The document describes a partnership between Welltok, Inc. and IBM Watson to create CaféWell Concierge, a consumer health optimization solution leveraging IBM Watson's natural language processing and cognitive computing capabilities. The solution is demonstrated through a narrative following "Scott" as he interacts with the CaféWell Concierge to improve his health and receive personalized recommendations and support.

1.
Welltok,
Inc.
IBM
Watson
Vance
Allen,
CTO
Sridhar
Sudarsan,
CTO
Chat
with
us
on
Twi=er
#Welltoklive
@Welltok
@IBMWatson

2.
PAGE
2

3.
First
Consumer
Health
Op1miza1on
solu1on
leveraging
the
power
of
the
IBM
Watson
Natural
Language
Processing
and
Cogni1ve
Compu1ng
technology
What
is
CaféWell
Concierge?
PAGE
3
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

4.
Meet
ScoB
I’m
creaOng
a
mHealth
app!
PAGE
4
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

5.
PAGE
5
ScoB
is
Ready
to
Get
Healthier
I
sit
hunched
over
&
drink
RedBull
all
day!
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

6.
ScoB
Gets
CaféWell
Concierge
PAGE
6
©
2014
InternaOonal
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Machines
CorporaOon
&
Welltok,
Inc.

7.
ScoB
Now
has
a
Health
Concierge
PAGE
7
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

8.
ScoB
Occassionally
Travels
for
Biz
NYC
bound!
Hello,
#HxRefactored
PAGE
8
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

9.
Hungry
ScoB
Where
should
I
eat
tonight?
PAGE
9
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

10.
He
is
Rewarded
for
Healthy
Choices
PAGE
10
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

11.
ScoB
Wants
to
Run
in
NYC
Achieve 10,000 steps per
day to move your team
into 1st place and get
rewarded
Challenge
accepted!
Where
should
I
run?
PAGE
11
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

12.
I’ve
been
coding
all
night
Hood
is
Up
–
ScoB
is
Stressed
PAGE
12
©
2014
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Business
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&
Welltok,
Inc.

13.
ScoB
Receives
Unprompted
Recs
Concierge,
you
know
me
so
well!
PAGE
13
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

14.
The
Power
of
a
Health
Concierge
What
could
possibly
smart
enough
to
solve
this
problem?
It’s
not
easy
to
get
Sco=
healthy.
PAGE
14
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

15.
Understands
natural
language
Generates
&
evaluates
hypotheses
Adapts
&
learns
Watson understands me.
Watson engages me.
Watson learns and improves over
time.
Watson helps me discover.
Watson establishes trust.
Watson has endless capacity for
insight.
Watson operates in a timely
fashion.
Watson
is
Cogni1ve
Compu1ng
PAGE
15
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

16.
explorer
India
In
May
1898
India
In
May
celebrated
anniversary
in
Portugal
In
May,
Gary
arrived
in
India
aaer
he
celebrated
his
anniversary
in
Portugal
Portugal
400th
anniversary
celebrated
Gary
In
May
1898
Portugal
celebrated
the
400th
anniversary
of
this
explorer’s
arrival
in
India
This
evidence
suggests
“Gary”
is
the
answer
BUT
the
system
must
learn
that
keyword
matching
may
be
weak
rela:ve
to
other
types
of
evidence
arrived
in
arrival
in
Legend
Keyword
“Hit”
Reference
Text
Answer
Weak
evidence
Red
Text
Ques1on
Suppor1ng
Evidence
Beyond
Simple
Search
&
Keywords
PAGE
16
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2014
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Machines
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&
Welltok,
Inc.

17.
Importance
of
Deep
Seman1c
Discovery
27th May 1498
landed in
arrival
in
explorer
India
Para-
phrases
Geo-
KB
Date
Match
Stronger
evidence
can
be
much
harder
to
find
and
score…
§ Search far and wide
§ Explore many hypotheses
§ Find judge evidence
§ Many inference algorithms
On the 27th of May 1498, Vasco da
Gama landed in Kappad Beach
400th anniversary
Portugal
May 1898
celebrated
In
May
1898
Portugal
celebrated
the
400th
anniversary
of
this
explorer’s
arrival
in
India.
Kappad Beach
Legend
Temporal Reasoning
Reference Text
Answer
Statistical Paraphrasing
GeoSpatial Reasoning
Question Supporting Evidence
Vasco da Gama
PAGE
17
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2014
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&
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Inc.

