Welcome,
everyone.
Today,
I
will
be
going
through
custom
heat
maps
and
how
they
provided
some
new
insights
into
pet
treating.
First
of
all,
I'm
Jared
Shaw
and
I
work
for
Mars.
I've
been
with
Mars
for
eight
years.
Prior
to
Mars,
I
worked
in
semiconductor I
ntel
and
IM
flash
technologies
for
about
13
years.
My
background
is
statistics
and
education.
I've
done
a
lot
of
consulting
over
the
years
as
well
as
teaching
others
in
various
statistical
methods.
I'm
married
and
I
have
three
kids.
They're
all
adopted.
I
play
games
on
the
sidelines,
build
models
and
then
periodically
camping.
Then
I
also
tinker
around
with
construction.
On
the
bottom
right,
there
is
a
room
over
my
garage
that
I
finished.
Today,
I'll
be
going
through
basically
the
abstract
I
submitted.
Then
C&T
stands
for
care
and
treat.
I'll
give
some
background
on
that
for
Mars.
I'll
go
through
measuring
efficacy,
research
protocols
we
have
for
these
types
of
studies.
Then
I'll
get
into
the
JMP
portion.
This
is
not
a
live
demonstration
of
JMP.
I'll
just
be
showing
some
images
from
the
program
and
talking
through
some
approaches
that
we
used
in
looking
at
this
data
and
then
ending
up
with
the
new
approach
that
I
introduced.
First
of
all,
the
abstract
here
is
I
have
these
custom
heat
maps
and
they
provide
a
lot
of
insights
into
pet
treating.
Overall,
our
intention
is
to
improve
the
cleaning
of
pet's
teeth
with
a
new
treat.
We
do
this
by
changing
texture,
changing
ingredients.
We
want
to
do
something
that
will
help
impact
the
teeth,
but
also
be
safe
and
delicious
and
fun
for
the
pet
as
well
as
the
pet
owner.
The
results
provide
a
lot
of
insights,
including
patterns
that
we
see
across
the
mouth.
How
does
the
product
affect
the
teeth?
The
current
graphs
and
methodologies.
The
modeling
is
pretty
good,
but
the
methodologies
and
how
we
showcase
this
data
is
not
very
good.
It
doesn't
offer
very
clear
insight
unless
there's
a
remarkable
difference
between
the
products
that
are
being
tested.
I
found
that
there
is
a
great
opportunity
to
utilize
JMP
mapping
to
create
some
custom
maps.
These
new
images,
they
spawned
a
whole
huge
investigation,
brought
in
some
new
associates
to
do
some
great
insights
and
learning.
Just
from
a
simple
image,
brought
some
great
rewards.
Maybe
to
help
bottom
or
to
ground
us
to
a
baseline.
First
of
all,
let's
consider
what
Care
& Treats
means.
Pet
care
consists
of
a
dry
diet
and
you
also
have
wet
diets
and
then
the
Care &
Treat
components.
This
is
split
up
into
two
pieces,
the
treat
and
then
the
care.
Treating
products,
they
have
a
high
palatability,
they
excite
pets.
They
may
add
some
extra
nutrition
and
supplement
the
pet's
diet.
They're
used
for
training.
Positive
reward
in
training,
getting
the
pet
to
respond
to
your
voice,
etc.
In
some
cases,
they're
long-
lasting
to
help
relieve
boredom,
reduce
destructive
behavior
and
so
forth.
The
care
products,
on
the
other
hand,
these
are
also
have
a
high
probability
to
encourage
consumption.
But
these
promote
a
healthy
teeth
that
we
concentrate
a
lot
on
oral
care,
reducing
bad
breath
and
they
can
also
act
as
a
medication
to
give
appeal
to
your
pet.
You
may
have
heard
of
like
a
pill
pocket,
et
cetera.
Now,
one
of
the
main
drivers
of
these
treats
is
texture
and
that
really
promotes
the
consumption
benefits.
Does
it
become
fun
to
chew
the
product?
For
those
that
have
dogs,
you
may
notice
that
your
dog,
in
some
cases
when
they
start
eating
a
product,
they
seem
to
inhale
it
more
than
they
chew
it.
The
texture
piece
definitely
is
something
that
you
want
to
have
them
enjoy
the
experience
of
biting
into
the
product.
This
is
just
an
image
to
help
us
understand
what
we're
talking
about
for
treating
and
trying
to
clean
the
teeth
for
our
pets.
