Cognitive Digital Agent
TM Forum Catalyst Program
2H2016
© 2017 TM Forum | 1
Content Outline
 TM Forum Catalyst Program Overview
 The Catalyst Project Team & Journey
 Problem Statement, Objectives, Key Benefits
 Digital Cognitive Agent Solution
 Key Learnings
 Contributions to TM Forum
 Readiness For Commercial Launch
 Next Steps
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TM Forum Catalyst Program Overview
What is it?
Why
participate?
Benefits to
Service
Providers
●
●
●
●
●
●
●
Rapid-fire, member-driven proof-of-concept projects (5-6 months)
Connecting service providers, technology suppliers, and global enterprises to create
innovative solutions to common industry challenges
Cost effective collaborative research & development including use of TM Forum assets
Ability to transform ideas into collaborative proof-of-concept projects
Close collaboration between service providers and suppliers yields high quality results.
Extended R&D arm
Recognition in contributing new best practices, frameworx assets (output from the POCs)
to the TM Forum community.
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The Catalyst Project Team
Project Champion:
Vincent Seet (Sponsor), Paul Prantilla (Project Lead), Jeff Ko (Project Manager),
Camille Cambronero, Kenu Cardino, Dean Valiente, Leandro De Guzman,
Janine Gallardo, Michael Recabar,
Participant 1:
Rohit Batra,
Mukund Nambirajan
Participant 2:
Richard Im, Louis Victor,
Richard Chang,
Zhang Guoping, Pippo Yu,
Daniel Ding, Rong Xiaolong,
Fusheng,
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Participant 3:
Ravi Kumar,
Guruprasad,
Srinivas Thonupunoori,
Ramesh Jallaram,
Ranjith Kumar Kodi
The Catalyst Journey

Onsite project team meeting in Manila to

Formed project team & initial ideation

Submitted catalyst proposal to TM
finalize project plan, scope, architecture
Forum
and integration points

Project was approved
 Catalyst demonstration in
TMForum Live! Asia
 Project implementation &
Singapore
testing, weekly project calls
Jun

Jul
Aug
Team representatives attended TM Forum Action
Week in Vancouver to pitch for the project and
gained acceptance from the catalyst committee
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Nov