18.
Answer
Scoring
Models
Responses
with
Confidence
Inquiry
Evidence
Sources
Models
Models
Models
Models
Models
Primary
Search
Candidate
Answer
GeneraOon
Hypothesis
GeneraOon
Hypothesis
and
Evidence
Scoring
Final
Confidence
Merging
&
Ranking
Synthesis
Answer
Sources
Inquiry/Topic
Analysis
Evidence
Retrieval
Deep
Evidence
Scoring
Learned
Models
help
combine
and
weigh
the
Evidence
Hypothesis
GeneraOon
Hypothesis
and
Evidence
Scoring
Inquiry
DecomposiOon
1000’s
of
Pieces
of
Evidence
Mul1ple
Interpreta1ons
of
a
ques1on
100,000’s
Scores
from
many
Deep
Analysis
Algorithms
100’s
sources
100’s
Possible
Answers
Balance
&
Combine
How
Watson
Works:
DeepQA
Architecture
PAGE
18
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

19.
PAGE
19
©
2014
InternaOonal
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&
Welltok,
Inc.
“Medicine
has
become
too
complex.
Only
about
20%
of
the
knowledge
clinicians
use
today
is
evidence-­‐based.”
Steven
Shapiro
Chief
Medical
&
ScienOfic
Officer
University
Pi=sburgh
Medical
Center
Addressing
Healthcare
Challenges

20.
PAGE
20
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.
Employers
Medical
devices
and
diagnosOcs
Health
plans
EducaOon
Public
health
Social
programs
Hospitals
Home
health
agencies
Physicians,
nurses
and
pracOOoners
Therapists
NGOs
Bio-­‐
pharmaceuOcals
Family
CommuniOes
of
care
Individual
Governments
of
U.S.
GDP
consumed
by
healthcare
spending
17.6%
Avoidable
costs/yr.
by
be=er
medicine
adherence
1/2
trillion
Of
36
million
deaths
a=ributed
to
NCDs*
could
be
prevented
9
million
4x
People
60+
that
will
be
unable
to
care
for
themselves
by
2050
Watson
in
Healthcare

21.
A
World
of
New
Possibili1es
PAGE
21
©
2014
InternaOonal
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&
Welltok,
Inc.

22.
• Partnership
between
Welltok
&
IBM
Watson
• Leverage
the
power
of
cogniOve
compuOng
• Natural
language
dialogue
• Technology
that
can
handle
unstructured
content
• Understanding
of
consumers’
health
status
and
moOvaOons
So
How
Did
We
Do
It?
PAGE
22
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

23.
• ApplicaOon
that
can
learn
and
adapt
• Understands
your
locaOon
and
preferences
• ConOnually
gets
smarter
• Provide
Intelligent
recommendaOons
bases
on
different
user
a=ributes
• Integrate
mulOple
sources
of
content
–
both
structured
and
un-­‐structured
Problems
We
Overcame
PAGE
23
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

24.
Access Watson Developer Cloud!
using Watson Experience Manager!
Develop app
“Powered by
Watson”!
using APIs !
Enrich
Watson!
with content!
Train
Watson !
using tools
and experts!
Test app!
functional and
non-functional!
Deploy
application!
Building
a
Watson
app

25.
How
Are
We
Doing
It?
Content
Inges1on
IdenOfy
Content
,Transform,
Curate
and
Ingest
ENRICH
Both
the
quesOons
and
responses
with
a=ributes
“we”
already
know
about
a
user
TRAINING
Watson
needs
to
be
trained
on
how
to
process
content
for
Health
OpOmizaOon
ANALYZE
QuesOons
and
responses
to
gain
more
intelligent
insights
Use
Cases,
Q
&A
Pairs,
and
Content
AcquisiOon
PAGE
25
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.
Partnership

