Google Dialogflow CX

Google Dialogflow CX is a third-party platform that provides voice and chat virtual agents. Virtual agents interpret what your contacts say or type in the chat window and respond appropriately. They do this using technologies such as:

• Speech-to-text Also called STT, this process converts spoken language to text. (STT)
• Text-to-speech Allows users to enter recorded prompts as text and use a computer-generated voice to speak the content. (TTS)
• Natural language processing Also called NLP, this process understands human speech or text and responds with human-like language. (NLP)
• Artificial intelligence (AI)

Virtual agents are flexible and can provide a range of functions to suit the needs of your organization. For example, you can design your virtual agent to handle a few simple tasks or to serve as a complex interactive agent.

CXone supports using Google Dialogflow CX with voice and digital chat-based channels. For voice channels, you can set up the channel in Virtual Agent Hub or using a SIP Protocol used for signaling and controlling multimedia communication sessions such as voice and video calls. backchannel connection, which happens outside of Virtual Agent Hub.

Comparison of Google Dialogflow CX and ES

CXone supports Google Dialogflow ES and CX. The two versions are similar, but have some key differences.

Dialogflow ES is suitable for small, simple bots. It simulates nonlinear conversation paths using a flat structure of intents and context as a guide. This approach doesn't support large or complex bots. You can pass contexts using the customPayload property of the Virtual Agent Hub Studio action used in your scripts. These bots use context data to determine the contact's intents.

Dialogflow CX supports complex, nonlinear conversational flow suitable for large, complex bots. It allows intents The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish to be reused and doesn't require contexts. You can pass customPayload data, but you don't need to include contexts.

Conversation Flow for Voice and Text Virtual Agents

The beginning of the conversation is different for voice and text virtual agents: 

• For voice virtual agents, contacts The person interacting with an agent, IVR, or bot in your contact center. call a phone number and reach your organization. The contact may be connected directly to the virtual agent, or they might need to choose an option in an IVR Automated phone menu that allows callers to interact through voice commands, key inputs, or both, to obtain information, route an inbound voice call, or both. menu. Once the conversation with the virtual agent begins, the contact's utterances What a contact says or types. are transcribed Also called STT, this process converts spoken language to text. into text so the virtual agent can analyze them. The virtual agent's responses are converted to synthesized speech using a text-to-speech Allows users to enter recorded prompts as text and use a computer-generated voice to speak the content. service before being sent to the contact. Transcription and speech synthesis can happen in CXone or, in some cases, in the provider's platform.

• For text virtual agents, contacts start a conversation using a chat window. The location of the chat window varies depending on the specific channel A way for contacts to interact with agents or bots. A channel can be voice, email, chat, social media, and so on. you use. For example, it might be located on your website or the contact could start the conversation from the location of a third-party service, such as Apple Messages for Business. The contact's utterances are already in text format, so they don't need to be transcribed. They're passed on directly to the virtual agent.

After the conversation has started, the virtual agent analyzes the contact's utterances to understand the purpose or meaning behind what a person says. This is known as the contact's intent. When the intent is identified, the virtual agent sends an appropriate response to the contact. There are two methods of sending responses. Voice virtual agents can use either method, but text virtual agents can only use one. The methods are: 

• Exchanges flow through Virtual Agent Hub: Requests and responses are sent via Virtual Agent Hub and the script with each turn. This option allows for customization of the virtual agent's behavior from turn to turn. For voice virtual agents, this is the utterance-based method of connection. All text virtual agent providers use this method.
• Conversation directly between contact and virtual agent: Requests and responses are sent back and forth between the virtual agent and the contact. CXone stays connected to the virtual agent service throughout the conversation, but does not participate in it. CXone waits for the signal that the conversation is complete or that the contact needs transferred to a live agent. For voice virtual agents, this is the SIP Protocol used for signaling and controlling multimedia communication sessions such as voice and video calls. backchannel method of connection. No text virtual agent providers support this option.

