# US 2026/0073409 A1 · Methods and systems for cross-platform overlap modeling

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- Number: US 2026/0073409 A1
- Document ID: US20260073409A1
- Status: Published application
- Kind: A1
- Application number: 19/395,437
- Filed: 2025-11-20
- Published: 2026-03-12
- Earliest claimed: 2023-07-05
- Assignees: Ispot.Tv, Inc.
- Applicants: iSpot.tv, Inc.
- Inventors: Sean Muller; Michael Bardaro; Dipti Shah; Vijoy Gopalakrishnan; Sam Hui; Jonathan Woodard
- Web page: https://patentgrep.com/patent/US20260073409A1

## CPC

- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A multivariate probit model is used to determine overlaps for reach and impressions for a plurality of different platforms.

## Claims

### Claim 1

A computer-implemented method for determining a proportion of a population that is exposed during a time interval to a content item, the computer-implemented method comprising:
receiving, by an aggregation server from a plurality of automatic content recognition (ACR) systems respectively associated with a first plurality of television sets, first reach statistics comprising the proportion of the population exposed during the time interval to the content item by a first platform, wherein the first platform includes a linear television platform, the plurality of ACR systems sample audio and/or video from the content item and compare the sampled audio and/or video with a database of content items to identify the content item by its unique characteristics, the first reach statistics identify at least one of a household or device that displayed the content item using the first platform;
receiving, by the aggregation server from a plurality of pixel tag tracking systems respectively associated with a second plurality of television sets, second reach statistics comprising the proportion of the population exposed during the time interval to the content item by a second platform, wherein the second platform includes a streaming platform, the plurality of pixel tracking systems search for pixel tags embedded in the content item to identify the content item, and the second reach statistics identify the at least one of the household or the device that displayed the content item using the second platform, wherein at least some of the second plurality of television sets are included in the first plurality of television sets;
receiving, by the aggregation server from a video sharing system, third reach statistics comprising the proportion of the population exposed during the time interval to the content item by a third platform, the third platform including the video sharing system and the content item including a video displayed by the video sharing system, wherein the third platform does not identify the at least one of the household or the device that displayed the content item using the third platform;
calculating, by at least one processor in operable communication with the aggregation server, overlap reach statistics comprising the proportion of the population exposed to the content item during the time interval by both the first platform and the second platform;
calculating, by the at least one processor, using a multivariate probit model with the first reach statistics, the second reach statistics, the third reach statistics, and the overlap reach statistics to calculate proportion information including at least one of:
the proportion of the population that is not exposed to the content item during the time interval by any of the platforms;
the proportion of the population that is exposed to the content item during the time interval by only one of the platforms;
the proportion of the population that is exposed to the content item during the time interval by at least two of the platforms but not by one of the platforms; or
the proportion of the population that is exposed to the content item during the time interval by all of the platforms;
generating, by the at least one processor, a report including the proportion information; and
displaying the report on a display device.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is not exposed to the content item during the time interval by the first, second, or third platform.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is exposed to the content item during the time interval by only the third platform.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is exposed to the content item during the time interval by only the second platform or by only the first platform.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is exposed to the content item during the time interval by both the second platform and the third platform, but not on the first platform.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is exposed to the content item during the time interval by both the first platform and the third platform, but by the second platform.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is exposed to the content item on both the first platform and the second platform, but not on the third platform.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1, wherein using the multivariate probit model comprises using the multivariate probit model to calculate the proportion of the population that is exposed to the first platform, the second platform, and the third platform.

### Claim 9 (depends on 1)

The computer-implemented method of claim 1, wherein calculating the overlap reach statistics comprises calculating the overlap reach statistics using an Internet Protocol (IP) address.

### Claim 10 (depends on 1)

The computer-implemented method of claim 1, wherein the first platform comprises a linear platform.

### Claim 11 (depends on 1)

The computer-implemented method of claim 1, wherein the second platform comprises over-the-top (OTT) platform.

### Claim 12 (depends on 1)

The computer-implemented method of claim 1, wherein the multivariate probit model takes as input a mean vector corresponding to a total reach of the first platform, the second platform, and the third platform, respectively, as well as correlation matrix including correlation parameters relating a probability of an individual watching a first content item on both the first platform and the second platform, a probability of the individual watching the first content item on both the first platform and the third platform, and the probability of the individual watching the first content item on both the second platform and the third platform.

### Claim 13 (depends on 1)

The computer-implemented method of claim 1, wherein the multivariate probit model is expressed by:
(
u
1
u
2
u
3
)
∼
MVN
⁡
(
[
μ
1
μ
2
μ
3
]
,
[
1
r
v
r
1
w
v
w
1
]
)
⁢
[
z
i
=
1
]
⇔
[
u
i
>
0
]
where:
MVN is a multivariate normal distribution;
z1, z2, and z3 are indicator variables for exposure to the content item by the first platform, the second platform, and the third platform, respectively, and take a value of 1 if exposure occurs during the time interval or is 0 otherwise;
u1, u2, and u3 are latent variables;
μ1, μ2, and μ3 are mean parameters and correspond to total reach for the first platform, total reach for the second platform, and total reach for the third platform and are obtained from the first reach statistics, second reach statistics, and third reach statistics, respectively;
r is a correlation parameter between the first platform and the second platform;
v is a correlation parameter between the first platform and the third platform; and
w is a correlation parameter between the first platform and the third platform.