Catalyst demonstration in
Globe
Dec
Problem Statement
Consumers Behavior Are Changing in Digital Economy
preferred source for product and service
information.
Mobile device is
customer information
center
2
G
Speed
171.2kbps
4
G
3
G
Speed
2Mb
ps
 Digital channels have already become the
 Customer experience is top strategic
Mobile
Smart
Personal and smart
interaction mode
Speed
100M
bps
performance measure, but has now fallen
for recently years in a row due to complex
IVR flow and too long wait time when
transfer to agent.
 Cost Optimization by use of new
channels, so the digital self-service
Social
channels are best way to reduce cost.
 Drive customers to use self-service
channels as primary means to address
their concerns while at the same time
provide them with a wonderful experience
Internet is the main channel for communication
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and ultimately manage customer care
service cost effectively.
Objectives of The Catalyst Project
The New Experience - Natural Conversation Flow over IVR/FB/Apple Watch
OBJECTIVES
• Prove the feasibility of the use of
cognitive computing technologies
to enhance customer experience
to make it more intuitive, natural
and ‘fun’ to interact with selfservice channels such as IVR and
portals.
• Provide an opportunity to
demonstrate how such cognitive
virtual customer care agent could
be used to augment the human
workforce.
Customer
Hi there! This is your friendly Globe agent.
How may I help you today?
Cognitive Digital
Agent
I would like to find out if I am eligible to
re-contract for my Globe mobile line.
Dear Shirley, thanks for being with us for the last 5
years. Yes, you are eligible to re-contract now.
What are the latest handset promos
and plans available?
●
●
You may walk in to the nearest Globe store
to complete the re-contracting procedures
with your chosen plan and handset. Thank
you for your continuous support!
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Key Benefits
 Enhance customer experience – with more human like response and various
online interaction channels such as the facebook, iWatch etc, this allows efficient
customer care interaction and resolution to solve customer problems quickly.
 Reduce agent cost – high customer care staff costs with repetitive requests
can be significantly reduced with automated responses by AI engine.
 Learning - With AI learning of customer behavior and patterns, the virtual agent
incrementally improves to improve the overall customer experience for reduced
costs.
 Natural language – free flow natural language can be recognized for a more
complex customer care support
 Open Integration – moving away from tightly coupled legacy systems to a more
open integration and decoupling of system functions for its internal flow, leading
to efficiency in touch points and automation across AI, CRM, IVR etc.
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Use Case Scenarios
COMMON & ESSENTIAL USE CASES (PER BUCKET)
POSTPAID
PREPAID
Billing Inquiries
New
Acquisitions and
Recontracting
◾ Account
Inquiries
◾ Promo Usage
Troubleshooting
◾ Promo Usage
Mechanics
◾ Account
Inquiries
◾
◾
FINAL USE CASES
BROADBAND
Validate
Outages
◾ Troubleshooting
◾ Account
Inquiries
◾
(when subscriber
loses load)
Facebook
Chatbot
Postpaid User asks when their current plan can be
recontracted. System identifies the kind of plan the user
has and answers the inquiry.
Postpaid User who has been temporarily disconnected
(due to credit chase) asks to be reconnected (fully if
credit is paid, or temporarily if there is only a promise to
pay).
PROPOSED CHANNELS
Menuless
IVR
a. User inquires the account balance.
b. User asks how much data, SMS and voice they have
left in their plan.
Apple
iWatch
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Postpaid User asks for upcoming phone models and
how much down payment / cashout is needed.
Project Team’s Areas Of Contribution
Project Champion:
Facebook
Chatbot
Participant 1:
CRM
Participant 2:
Menuless
IVR
Softphone, IPCC,
Voice recognition
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Participant 3:
Apple
iWatch
Natural Language Processing,
Machine Learning
Solution Architecture
Huawei
IVR/IPCC
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Account Balance ( iWatch)
Speech to Text
conversion done
by apple watch
for user
command
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App response in
text
Data/Call/SMS Balance ( iWatch)
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FB Channel Interaction
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FB Channel Interaction
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FB Channel Interaction
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Key Learnings
Digital channels are more readily to be integrated to AI than legacy
channels such as IVR
Machine learning is achieved in a 2-step process
Lack of standardized APIs for channels to integrate with AI hence a
channel wrapper API is developed
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Key Learnings
Digital channels are more readily to be integrated to AI than legacy
channels such as IVR
Traditional IVR Setup (without AI)
2. Speech Stream
(MRCP)
1. User’s
speech
6. Play
response
IVR
5. Process intent &
convert result to
speech
Ideal IVR Integration With AI
1. User’s
speech
ASR
4. Return
intent
3. Recognize speech and
match to intent using
predefine grammar file
2. Speech Stream
(MRCP)
IVR
8. Play
response
7. Return result in
text and IVR
converts to speech
ASR
4. Return recognized
speech in text
5. Pass on
the text
AI
3. Recognize speech
and convert to text
only
6. Interpret text, determine
intent and process request
 IVR/ASR vendors would need to enhance their solutions and disable intent interpretation on ASR so as
to pass the recognized speech to AI for interpretation. This might need MRCP to be modified.
 Digital channels such Facebook chatbot and Apple iWatch on the other hand simply passes the user
raw input (text or speech) by default for interpretation by AI.
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Key Learnings
Machine learning is achieved in a 2-stage process
 Training module and Self-learning capabilities have been implemented in linguistic model.
 Stage 1: Use the training module to feed the initial training set for AI to understand the user utterance
and intents
 Stage 2: Self-learning module is then used to handle the user utterances in effective way when the
classified intent accuracy factor is less. In this case, AI clarifies the intent with the user and eventually is
able to determine the intent. AI then learnt this new user utterance.
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Key Learnings
 Below screen shots from Linguistic model demonstrates the UI for intent creation and initial training
Training the AI for Intent