At the end of the conversation, the virtual agent sends a signal to the Studio script. It can signal that the conversation is complete, or that the contact needs to speak with a live agent. If the conversation is complete, the interaction ends. If a live agent is needed, the script makes the request. The contact is transferred to an agent when one is available.

Once the conversation is complete, post-interaction tasks can be performed, such as recording information in a CRM Third-party systems that manage such things as contacts, sales information, support details, and case histories..

Prerequisites

To use Google Dialogflow CX virtual agents A software application that handles customer interactions in place of a live human agent. with CXone, you need:

• A Google Cloud Platform account.

• A Google Dialogflow CX virtual agent configured and trained to provide responses to your contacts' requests. To complete integration in CXone, you need the virtual agent name from the virtual agent's settings in the Google Dialogflow CX console.

Alpha Visibility in Google

Alpha visibility is a Google program that provides Google Cloud Projects access to features that aren't otherwise available. Alpha visibility is not required to use Dialogflow CX with CXone. However, there is one case when you may need to have alpha visibility enabled.

Alpha visibility is needed to have the last user utterance returned from the Dialogflow virtual agent along with the intent information. You can view this information in a script trace. If the lastUserUtterance variable is empty when it should contain data, alpha visibility may not be enabled for your project. If you require this information, your Google Cloud project must have alpha visibility enabled.

Components of an Integration

The integration of Google Dialogflow CX involves the following components: 

• CXone: CXone must have a configured voice channel A way for contacts to interact with agents or bots. A channel can be voice, email, chat, social media, and so on. to use with the integration.
• Virtual Agent Hub in CXone: Virtual Agent Hub holds the configuration information for connecting to your virtual agent A software application that handles customer interactions in place of a live human agent. provider. This includes choosing the options you want to use for TTS Allows users to enter recorded prompts as text and use a computer-generated voice to speak the content. and transcription Written form of a voice or digital interaction, if your integration requires these services.
• Studio scripts: You need at least one script that includes a virtual agent Studio action. The action must be configured with the connection information for your virtual agent. The point of contact for the channel you're using with the integration must be configured to use this script.
• Google Dialogflow CX: Your virtual agent must be fully configured in the provider's platform. See the prerequisites on this page for any specific requirements.

Rich Media Support for Text Virtual Agents

If your channel supports it, you can include rich media Elements in digital messaging such as buttons, images, menus, and option pickers. content in the messages. The type of rich media that can be sent differs from channel to channel, as shown in the following table.

	Adaptive Cards	HTML & Markdown Text	Rich Link	Quick Replies	List Picker	Time Picker	Form message
Apple Messages for Business
Digital Chat
Email				Uses fallback text
Facebook Messenger
WhatsApp
Google Business Messages

Supported:

Not Supported:

Learn more about digital channel support for rich media.

When you want to include rich media content in text virtual agent responses, configure it in your virtual agent's management console. It should go in the configuration for each response that will send the rich media.

Rich media content is sent as JSON. When building your rich media JSON, follow the schema for the digital channel you're using. The schemas are different for each channel. Find the JSON for the media content you want to use, then add it to the response message configurations that you create in AmeliaAmazon Lex V1Amazon Lex V2CognigyGoogle Dialogflow ESGoogle Dialogflow CXMicrosoft AzureIBM WatsonNuance MixOmiliaSalesforce EinsteinServiceNow your virtual agent provider's configuration console. Learn more about working with rich media in Studio scripts. You can use the Digital Experience JSON mirror tool to verify your JSON before adding it to your scripts or virtual agent.

Conversation Transcripts

You can capture the transcript and intent information from Google Dialogflow CX voice conversations. If you use a SIP backchannel connection with Dialogflow CX, this option is not available for you. You can use the captured data in any way you want. For example, in cases where an interaction is transferred to a live agent, you could display it for that agent. Another option could be to save it as a permanent record of the conversation. You can choose to capture just the transcript, just the intent information, both, or neither.

