Build a pragmatic chatbot in 5 days. By this case study, we know introduce chatbot is a digital transformation which same application category as robotic process automation. Asset-based agile approach is crucially to make chatbot implementation success. Build and leverage process and service inventory, application and user interface instead of reinvent them.

1. Build a pragmatic chatbot in 5 days
- An essential chatbot in IT daily operation
Jesse Wei
web.jesse@gmail.com
2018-01-18
version 0.9
OP
DEV

2. Agenda
 Objective
 Day 1 – Inception
 Day 2 – Elaboration
 Day 3 – Construction
 Day 4 – Transition
 Day 5 – Sprint2(IECT)
 Retrospective

3. OBJECTIVE
Quick and cost save

4. Requirement background
 A service fulfillment system in life insurance
company, the batch system run >6000 jobs
daily
 Required daily operation support to
collaborate multiple development and
operation team in more effective method

5. Requirement
 Application
 Platform
 Approach to Success
 Non stop service
 Push required
information by role
 Pull information by
human machine
interactive interface
 Process automation
support
 Automatic sharing
information by
role/group

6. Requirement
- Application: Command Console Consolidation
DEV
SIT UAT
PROD
 System user interface
consolidation
 Group information
consolidation
 On line coach mode
 Role base virtual
group

7. Requirement
 Application
 Platform
 Approach to Success
 Conversational
platform
 training
 analytics
 Collaboration platform
and design
 push/pull
 trust circle and coach
 Develop tools
 Networking tuning

8. Requirement
 Application
 Platform
 Approach to Success
 Asset-based Agile
Approach
 Behavior-Based
Building tool
 Cognitive
Communication Cloud
 Domain-Driven
Design

9. Methodology
- realized based on agile approach
Approach Output/Ref
AAA Build asset/service context map • Asset Context Map
• ref: ABCD
BBB Build behavioral specification in
ubiquitous language
• Behavioral specifications
• ref: BDD
CCC Map to intent/entity/context
object
• Dialog flow
• ref: BBR
DDD Refine or build micro service
interface
• Program spec.
• ref: DDD

10. Summary in Objective
- The Chatbot
is
 new Architectural Pattern
 with Ubiquitous Language
 with Learning Mechanism
 support Robotic Process
Automation
 new Service Interface
 with Multi-channel support
 with Extendable Integration
and Fulfillment service
is not
 new Application
Why reinvent wheel?

11. Robotic Process Automation:
= Autonomation
 Autonomation(自働化) = user + control + management
 Static routing
 Fundamental = ((user + control) + management)
 ex.
 Process control: integrated control point to process
 sales process = (inv shipping(-INV/+COGS) + AR(+AR/-Rev) })
 Content-based routing
 Supplemental = (user) + (control) + (management)
 Ex.
 PO Approval hierarchy
 Intelligent routing
 (user) + (control + management)
 Dynamic and intelligent routing by objective, leverage machine learning
 Ex.
 Load balance
 Skill based/Sentiment routing
11

12. DAY 1 – INCEPTION
Asset-based Agile Approach

13. Asset-based Agile Approach
 Process inventory
 Process transformation
 Service interface
 Service survey
 Service package
 Leverage current CLI/API into service interface
 Asset/Service Inventory Integration
 REPL tools: Jupyter
 Build by snippet

14. BEFORE

15. BEFORE
- Service Boundary
Transform From Human to Robot

16. AFTER
- System Boundary, get and dispatch message by iwsChatbot

17. Messaging by level
- Trust Circle
PMO
OP
DEV
• Executive
• Stage Level Message: Progress
• Operational
• Detail Level Message: Error/Priority
• Develop
• Detail Level Message: Error/Log
 Trust circle
 3 Cycle depend liability /
 Define message level
 Management and control
 Information cost
 Coach cost

18. Three pig model for multiple trust circle
Traditional trust circle
PMO
OP
DEV
with chatbot
PMO
OPDEV
* Trust circle is an low transaction cost institutional arrangement to implement
- Democratic centralism, or Nemawashi
- The Pig is people who committed

19. Summary in AAA
Push
 Just follow LINE
developer guide
import requests
def send_line_msg(headers, data):
……
def pln_get_jobsts(job_ptn, site=None):
…..
Pull
 Webhook get through
NAT

20. DAY 2 – ELABORATION
Behavior-Based Building method

21. Behavior-Based Building method
Push
 Proactive
 System information
 Subscribe by role
 Drill down by interactive
service
Pull
 Interactive
 Link and integrate with
push message
 Authorize service by role