These
are
just
a
couple
of
different
images
that
show
the
breakdown
of
the
teeth
in
pets
and
how
we
want
to
understand
how
a
product
is
impacting
these
different
teeth.
How
do
we
measure
efficacy?
Efficacy
is
basically
how
well
the
product
is
running.
Periodontal
disease
is
the
most
widespread
of
oral
disease
in
pets.
Companies
all
over,
when
they
get
into
the
care
space,
they're
looking
for
how
they
can
take
texture,
how
they
can
take
shapes,
how
they
can
build
a
chew
that
really
affects
this
oral
care.
They
want
to
measure
the
efficacy
of
the
treats.
They
use
different
approaches
over
the
years.
I'm
not
really
going
to
go
too
much
into
these.
This
is
just
more
informational...
Years
ago,
there
was
a
Logan
& Boyce
method.
This
is
a
visual
measure
on
the
teeth.
It
was
invasive
in
how
they
did
it
against
the
pets
and
you
can
read
more
about
that
on
your
own.
But
there's
also
GCPI.
This
was
less
invasive
to
the
pets,
but
still
a
manual
approach.
Then
recently,
probably
within
the
last
maybe
4
or 5
years,
they
came
up
with
this
QOLF
technique
that
essentially
takes
an
image
of
the
teeth
before
and
after.
We
find
that
this
is
just
much
more
informative.
It
gives
us
much
clearer
results
on
how
things
are
proceeding
when
the
pets
consume
these
products.
Efficacy formula
for
itself.
First
of
all,
there's
what's
called
an
ITS
and
you'll
see
this
in
the
data
later
on.
It
stands
for
Individual
Tooth S
core.
Basically,
it's
looking
at
how
much
plaque
exists
on
the
tooth.
In
this
case
for
the
data
that
I'm
using
is
based
upon
the
GCPI
approach.
The
length
of
the
tooth
and
this
gives
us
an
idea
of
basically,
how
much
plaque
is
on
each
one
of
those
teeth.
The
Chew
X,
you'll
see...
Actually,
have
them
called
different
names.
But
essentially,
this
is
the
treat
that's
being
tested.
Then
the
overall
efficacy,
this
is
again,
that
ability
or
can
we
produce
the
intended
result
from
the
product?
This
looks
at
the
calculation
takes
the
no
chew,
subtract
the
result
of
the
chew
and
then
divides
by
that
no
chew
and
we
get
that
efficacy.
The
research
protocol.
The
background
image
here
is
actually
one
of
our
feeding
center
here
in
Tennessee.
The
round
sections,
there
are
dog
pods.
We
have
several
dogs
within
each
one
of
those
pods
and
then
the
center
building
has
the
cat
rooms.
What
we
do
is
we
prepare
the
pets
by
cleaning
their
teeth
so
they
get
a
professional
cleaning.
We
try
to
get
all
of
plaque
off
the
teeth
to
give
them
a
score
of
zero.
We
run
a
crossover
design.
Essentially,
this
means
that
every
Chew
is
going
to
be
administered
to
every
pet.
Not
at
the
same
time.
We
break
it
up
into
different
phases.
In
each
of
these
phases,
the
chews
are
then
rotated
against
different
dogs,
as
I
have
written
here,
or
cats
as
well.
Scoring
this
is
essentially
done
between
each
phase.
After
a
phase
of
the
study,
so
after
those
pets,
they
have
their
standard
diets.
They
get
maybe
a
treat
product
at
the
end
of
the
day
and
then
at
the
end
of
whatever
prescribed
time
frame,
they
measured
the
amount
of
plaque
that
is
on
the
teeth.
Cleaning
teeth
with
a
score
of
zero
across
all
the
treatments.
We
just
removed
these
from
the
study.
It's
just
something
that
where
they're
consuming
the
product.
But
for
whatever
reason,
that
tooth
didn't
get
impacted
by
the
product.
Typically,
we'll
see
these
with
the
front
teeth,
the
in
cisors,
they're
used
more
for
cutting.
Generally,
the
products
are
more
about
the
chewing
behavior.
This
whole
mouth,
what
this
is
talking
about
is
some
cases,
we
have
these
individual
tooth
scores.
We
have
zero
on
a
No
Chew,
so
basically
it's
missing,
or
we
find
that
the
No
Chew
has
results
that
are
less
than
the
tooth.
Basically,
No
Chew
means
that
for
that
phase
of
the
study,
the
dog
or
cat,
they
did
not
receive
a
treat
product
to
consume,
they
just
had
a
standard
diet.
