difficulty identifying an entity, adding a feature and re-training can often help improve the detection of related intents and entities.

Intents are similar to verbs in a sentence. An intent represents actions the user wants to perform, commonly linked to a process (automation) on a 1-2-1 basis. It is a purpose or goal expressed in a user’s input, such as booking a meeting, making a council tax payment, or changing address. You can define a set of named intents that correspond to actions users want to perform in your Application. A Local Council NLU Application may define an intent named “Balance Check” which NLU processes from the utterance “I would like to understand the remaining amount on my account”. Another way of asking for the same intent may be “I want to check my account balance”, both utterances relate to the same intent. A maximum of 80 intents is allowed in a NLU Application.

These are built-in types that represent common concepts like dates, time, and geography. These do not count towards the maximum number of entities you may use in your NLU Application.

Training is the process of teaching your NLU Application by example to improve its language understanding. If you make updates by adding, editing, or deleting entities, intents, or utterances to your Application, you need to train your Application before testing and publishing. When you train a NLU Application, NLU generalizes from the examples you have labeled and learns to recognize the relevant intents and entities in the future, which improves its classification accuracy.

The level of confidence the technology has that the characters has been identified correctly.

Used to define a structure identifying specific fields or areas of text to extract information from.

The process to produce the output once extracted and verified.

The reading of information from the images identified using the Document Definition Template.

Used to identify/create the specific areas or regions within a scanned document requiring capture during the recognition process.

Location where the scanned images are sent prior to recognition.

The process of loading images from the Hot Folder.

Used to establish the profile and configuration for the specific project.

Used to control the overall process.

The process used to verify items that did not meet the 100% confidence level.

As the digital workforce completes different processes it builds up metrics on how applications perform at different times of the day. This information is then mined to understand the performance of individual applications to contribute to the algorithmic decisions within work selection.

In traditional RPA deployments, work is aligned to a queue and work is selected on a First In First Out (FIFO) basis. Cross-queue search allows all queues to be searched in parallel.

Provides access to the IADA® GetProcessPerformanceML method for parser response data.

A function available within IADA to allow a digital worker to select an item based on its importance. This function allows for cross-queue searching.

Based on the output from the application performance weighting data set, process performance weightings can be designed to calculate the most optimum time for a task to be executed.

The page an administrator is presented with upon login.

A holding area for a request that has been partially completed.

Elements that can be used within a form to capture information from the user.

An entity within Interact that hold a range of properties such as a fields, rules, roles, privilege checks, an association to a queue which is displayed on the dashboard.

The page a user is presented with upon login.

Dictates how the fields behave depending on user interaction.

Dictates the start-state of a field.

One or many pages within a form.

A value used to track the lifecycle of a request.