1. Chatbots Best Practices & Design Patterns…
@rsantrod
@iamsoham

2. Design Patterns Best Practices Personas Flow-chart approach
Intent Separation
Entity Definition
Multi-language
Error Handling
Bot training
Instant apps
Channel agnostic design
Personas
Flow-chart approach
Multi-bot approach
Custom component approach
Best Practices

3. Cloud & Mobile Solution Specialist
Oracle ACE
Blogger ( & blog.avanttic.com)
Cloud and Mobile Consultant
UI Designer & Architect
Oracle ACE Associate
Tech Enthusiastic
Blogger & Writter (

4. The architecture and functionality of the Oracle Mobile Cloud enterprise
Build RESTful APIs using Oracle ADF BC as REST, and with Node.js
Deploy web services on Java Cloud Service and Application Container Cloud Service
Integrate different systems and building Mobile ready APIs using Mobile Cloud
An overview of Oracle JavaScript Extension Toolkit
Build hybrid applications using Oracle JavaScript Extension Toolkit
Create mobile-first Uis using zero- code platforms: Oracle VBCS and Oracle MAX
Build a Chatbot for your enterprise using Oracle Intelligent Bots Cloud Service

5. Chatbot terms I want to request an appointment for tomorrow at 12
Utterance
I want to request an appointment for tomorrow at 12
Intent
Entity
Introduce the terms we are going to talk about:
Intent, utterances, entities. Maybe channel

6. Best Practices

7. Best Practices: Intent separation
Separate intents.
Check out for utterances overlap
Use batch testing when new intent is added
Mixing of different languages within the same bot

8. Best Practices: Intent separation
Separate intents.
Check out for utterances overlap
Use batch testing when new intent is added
Mixing of different languages within the same bot

9. Best Practices: Intent separation
Separate intents.
Check out for utterances overlap
Use batch testing when new intent is added
Mixing of different languages within the same bot

10. Best Practices: Intent separation

11. Best Practices: Entity definition
System entities when possible
Custom entities can be defined

12. Best Practices: Entity definition
Regex enties

13. Best Practices: Entity definition
I want a medium thin pepperoni pizza
Size
Crust
Type
Pizza
Composite entities to define sub entities

14. Best Practices: Bot training
Normal training

15. Best Practices: Bot training
Batch training

16. Best Practices: Bot training
Batch training

17. Best Practices: Multi-language
Detect user’s language: translation services
Option 1: translation services

18. Best Practices: Multi-language
Answer in user’s language
Resource bundles

19. Best practices: Error handling

20. When you don’t need a chatbot
Mid/High Volume Search Activities
Form-filling
Login
Command Driven Activities
One doesn’t need chatbot. Think before you suggest chatbot as a solution to clients. It is innovative and cool but it is not supposed to solve all the problems in the world.
Some of the things for which might not be the answer :
Login activity
Form filling activity
Mid/High volume Search activity
Command driven activities for example smart home systems,

21. Best Practices: Instant Apps
Use for things that chatbot is not meant for, i.e., login, filling forms etc.
Use instant apps for these purposes.

22. Best Practices: Instant Apps
Use for things that chatbot is not meant for, i.e., login, filling forms etc.
Use instant apps for these purposes.

23. Best Practices: Channel agnostic design
Dont use card layouts, global links, location components. (for example WeChat doesn’t support that)
Specific properties in user object can vary from channel to channel, don’t design bot based on it.

24. Design Patterns

25. Bot – What & Who WHAT Pizza Home Automation Retail Leave/Timesheet
HR - Queries
IT Service Desk
WHO
Age group
Employee
Public
Channel
Automation
Bot

26. Bot – Personas – Know your User
Slightly older
3 kids
Not so tech-savy
Not a mulit-tasker
~Hardworker

27. Bot – Personas – Know your User
Slightly older
3 kids
Not so tech-savy
Not a mulit-tasker
~Hardworker
Middle aged
~1 kid
Average tech-savy
Finds his way around
Hardworking

28. Bot – Personas – Know your User
Slightly older
3 kids
Not so tech-savy
Not a mulit-tasker
~Hardworker
Middle aged
~1 kid
Average tech-savy
Finds his way around
Hardworking
Young
Extreme tech-savy
Well informed
Easily distracted
Excited about new stuffs

29. Channel - Selection Txt Chat Channel Properties FB Messenger WeChat
> 1.2 Billion active users.
WeChat
> 1.1 Billion active users.
In Web
Most accessible
In App
custom mobile app
Slack
Enterprise messaing, Collaboration tool. Used extensively in DevOPs
Skype 4 Business
Enterprise messaging
Alexa, Google Home
Audio device
Telegram
Dominant chat in some markets.
Text
Most used and most accessible. Limited interaction UI. Texts costed.
Txt

30. Design Patterns: Flow-chart approach
I have been scammed in marktplaats
Someone broke into my house
My car is stolen
Intents
FLOW
When/How
When/Where
When/Where
Entities
Requirement refinement
Bot’s boundary definition
Intent identification
Initial set of utterence definitions
Entity identification
Custom component requirements
Flow overview definition
File a report
File a report
File a report

31. Flow Chart Approach Advantages Requirement refinement
Bot’s boundary definition
Intent identification
Initial set of utterence definitions
Entity identification
API definitions
Flow overview definition

32. Multi bot approach Respond in user’s preferred language
Keep dialog flow same
Add languages easily

33. Multi-bot approach MyBot_nl Master Bot MyBot_es Detect Language
setVariable
Reusability
Develop-once Use everywhere
Agile approach
Extend your bot’s capabilities
MyBot_de

34. Design Patterns: Custom component approach
Bot Flow is comparatively small
Only NLP and Entity matching is used
Strong integration with Backend
Business logic intensive flow
Flexibility in bot platform
Wit.ai, Api.ai
Intent Match
Component A
Component B
Component C

35. Bot Maturity Model Level 1 Level 2 Level 3 Interaction Intelligence
API queries
Simple Q&A
One Language
One Channel
Word based rules
Menu based
Human to bot interaction
Links for more information
API transactions
Multi channel
Multi language
Line based intelligence
Training of NLP model
Bot initiates conversation
Event
producing
API intelligent queries
Mood detection
Context from channel
State machine
Human
Handoff
GEO
Self
learning
Multi person
Case
Process interaction
Bot-to-bot interaction
Conversation intelligence
B2B
Conversation listening
Historic
analysis
Interaction
Intelligence
Integration

36. Conclusions

37. Conclusions Separate Intents Define Entities
Use Batch/Run Report Regularly
Use Translation Services and Resource Bundles
Draw some flow diagrams
Define the boundaries
Take care of channel-agnostic features
Keep the flow fexible

38. Questions www.linkedin.com/in/rsantrod
@rsantrod
@iamsoham