The
Chew
X
means
that
they
had
some
care
treat
at
the
end
of
the
day.
We
summarize
the
data
across
the
whole
mouth.
Sometimes
we'll
break
it
up
into
regions
in
order
to
give
us
an
idea
of
how
it's
performing.
The
analysis
protocol
itself,
really
these
are
done
with
linear
mixed
models.
We
have
fixed
effects
with
maybe
treatments
or
the
regions
depending
on
the
study.
Then
we
have
random
effects
that's
really
focused
around
the
pet
ID.
Again,
the
intent
here
is
that
we
have
the
effects
affect
all
pets
regardless
of
the
specific
pets
in
the
study.
We
also
then
run
specific
contrast.
This
is
where
we
look
at
different
sizes
of
the
mouth,
different
regions,
etc
.
Over
here
on
the
right-
hand
side
in
this
table
is
an
example
of
some
of
those
contrast
and
depending
on
the
number
of
contracts
we
run,
of
course,
we're
going
to
use
the
FWER,
to
control
for
inflated
error.
Then
we
communicate
these
results.
We
take
the
analysis
results,
we
take
images
and
then
we
sit
down
with
the
stakeholders
and
we
show
them
which
of
these
Chews
was
better
essentially.
Initially,
when
I
started
getting
involved
in
these
studies,
it
was
very,
this
Chew
did
better
versus
this
other
Chew
for
the
whole
mouth.
But
as
we
started
bringing
in
different
regions
of
the
mouth,
we
started
seeing
some
different
results
and
had
much
more
fruitful
discussions.
This
will
get
us
into
the
analysis.
What
I'm
going
to
do
here
is
I'm
just
going
to
concentrate
really
on
the
data
visualization
component.
I'm
not
going
to
go
too
much
into
the
statistics
on
the
modeling
piece.
This
is
just
about
visualization
and
in
this
first
part
is
specifically
about
ways
that
we
are
trying
to
visualize
the
teeth
initially.
This
is
a
results,
this
is
from
JMP,
from
running
the
mixed
effects
model
and
then
at
the
end
here
we
are
running
these
contrasts.
In
these
type
of
results
as
we
look
at
these,
because
I
have
here
marked
in
the
center,
the
Chews
would
show
no
difference
but
areas
of
the
mouth
would,
particularly
the
molars.
You
can
see
here
on
the
left,
I
have
just
Chew
by
itself
being
compared
and
then
on
the
right-
hand
side,
you
see
I
have
different
areas
of
the
mouth.
Different
areas
of
mouth
were
showing
interesting
differences
but
the Chew
by
themselves
compared
to
No
Chew,
maybe
we're
not
seeing
too
much
for
one
of
them
but
some
for
another.
Then
we
would
group
them
into
different
sections
used in
the
variability
chart.
This
shows
my
different
Chews
with
the
no
Chew
and
then
again
the
areas
of
the
mouth.
In
this
case,
I
would
see
that
the
mean
of
the
data
is
here
on
the
left
and
then
the
standard
deviation
of
the
data
is
on
the
right.
Definitely,
one
area
of
the
mouth
is
operating
differently
than
another,
as
I
can
see
here
on
the
right-
hand
side.
Let
me
just
turn
on
my
pointer.
Over
here,
we
can
see
that
this
variability
for
the
lower
molars
versus
the
upper
molars
is
different
for
different
Chew.
Other
ways
that
we
tried
to
portray
this
is
using
graph
builder,
we
used
the area
of
the
mouth
over
here.
I
forgot
to
mention
this
earlier
but
the
IUL,
this
is
in
sisors
and
upper
lower
canine
teeth
and
then
these
are
the
molars.
We
can
see
definitely
some
pattern
going
on
when
I
compare
across
different
phases.
I
have
phase
1,
phase
2
and
phase
3.
Phase
3,
it
looks
like
I
have
this
linear
effect
of
sorts
that's
occurring
between
the
Chews.
It's
just
how
it's
showing
up
visually.
It
doesn't
mean
in
the
order
in
which
they
are
given,
it's
just
what
the
data
is
showing.
Looking
further
into
the
variability
chart,
bringing
in
the
phases.
We're like
"Hey,
do
we
see
differences
per
phase?"
Here
we
really
see
for
this
Chew
W,
the
variability
was
much
lower
than
No Chew
and
Chew P.
It
really
starts
questioning,
well,
what's
going
on
here?
Why
is
this
specifically
happening
for
this
Chew?
What
could
we
do
to
understand
that
better?