### Claim 14 (depends on 13)

The computer-implemented method of claim 13, wherein r is estimated by:
r
=
arg
⁢
min
r
(
❘
"\[LeftBracketingBar]"
O
-
∫
0
∞
∫
0
∞
f
μ
^
1
,
μ
^
2
,
r
(
u
1
,
u
2
)
⁢
du
1
⁢
du
2
❘
"\[RightBracketingBar]"
)
where the argmin is taken with respect to r, over a range (−1, +1), and
O is the proportion of the population exposed to the content item during the time interval by both the first platform and the second platform.

### Claim 15 (depends on 13)

The computer-implemented method of claim 13, wherein v is estimated by:
v
=
arg
⁢
min
v
(
❘
"\[LeftBracketingBar]"
O
-
∫
0
∞
∫
0
∞
f
μ
^
1
,
μ
^
3
,
v
(
u
1
,
u
3
)
⁢
du
1
⁢
du
3
❘
"\[RightBracketingBar]"
)
where the argmin is taken with respect to v, over a range (−1, +1), and
O is the proportion of the population exposed to the content item during the time interval by both the first platform and the third platform.

### Claim 16 (depends on 13)

The computer-implemented method of claim 13, wherein w is estimated by:
w
=
arg
⁢
min
w
(
❘
"\[LeftBracketingBar]"
O
-
∫
0
∞
∫
0
∞
f
μ
^
2
,
μ
^
3
,
v
(
u
2
,
u
3
)
⁢
du
2
⁢
du
3
❘
"\[RightBracketingBar]"
)
where the argmin is taken with respect to v, over a range (−1, +1), and
O is the proportion of the population exposed to the content item during the time interval by both the second platform and the third platform.

### Claim 17 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by only the third platform (π001) is calculated by:
π
001
=
∫
-
∞
0
∫
-
∞
0
∫
0
∞
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
du
1
⁢
du
2
⁢
du
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 18 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by only the second platform (π010) is calculated by:
π
010
=
∫
-
∞
0
∫
0
∞
∫
-
∞
0
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 19 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by only the first platform (π100) is calculated by:
π
100
=
∫
0
∞
∫
-
∞
0
∫
-
∞
0
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 20 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is not exposed to the content item during the time interval by the first platform, the second platform, or the third platform (π000) is calculated by:
π
000
=
∫
-
∞
0
∫
-
∞
0
∫
-
∞
0
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 21 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by all three platforms (π111) is calculated by:
π
111
=
∫
0
∞
∫
0
∞
∫
0
∞
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 22 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by both the first platform and the second platform, but not the third platform (π110) is calculated by:
π
110
=
∫
0
∞
∫
0
∞
∫
-
∞
0
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 23 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by both the first platform and the third platform, but not the second platform (π101) is calculated by:
π
101
=
∫
0
∞
∫
-
∞
0
∫
0
∞
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 24 (depends on 13)

The computer-implemented method of claim 13, wherein the proportion of the population that is exposed to the content item during the time interval by both the second platform and the third platform, but not the first platform (π011) is calculated by:
π
011
=
∫
-
∞
0
∫
0
∞
∫
0
∞
f
Θ
(
u
1
,
u
2
,
u
3
)
⁢
d
⁢
u
1
⁢
d
⁢
u
2
⁢
d
⁢
u
3
where fΘ(u1, u2, u3) denotes a probability density function (PDF) of the multivariate Gaussian distribution with parameter vector Θ, wherein:
f
Θ
(
u
→
)
=
(
2
⁢
π
)
-
3
/
2
⁢
⌈
∑
⌉
-
1
/
2
⁢
exp
⁢
(
-
1
2
⁢
(
u
→
-
μ
→
)
⁢
′Σ
-
1
(
u
→
-
μ
→
)
)
.

### Claim 25

A system for determining a proportion of a population that is exposed during a time interval to a content item, the system comprising:
an aggregation server configured to:
receive, from a plurality of automatic content recognition (ACR) systems respectively associated with a first plurality of television sets, first reach statistics comprising the proportion of the population exposed during the time interval to the content item by a first platform, wherein the first platform includes a linear television platform, the plurality of ACR systems sample audio and/or video from the content item and compare the sampled audio and/or video with a database of content items to identify the content item by its unique characteristics, the first reach statistics identify at least one of a household or device that displayed the content item using the first platform;
receive, from a plurality of pixel tag tracking systems respectively associated with a second plurality of television sets, second reach statistics comprising the proportion of the population exposed during the time interval to the content item by a second platform, wherein the second platform includes a streaming platform, the plurality of pixel tracking systems search for pixel tags embedded in the content item to identify the content item, and the second reach statistics identify the at least one of the household or the device that displayed the content item using the second platform, wherein at least some of the second plurality of television sets are included in the first plurality of television sets; and
receive, from a video sharing system, third reach statistics comprising the proportion of the population exposed during the time interval to the content item by a third platform, the third platform including the video sharing system and the content item including a video displayed by the video sharing system, wherein the third platform does not identify the at least one of the household or the device that displayed the content item using the third platform; and
at least one processor in operable communication with the aggregation server and configured to:
calculate overlap reach statistics comprising the proportion of the population exposed to the content item during the time interval by both the first platform and the second platform;
use a multivariate probit model with the first reach statistics, the second reach statistics, the third reach statistics, and the overlap reach statistics to generate a report including proportion information at least one of:
the proportion of the population that is not exposed to the content item during the time interval by any of the platforms;
the proportion of the population that is exposed to the content item during the time interval by only one of the platforms;
the proportion of the population that is exposed to the content item during the time interval by at least two of the platforms but not by one of the platforms; or
the proportion of the population that is exposed to the content item during the time interval by all of the platforms; and
display the report on a display device.