22. Traditional Trust Circle
PD
PM
Owner
Developer
Key
User …
Leader …
DeveloperOperator TeamPMO
Operator

23. Before Process Automation
PD
PM
Owner
Developer
Key
User …
Leader …
DeveloperOperator TeamPMO
Operator
Job Scheduler

24. Before Process Automation
PD
PM
Operator
Key
User …
Leader …
Owner
Developer
DeveloperOperator TeamPMO
Job Scheduler

25. Before Process Automation
PD
PM
Operator
Key
User …
Leader …
Owner
Developer
DeveloperOperator TeamPMO
Job Scheduler

26. Before Process Automation
DeveloperOperator TeamPMO
PD
PM
Operator
Owner
Developer
Key
User …
Leader …
Job Scheduler

27. AFTER
- System Boundary, get and dispatch message by iwsChatbot
PMO
OP
DEV

28. Process inventory
- Management Flow: Data Stage
28
D_ODS_CLM
O_CLAIM_CASE O_CLAIM_CURE …
M_ODS_CLM
O_CLAIM_LIAB O_CLAIM_PAY …
D_SDM_CLM
SDM_CLAIM_CASE …
D_RPT_CLM
CLM_0170_11G CLM_0170

29. Process inventory
- Management Flow: job status
Process
Flow
Project TGL
ODS
STAGE ODS
SDM
SDM
RPT
RPT CR CF ML
Template
PROCESS
ODS Template
STAGE
STG_SRC.bat
STG_SRC.sql
STG_SRC.ddl
ODS
ODS_SRC.sql
ODS_SRC.ddl
BSD ID = RPT GROUP
SDM
SDM_CLM.sql
SDM_CLM.ddl
RPT
CLM_001.sql
CLM_001_11G
CLM_001.ddl
OUT
CLM_001_1.rpt
CLM_001_2.rpt
ARC
CLM_001_1.pdf
CLM_001_1.ctl
CLM_001_2.pdf
CLM_001_2.ctl
TAT
JOB = Prog= File JOB = Prog≠ File
Smart
PlanDynam
ic
Schedu
le
Auto
Contro
l
Depend
ency
Inversio
n

30. Process inventory
- Job Stream View
30

31. Process/Asset/Service inventory
Command Purpose Comment
iws_plan.sh Replan after JobPlanner
iws_reset.sh Clear IWS object and replan
iws_restore.sh Restore object from *.log under
1020.ETLSYSods_planner
iws_showdup.sh Show job duplicate
iws_showerr.sh Show job log message when error
iws_backup.sh Backup current IWS object Back up to *.log
Iws_run.sh Run plan in specified data date
Iwsimport.sh Import plan base into IWS
iwsplandate.py Set ETL_DT in variable table
iwssetcalendar.py Set ETL_DT in variable table
Set runcylce to weekly
Set start time = current time – 1min
iws_showstatus.sh Show job status
iws_showlog.sh Show job log …

32. iws Chatbot sequence diagram
Workload Scheduler Chatbot
User
iwsBot
BOT ASST Scheduler Messaging Conv platform prebuild Agent
Start jobs_poll
Show Job Stage
showjobs
Status reply
Push message
Line message
Show Error
showjobs
Status reply
Push message
Line message
Push message
Query Status
Query status
Query intent
Default conversation
Intent status
showjobs
Status reply
Reply message
Reply message
Line message
Pull(Interative)
http://msc-generator.sourceforge.net v6.1

33. Class/Instance Tree
IWS Chatbot
iwsASST
PULL
UnitTest Production
iwsBOT
PUSH
Unit Test
UT
site=DEV
msg=Y
Operating
OP
site=*
msg=Y
External
EX
site=*
msg=N
Site Group
MessageLevel IM*

34. BBB
- Domain Language Layer
Cognitive
Communication
Ubiquitous
Language
Service
• Smalltalk
• Sentiment
• Personality Insights
• Domain Model
• Bounded Context
• Domain
• Group
• Predefined Domain
Agent

35. Summary in BBB
Behavior-Based
 Mapping Dialog to
Behavior-based model
ex. flask-assistant
from flask import Flask
from flask_assistant import Assistant, ask,
tell
app = Flask(__name__)
assist = Assistant(app, route='/')
@assist.action('greeting')
def greet_and_start():
speech = "Hey! Are you male or
female?"
return ask(speech)
if __name__ == '__main__':
app.run(debug=True)

36. DAY 3 – CONSTRUCTION
Cognitive Communication Cloud

37. Context Free Intent
Dialog flow
Context
welcome
show datadt
show
datadt_site
site
show_cmd
showlog
showlog job
showstatus
showstatus
job
show_status run_cmd
watch
watch_job
rerun
rerun_job
submit
submit_job
Fallback
help escape
smalltalk
env-focused
site command
jobname

38. Domain Driven Design service
DDD
Service
API
CLI
GUI
NLI
 Service
 API: Application Interface
 CLI: Command Line
Interface
 GUI: Graphic User
Interface
 NLI: Natural Language
Interface
 Conversation with context
and guided interaction
 Ubiquitous Language
support