26.
High
Level
Architecture
/
Data
Flow
CafeWell
Partner
Public
CafeWell/
Watson
Content
Enrichment
Watson
IngesOon
Watson
Corpus
Watson
Training
Sponsor
CafeWell
Responsibility
IBM
Responsibility
Content
Acquisi1on
PAGE
26
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

27. CONFIDENTIAL
INFORMATION
©
WELLTOK
2009-­‐2014
PAGE
27
Ex:
Watson
Being
Used
for
Health
OpOmizaOon
Ques<on:
What
should
I
do
for
my
workout
today?
Answer:
You
could
go
for
a
run
in
Central
Park,
bring
your
Fitbit
Pebble
and
be
closer
to
earning
$50
from
Acme
Healthcare.

28.
Update
the
user’s
profile
and
Social
Health
IOnerary
and
analyze
transacOon
stream
CaféWell
Concierge
Flow
Concierge
QuesOon
Enrichment
Concierge
Answer
Enrichment
You
could
go
for
a
run
in
Central
Park,
bring
your
Fitbit
Pebble
and
be
closer
to
earning
$50
from
United
Health
Care.
Why
don’t
you
invite
RunnerDude100?
26
year
old
single
male,
insured
with
United
PPO
plan,
who
lives
in
the
zip
code
11231-­‐4710,
currently
at
23
St.
&
5th
Ave.,
NYCH,
with
health
interests
of
running,
hiking,
and
biking,
currently
at
75
minutes
of
120
minutes
of
my
goal
acOvity
for
the
week,
shown
a
recent
tendency
to
be
acOve
outside,
and
has
been
discussing
men’s
issues,
what
should
I
do?
Ques<on:
What
should
I
do
for
my
workout
today?
PAGE
28
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

29.
Welltok
Responsibility
IBM
Responsibility
Answer
Enrichment
QuesOons
QuesOon
Enrichment
Answers
Intelligence
Tuning
High
Level
Architecture
/
Data
Flow
Q&A
Training
PAGE
29
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

30.
CreaOng
the
Experience

31.
PAGE
31
Thank
you!
Chat
with
us
on
Twi=er
#Welltoklive
@Welltok
@IBMWatson

32.
What
is
CaféWell
Concierge?
PAGE
32
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

33.
Workflow
Example
Ques<on:
What
should
I
do
for
my
workout
today?
Answer:
You
could
go
for
a
run
in
Central
Park,
bring
your
Fitbit
Pebble
and
be
closer
to
earning
$50
from
Acme
Healthcare.
Why
don’t
you
invite
RunnerDude100?
PAGE
33
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

34.
How
Does
it
Work?
• “Teach”
Watson
how
to
process
the
content
• Enrich
the
quesOons
consumers
ask
by
adding
info
we
already
know
about
them
• Enrich
the
responses
we
receive
from
Watson
to
make
it
acOonable
and
engaging
• Analyze
&
learn
from
the
Q&A
stream
CaféWell
Concierge
Recap
What
is
it?
• Enables
users
to
ask
quesOons,
in
everyday
language,
and
receive
relevant,
personalized,
responses
• Creates
Intelligent
Health
IOneraries
based
on
locaOon,
health
status,
benefits,
interests,
acOvity
level
• UOlizes
personalizaOon,
recommendaOons,
intelligence,
cogniOve
compuOng
and
natural
language
processing
(NLP)
to
help
consumers
opOmize
their
health
PAGE
34
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.

35.
• Partnership
between
Welltok
&
IBM
Watson
• Leverage
the
power
of
cogniOve
compuOng
• Natural
language
dialogue
• Technology
that
can
handle
unstructured
content
• Understanding
of
consumers’
health
status
and
moOvaOons
So
How
Are
We
Doing
It?
PAGE
35
©
2014
InternaOonal
Business
Machines
CorporaOon
&
Welltok,
Inc.