Another
visual
that
we
generated
for
this
study
is
we
again,
summarized
it
by
the
area
of
the
mouth
and
then
the
Chew
efficacy
for
each
of
the
Chews
themselves.
We
can
definitely
see
some
differences
between
the
Chews,
but
overall,
they
might
seem
like
that
they're
similar
even
though
we're
seeing
differences
in
the
areas
of
the
mouth.
One
of
the
things
that
I
started
asking
is
like
why
do
we
see
these
differences
between
areas
of
the
mouth
but
we
don't
see
across
the
Chews
as
much
what's
going
on?
Here
I
generated
a
plot
where
down
here
on
the
x-
axis,
I
have
the
different
dogs
and
then
the
Chew
efficacy
for
the
W
and
P
Chews
and
then
areas
of
the
mouth.
Definitely,
what's
interesting
here
is
that
particular
animals
are
showing
the
difference
and
other
animals
are
not
now.
We
would
expect
this,
given
randomness
of
the
study
that
the
Chews
are
going
to
behave
differently
and
how
the
pet
is
consuming
the
product.
We
really
started
making
me
think it,
much
more
deeply
about
the
data
and
say
really
what's
going
on
here
in
this
data?
Do
the
pets
chew
the
product
differently?
That
led
me
into
this
data
visualization
for
the
second
part
because
it
made
me
really
start
thinking
about
the
data,
what
can
I
do
or
how
can
I
look
at
this
differently
to
bring
out
this
individual
component
of
the
pets.
I
was
working
with
a
research
scientist
and
we
were
going
through
one
of
the
studies
and
they
happened
to
have
this
card.
As
you
can
see
here
on
the
right
hand
side,
this
is
just
a
picture
of
the
card.
They
had
this
sitting
on
their
desk
and
I
was
sitting
there
staring
at
it.
I
had
the
idea,
"What
if
I
created
a
tooth
map
in
JMP?"
I
could
then
color
each
of
these
individual
teeth
and
maybe
get
some
clarity,
further
clarity
in
these
studies
than
what
we
were
looking
at.
I
went
and
contacted
our
Waltham
scientist.
Waltham
is
a
site
within
pet
care
in
the
UK
and
they
concentrate
on
doing
research
on
the
pet
nutrition.
I
went
and
talked
to
them
and
one
of
their
scientists
drew
me
up
some
teeth.
Here
for
this
first
slide,
we
have
the
dog
teeth.
The
map
is
here
on
the
left
that
they
drew
up.
On
the
right
is
just
a
visual
to
give
you
an
idea
of
the
different
types
of
teeth
that
show
up
in
the
dog's
mouth.
Then
we
see
something
similar
for
cat
teeth.
Again,
on
the
left
is
the
one
that
was
drawn
up
for
this
study.
What
I
did
then
is
this
is
the
map
creator.
This
is
a
script
that's
available
on
the
JMP
community.
This
is
an
older
script.
It's
been
a
while
since
it's
been
updated,
but
I
found
that
it
was
very
helpful
for
this
scenario
and
so
downloaded
the
add
in
and
it
creates
the
add-
in
pull-
down
menu.
You
go
to
the
add-
in
pull-
down
as
you
see
up
here
on
the
upper
left.
You
can
click
on
Map
Shapes
and
then
you
can
do
the
Custom
Map
Creator.
When
this
pops
up,
you
get
this
screen
here,
again
without
the
teeth.
Then
you
get
a
couple
of
empty
tables.
I
dragged
the
image
file
onto
the
map.
I
gave
it
a
name.
Then
I
go
over
here
to
this
next
section,
and
basically,
I
start
tracing
the
teeth.
For
every
single
tooth,
I
would
trace
it
and
then
I
would
give
it
a
name.
This
is
an
example
of
after
doing
all
of
that
work.
As
you
can
see,
these
are
all
of
the
individual plot
points,
is
me
just
clicking
around
that
tooth
to
try
to
get
the
entire
shape
accurately,
so
it
would
show
up
as
accurate
as
possible
on
the
screen
when
we
look
at
the
plots.
Then
we
have
our
different
files
here.
You
have
this
XY.
This
gives
you
the
coordinates.
Down
here
on
the
graph
on
the
lower
left,
you
see
this
is
a
zero,
zero.
This
is
essentially
like
an
X,
Y
coordinate
system.
It's
just
telling
me
where
on
the
graph
that
particular
point
is
for
that
particular
shape
ID.