39. DDD Service model
Multi-Source
Microservice
API my service
and service broker
API CLI GUI NLI VR
Personality
Tone
Small
Talk
NLI
Multi-
Lang
Multi-
Chan
UI
API CLI

40. DAY 4 – TRANSITION
Domain-Driven Design

41. DDD
- Function layer for chatbot
Project /
Client
Template /
Product
Toolkit
• Conv. Training
• Service Integration
• Service Development
• Agent Integration
• Domain Intent/Entity
• Domain Context/Dialog
• Domain Welcome/Fallback
• Domain Service Interface
• Domain Prebuild Agent
• Conv. Platform
• Bot Framework
• IM
• Service Interface
• Learning Engine

42. CCC
- Chatbot component
Application
UI
CLI
API
NLI
Domain Service
Management Control User
Consumer
Appliance /
Console
PC
Mobile
Message
Social
Media
Web

43. CCC
- iwsChatbot component
Workload Scheduler
DWC
CLI
API
iwsBot
Job Plan
Job Status
Dynamic
Dependency
Data job
Report job
Export
Google Home
PC
Mobile
Line
Slack
Web

44. Demo
- Line/Slack
Line Slack welcome

45. For PMO group
Status report by push Drill down when needed

46. For OP and programmer group
Status and error msg Show detail log and rerun
Show error with owner and service level Inquiry detail log and ask action

47. Messaging Statistic
- last 30 days
Push
 Internal: 10~200+
 External: 6~35
Pull

48. Push message sample
- Messaging by channel
Internal: for Programmer External: for PMO
Common
For progr only

49. Demo Access
 Personal
 http://line.me/ti/p/@yyyyyy
 Pull(interactive)
 Group
 http://line.me/R/ti/g/XXXXXX
 Push/Pull(interactive)

50. DAY 5 – SPRINT2 (IECT)

51. PDCA
 Training console is
critical to improve
conversation interface
 Feedback is essential
 Add command by priority
 For example, Add natural
language interface to
Show dependency is
critical to OP
 Intent Analysis, to
improve resources
allocation

52. Intent Analysis

53. RETROSPECTIVE
Learning By Doing, Learning By Sharing
做中學, 學分享

54. Lesson Learnt in platform
 Fundamental
 Isolate interactive flow with application logic
 fulfillment SDK
 message API
 language/ framework
 training toolset
 analytics
 Channel independent
 multiple channel
 Advanced
 Device/Service plug-in
 assistant framework on device
 Service Marketplace
 prebuilt bot service

55. Lesson learnt in develop
 Intent design principle
 Single responsibility principle (SRP)
 ! – escape, ? – help
 Open Close principle (OCP)
 show log, show status: two different service interface
 Ubiquitous Language design
 Don't mess up engine
 sys.any is the last choice
 Intent group as language-based not service-based
 ex. show log, show status should integrate into one intent
 Track source code
 Find API, like lifespan=1

58. Business Data Store
Case study: Insurance: Data Map
Operational
applications
CONTRACT
GI
TI
FIN
CLM
080
Operational
repositories
Oracle
AS400
SQL server
Data
acquisition
Change
Data
Capture
File
Staging
STAGE
SRC
ODS
ODS
Summary
Data
Mart
SDM
Data
Mart
RPT
FDW
Speed Batch

59. Steps to add command
App flow
python Flask
Microservice
bash IWS
Dialog Train And Test
json Conv Console
Context
json Conv Console
Intent / Entity
json Conv Console
 Dialog pilot
 Intent
 Entity
 Context
 lifespan
 Train And Test
 App
 Integration test

60. Why NLI
 More flexible interface to
multiple channel
 Social media
 Mail
 Service chain
 Context sensitive service
 Lower cost with higher service
quality
 Consistence
 Flexible integration
 Dynamic Dialog by learning
 Difference service/system
 Conversion or Legacy
 KPI
 Service API vs. Improve KPI (or
Learning KPI)
 Learn how to learn
 Benefit
 Familiar interactive
model
 Easy to access service
 Access channel
without limitation
 Issue
 Converge
 V&V, or Test
 UT/SIT/FIT
 UAT

61. Approach Roadmap by phase
Inception Elaboration Construction Transition
AAA Asset
Service context
BBB Requirement Specification
CCC Intent
Entity
Dialog
Fulfillment
Chained agent
DDD Language Context driven Testing

62. Basic component
 Platform
 LINE/Slack
 Conversational platform
 Tools
 http://requestbin.net/
 Flask assistant
 Snippet
 Push
 Pull

63. Contingency change management
Authorization
Capability
Willing

64. Chatbot is kind of RPA
 Robotic process automation (or RPA)
 is an emerging form of business process automation
technology based on the notion of software robots or
artificial intelligence (AI) workers.
 Ref to wiki: RPA
 It is a process automation case by robot, but
only NLI and no UI
 Similarity : In methodology viewpoint, it is.
 Different: In current tool category viewpoint, it is not.