If you want to capture this information, you must enable it in the Google Dialogflow CX configuration settings in Virtual Agent Hub. You must also configure a Studio script used with your virtual agent. The script must include a action configured to manage the captured data. Captured data is stored temporarily for the life of the contact ID. If you need to save it, you can configure the script to send it to an archive. You are responsible for scrubbing all saved data for PII (Personally Identifiable Information).

Contact Center AI Insights

If you use Google Dialogflow Contact Center AI Insights, you need to make an additional configuration to your Studio script. The Contact Center AI Insights feature only works on conversations that have been marked complete.

By default, it takes 24 hours for Dialogflow CX virtual agent conversations to be marked complete. However, you can force them to close by sending an automated intent to Dialogflow at the end of each interaction.

To do this, you need to send the value conversation_complete through the AutomatedIntent property of the Voicebot Exchange action or the Textbot Exchange action after the interaction has ended. You can hard-code this value in the property, or you can send it via a variable.

Speech Context Hints

Speech context hints are words and phrases sent to the transcription service. They're helpful when there are words or phrases that need to be transcribed a certain way. Speech context hints can help improve the accuracy of speech recognition. For example, you can use them to improve the transcription of information such as address numbers or currency phrases.

If you want to use speech context hints, you must add them to your script. Dialogflow speech context hints are sent in the custom payload. You must include two parameters: 

• speechContexts.phrases: The Google class token for the hint you want to give. The token must match the language and locale of your contacts. If you want to add multiple tokens, add a speechContexts.phrases parameter for each token.
• speechContexts.boost: A weighted numeric value between 1-20 to the specified phrase. The transcription service uses this value when selecting a possible transcription for words in the audio data. The higher the value, the greater the likelihood of the transcription service choosing that word or phrase from the alternatives.

For example:

DYNAMIC customPayload
customPayload.speechContexts.phrases="$OOV_CLASS_ALPHANUMERIC_SEQUENCE"
customPayload.speechContexts.boost=10		

You can see the contents of this parameter in Studio traces and application logs.

Custom Scripting Guidelines

Before integrating a virtual agent The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish, you need to know: 

• Which script you want to add a virtual agent to.

• The virtual agent Studio action you need to use.

• Where the Studio actions must be placed in your script flow.
• The configuration requirements specific to the virtual agent you're using.
• How to complete the script after adding the virtual agent action. You may need to: 
  • Add initialization snippets as needed to the script using Snippet actions. This is required if you want to customize your virtual agent's behavior.
  • Re-configure the action connectors to ensure proper contact flow and correct any potential errors.
  • Use the OnReturnControlToScript branch to handle hanging up or ending the interaction. If you use the Default branch to handle hanging up or ending an interaction, your script may not work as intended.
  • Complete any additional scripting and test the script.

You may be able to obtain template scripts for use with virtual agent integrations from NICE CXone Expert Services. If you need assistance with scripting in Studio, contact your CXone Account Representative, see the Technical Reference Guide section in the online help, or visit the NICE CXone Community site.

Studio Action for Voice Virtual Agents

The Voicebot Exchange action is for complex virtual agents, or for when you need to customize the virtual agent's behavior from turn to turn. It monitors the conversation between the contact and the virtual agent turn by turn. It sends each utterance What a contact says or types. to the virtual agent. The virtual agent analyzes the utterance for intent The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish and context and determines the response to give. The action passes the virtual agent's response to the contact. When the conversation is complete, the action continues the script.

If you want to configure barge in or no input, additional scripting is required.

Studio Action for Text Virtual Agents

The TextBot Exchange action is for complex virtual agents or for when you need to customize the virtual agent's behavior from turn to turn. It monitors the conversation between the contact and the virtual agent A software application that handles customer interactions in place of a live human agent. turn by turn. It sends each utterance What a contact says or types. to the virtual agent. The virtual agent analyzes the utterance for intent The meaning or purpose behind what a contact says/types; what the contact wants to communicate or accomplish and context and determines which response to give. TextBot Exchange passes the response to the contact. When the conversation is complete, the action continues the script.