Then
I
have
a
name
file
that
gives
me
the
shape
ID
and
then
the
name
of
the
tooth.
In
this
case,
I
created
a
separate
file
for
dog
teeth
and
of
course,
for
cat teeth,
since
they
are
different
shapes
of
teeth
and
different
shapes
of
the
jaw.
D
just
stands
for
dog
and
then
the
ID
number
for
that
tooth.
One
thing
that
I
found
interesting
is
when
I
first
created
this
program,
this
was
a
few
years
ago,
I
was
able
to
just
create
the
maps,
and
I
created
a
custom
script.
People
would
run
the
script,
it
would
save
the
maps
to
their
C
drive
and
everything
would
work
fine.
But
soon
after
a
couple
of
years,
it
no
longer
worked.
It
was
because
Mars
entered
in
some
security
protocols
that
basically
wouldn't
allow
us
to
save
maps
to
the
C
drive.
It
basically
locked
it
up.
I
had
to
go
out
and
figure
out,
well,
how
can
I
still
do
this?
I
want
to
see
the
maps,
we
want
to
create
these
maps.
Then
I
found
another
community
forum
that
talked
about
putting
it
out
onto
the
app
data
for
your
username
roaming,
etc
.
You
see
the
path
here
and
so
you
put
your
maps
there
and
it
works
just
as
if
I
put
them
on
the
C
drive.
Here,
once
you
have
those
files
saved
on
to
their
proper
location,
then
you
go
into
JMP Graph
Builder.
If
you've
never
used
it
down
here
on
the
lower
left-
hand
corner
of
the
screen,
it
doesn't
show
it, it
just
says
Ma.
But
that
is
the
map
feature.
Since
I
gave
these
tooth
IDs
as
map
component
or
the
name
component,
then
I
take
that
tooth
ID
and
drag
it
down
to
that
section.
When
you
do,
you
can
see
here
in
the
background,
I
see
those
teeth
showing
up.
Not
all
of
the
teeth
show
up
because
for
this
particular
study,
I
did
not
look
at
every
single
tooth.
You
can
see
the
incisors
are
missing.
You
can
right-
click
on
the
image
and
go
to
Map
Shapes
and
show
the
missing
shapes.
When
you
do
that,
you
get
all
of
the
teeth
showing
up.
In
addition,
I
took
the
different
Chews
that
were
investigated
and
I
dragged
that
up
here
to
the
Group
X
up
here
at
the
top.
We
see
these
three
different
maps
for
each
of
the
Chews
and
the
No Chew.
I
then
take
the
ITS
and
pulled
it
over
here
to
color.
Again,
ITS
is
the
individual
tooth
score,
about
how
much
plaque
is
on
the
tooth.
At
this
point,
this
has
given
me
the
average
amount
of
plaque
that
is
on
all
of
these
dog's
teeth
that
were
in
this
particular
study.
I
can
start
to
see
where
that
plaque
is
showing
up
on
the
No Chew
.
Definitely,
it's
on
these
molars
especially
down
here
on
the
bottom
molars,
on
the
right-
hand
side
especially.
Then
I
can
also
see
for
the
different Chews
,
I
could
see
how
maybe
some
of
the
canine
teeth
on
average
was
showing
that
some
of
this
plaque
remained
on
the
teeth.
But
definitely,
I'm
seeing
some
cleaning
of
the
molars.
Clicking
on
done
and
giving
me
the
bigger
image
so
I
can
see
it
in
more
detail.
This
leads
me
into
data
discovery.
I
created
these
maps
and
they're
looking
great.
People
like,
"Hey,
this
is
a
really
interesting
way
of
looking
at
the
data."
But
I
wasn't
done
there.
We
started
discovering
something
when
we
looked
at
the
maps
differently.
In
this
case,
this
is
just
back
to
where
we
were, that
same
map.
What
I
did
is
I
started,
I
put
a
local
data
filter
on
and
by
the
dog
names.
Here
I
have
dog
names
on
the
left,
turn
on
a
local
data
filter
and
I
can
now
filter
on
each
one
of
these
dogs
and
look
at
them
individually.
Now,
we
don't
have
time
to
go
through
all
of
them,
but
I
wanted
to
show
just
a
few
of
them.
Here
for
Aura.
What
was
interesting
for
Aura
is
that
we
noticed
that
for
this
Chew P,
that
these
lower
molars
on
the
right-
hand
side
weren't
really
getting
cleaned
very
well.
According
to
the
score,
they
weren't
getting
cleaned
at
all.