Intent creation
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Key Learnings
More On Machine Learning Implementation:
 Based on reference AI architectural model, linguistic model has been built to define user intents related
to Balance enquiry, Upcoming phone models, re-contract and re-activate scenarios.
 Extensible, re-usable and loosely coupled integration layers and API’s have been built to integrate with
any consumer channels as well as any backend enterprise applications.
 Scalable user session management and security through tokenization for each channel has been
defined and implemented.
 Platform for analytics integration has been created through persistence of user requests, responses and
feedback.
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Key Learnings
Lack of standardized APIs for channels to integrate with AI hence a
channel wrapper API is developed
 The disparate channels use different APIs to send requests to AI for interpretation. This setup would not
be sustainable as variety and complexity of channels evolve. Hence, a wrapper API was developed to
shield the AI from the complexity in the channel integration.
 No changes to the AI is required
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Contributions To TM Forum
AI Reference Architecture & Guidelines
Customer Experience Use Cases Enhanced By AI With Detail Endto-End flows
New APIs for consideration by OpenAPI program to manage AI
conversations
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Contributions To TM Forum
AI Reference Architecture & Guidelines
Access Mechanism
The
reference
architecture
provides a generic model and
guidelines to build linguistic
models for Enterprise or Consumer
facing applications. The framework
lets
users
create
linguistic
intelligence for applications by
allowing them to define intent and
entities, configure pre-built entities
and subsequently bind each intent
with associated actions.
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Backend Applications
Contributions To TM Forum
 AI reference architecture and guidelines continued…
The picture on right depicts
the guidelines which can be
followed to build a robust
Artificial
Intelligence
Linguistic frame work.
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Contributions To TM Forum
Customer Experience Use Cases Enhanced By AI With Detail Endto-End flows
Use Case 1: Account Inquiry
Use Case 2: Recontracting
• User inquires the account balance.
• User asks how much data, SMS and voice they
have left in their plan.
Use Case 3: Line Reconnection
Postpaid User who has been temporarily
disconnected (due to credit chase) asks to be
reconnected (fully if credit is paid, or temporarily if
there is only a promise to pay).
Postpaid User asks when their current plan can be
recontracted. System identifies the kind of plan the
user has and answers the inquiry.
Use Case 4: New Phone Promotions
Postpaid User asks for upcoming phone models and
how much down payment / cashout is needed.
The respective Frameworx eTOM processes, TAM functions could be updated based on the enhancements with AI.
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High level workflows(Use case 1&3 IVR)
A
A
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High level workflows(Use case 1&3 Facebook &
Chatbot)
A
A
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High level workflows(Use case 2&4 IVR)
A
A
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High level workflows(Use case 2&4 Facebook &
Chatbot)
A
A
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Contributions To TM Forum
New APIs for consideration by OpenAPI program to manage AI
conversations
REST API specification for Tokenization/Authentication
Title
URL
Method
Authorization
https://<host-name>/iBot/getToken
URL Params
N/A
getToken
Data
Parameters
Common REST API specification for multiple channels to interact with AI
Title
URL
Method
URL Params
Data
Parameters
Content-Type: application/json
{
"userName":<userId>,
"password":<password>
}
Response
Response
{
"userName“ : “<user>",
"token“
: “<Token>",
"validPeriod": <YYYYMMDDHHMMSS>
}
API specifications for Tokenization and conversations with AI
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Conversation
https://<hostName>/iBot/secure/conversation
conversationAPI
NA
Content-Type : application/json
{
"type"
: "message",
"id"
: <Message Id>.,
"channel" : <facebook/IVR/Watch>,
"user" : {
"id"
: <MSISDN/PSID>,
"name" : <User Name>
},
"text"
: <User Text>,
"time"
: <Current Time>
}
{
"type"
: "message",
"id"
: <Message Id>.,
"channel" : <facebook/IVR/Watch>,
"user" : {
"id"
: <MSISDN/PSID>,
"name" : <User Name>
},
"text"
: <User Text>,
"time"
: <Current Time>
“response” : <AI response>
}
Readiness For Commercial Launch
Demonstrated AI Maturity In Enhancing Customer Experience – natural
language processing and machine learning provided a more immersive experience
for the customers and make it more fun for them to interact with service providers.
Potential Business Value That Could Be Realized – we estimate the customer
care agent costs could be reduced by 10% in 1-2years, and we could expect this to
increase to around 40% within 5yrs. This equates to around 5M-15M USD per year
in the APAC market. Other additional value is the operational efficiency achieved
with integration and automated processes for increased customer experience and
additional sales.
Time-to-Market – A production rollout is expected to be around 1-2 months given
the same use cases deployed for this catalyst project.
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Recommendation Of Next Steps
Convergence – we expect that AI to Analytics will converge in the
industry where there will be synergy between Customer Care to
new sales driven by AI engine across various customer channels
and internal system integrations
Digital Interaction – new digital channels and disruptions
expected with VR, Augmented Reality, Robotics etc. to be
considered going forward.
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Recommendation Of Next Steps
Develop AI Best Practices – TM Forum could consider initiating
a new track in the collaborative R&D to focus on the use of
cognitive computing technologies in the telecommunication
industry.
The track could focus on setting up a framework which would
guide the use of such technologies which could consist of
reference architecture, use cases, maturity model, business
metrics and principles.
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and the winner is…
Cognitive Digital Agent
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Catalyst Demonstration
Cognitive Digital Agent
Champion: Globe
Participants: Amdocs, Huawei, Infosys
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Catalyst Demonstration
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