This
started
telling
me
as
I
looked
at
this,
"M
an,
this
particular
pet,
Aura would
only
chew
the
product
on
the
left
side."
For
this
Chew
W,
we
actually
saw
a
different
signal.
She
actually
chewed
the
product
that
seemed
like
more
on
both
sides
of
the
mouth.
Very
interesting
results.
A
gain,
these
are
just
two
different
types
of
textures
that
are
being
looked
at
for
this
product.
Going
down
to
another
dog
here,
Gretchen.
She
showed
something
different.
For
Chew
P,
she
did
very
well
with
that
Chew,
but
for
Chew
W,
she
preferred
to
chew
it
more
on
the
right
side
of
her
mouth
than
the
left
side.
Again,
these
are
completely
two
different
animals
and
they
chew
the
product
differently
depending
on
the
texture
and
their
preference
to
the
texture.
Not
all
dogs,
we
are
starting
to
see
here
like
the
same
texture.
They're
very
individual.
When
I
was
doing
this,
I
started
asking
friends
of
mine,
"Do
you
chew
with
one
side
of
your
mouth
for
particular
products?"
Sure
enough,
as
we
started
collecting
that
data,
we
found,
like
for
myself,
I
like
to
chew
nuts,
but
only
on
the
left
side
of
my
mouth.
Others,
when
they
chewed
nuts,
it
didn't
matter
which
side,
etc
.
As
we
started
talking
about
it
and
taking
record
of
it,
we
started
to
see,
"Hey,
these
pets
are
consuming
product
more
like
an
individual
human
does
when
they
have
preferences
in
how
they
look
at
product."
And
just
a
couple
more
dogs
to
look at here.
Bagel
this
one,
the
top
teeth
clean
better
than
the
bottom
teeth.
Just
fascinating
results.
How
is
that
possible?
Because
the
product
is
when
they're
chewing
it,
they're
biting
down
into
the
product.
Your
teeth,
your
top
teeth
and
your
bottom
teeth
are
sinking
into
that
material.
Why
would
we
get
certain
components
showing
up
here?
Basically,
what
it
means
for
this
particular
dog,
for
Bagel,
it's
just
the
rear
molars
that
Bagel
was
using
to
chew
into
the
product.
The
front
molars,
who
wasn't
using
at
all.
Then
Muck,
this
is
a
great
example
of
the
dog.
Either
they're
not
consuming
the
treat
at
all,
or
they're
just
inhaling
the
treat.
Those
are
some
of
the
customer
calls
that
we
sometimes
get
is,
"Hey,
my
dog
is
not
even
chewing
this
product.
They're
just
like
taking
it
and
swallowing
it
whole."
Very
interesting
results.
A
s
we
started
looking
into
this
data
more
and
more,
it
really
led
us
to
believe
that,
"Hey,
we
need
a
new
product.
We
need
to
create
something
that
will
really
bring
in
a
whole
mouth
clean.
A
chew
experience
where
the
animal
likes
to
chew,
likes
to
really
get
into
the
product
and
consume
it
and
to
have
that
efficacy
result
where
the
product
is
helping
to
clean
the
teeth."
In summary,
what
I've
learned
from
this
experience
is
that
historical
studies
for
this
particular
experience
were
basically
giving
an
average
across
the
whole
mouth
and
it
wasn't
sufficient in
really
giving
us
a
good
idea
of
what
was
happening
with
the
product.
Viewing
these
tooth
maps
by
individual
pets
started
showing
some
very
interesting
results
that
really
we
couldn't
even
look
at
it
by
reaching into
the
mouth
across
all
of
the
pets.
We
actually
need
to
start
looking
at
it
by
individual
and
start
classifying
it
by,
"Hey,
this
particular
treat
impacted
these
teeth
only,
and
this
particular
treat
impacted
these
teeth
only."
Start
classifying
it
in
that
way
so
that
we
can
start
learning
a
lot
more
about
the
texture
of
the
product
and
how
it
was
consumed.
Now
these
findings,
we
started
applying
this
across
all
studies,
all
historical
studies.
We
pulled
this
into
a
large
analysis
that
started
really
digging
into
it
to
learn
more
from
the
history
of
what
we've
done
and
how
it
affects
things
moving
forward.
Of
course,
this
led
into
some
new
product
development.
Here's
an
example
of
what
that
is.
Unfortunately,
I
can't
show
it
to
you.
The
image
is
protected.
It
is
not
yet
released,
but
it
is
something
that
we're
investigating.
Thank
you.
