# US 2026/0094076 A1 · Systems and methods for behavioural and contextual data analytics

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- Number: US 2026/0094076 A1
- Document ID: US20260094076A1
- Status: Published application
- Kind: A1
- Application number: 19/411,687
- Filed: 2025-12-08
- Published: 2026-04-02
- Earliest claimed: 2015-03-26
- Assignees: The Nielsen Company (US), LLC
- Applicants: The Nielsen Company (US), LLC
- Inventors: Hannu Verkasalo
- Web page: https://patentgrep.com/patent/US20260094076A1

## CPC

- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06F 9/455: Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F 16/26: Visual data mining; Browsing structured data
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06Q 10/0637: Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- H04L 41/147: for predicting network behaviour
- 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
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

System and method for behavioral and contextual data analytics are disclosed. An example computer system to process observational data received from a wireless device includes a memory including machine readable instructions and a processor to execute the instructions to: process the observational data to identify temporally adjacent applications to generate usage metric data, the observational data including application usage data; build a behavior model based on the identified temporally adjacent applications, the behavior model to describe user behavior associated with the wireless device; and apply the behavior model to predict a usage duration of a second application in response to usage of a first application.

## Claims

### Claim 1

A computing system for determining audience segments comprising:
a processor; and
a non-transitory computer readable storage medium, having stored thereon program instructions that, upon execution by the processor, cause performance of a set of operations comprising:
receiving, from a server, first-party data comprising usage metrics for wireless devices of a first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the first set of users;
ingesting the first-party data into a model trained using usage metrics for wireless devices of a second set of users different from the first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the second set of users;
querying the model based on a particular behavioral pattern represented by the usage metrics of the first-party data for wireless devices of a particular segment of the first set of users; and
based on the querying, outputting, by the model, one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern.

### Claim 2 (depends on 1)

The computing system of claim 1, wherein the model is configured to analyze behavioral patterns of usage of wireless devices represented by usage metrics input into the model to determine one or more characteristics of users of the wireless devices that exhibited the behavioral patterns.

### Claim 3 (depends on 1)

The computing system of claim 1, wherein the particular behavioral pattern comprises a pattern of exposure of the particular segment to one or more particular types of multimedia content.

### Claim 4 (depends on 1)

The computing system of claim 1, the set of operations further comprising:
transmitting, to an external computing system, the one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern to facilitate use of the one or more determined characteristics by the external computing system to select targeted content.

### Claim 5 (depends on 1)

The computing system of claim 1, wherein the usage metrics for the wireless devices of the first set of users comprises one or more of a frequency of usage or a duration of usage of one or more applications installed on the wireless devices.

### Claim 6 (depends on 1)

The computing system of claim 1, the set of operations further comprising:
receiving additional usage metrics for wireless devices of a third set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the third set of users;
updating the model using the additional usage metrics;
querying the updated model based on the particular behavioral pattern; and
based on the querying of the updated model, outputting, by the model, one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.

### Claim 7 (depends on 6)

The computing system of claim 6, the set of operations further comprising:
transmitting, to an external computing system, the one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.

### Claim 8 (depends on 1)

The computing system of claim 1, wherein:
the model comprises a plurality of nodes and a plurality of weights, respective nodes of the plurality of nodes represent one or more of the first set of users or the second set of users, respective weights of the plurality of weights represent respective correlation coefficients between the respective nodes, and
the set of operations further comprises determining the plurality of weights based on the usage metrics.

### Claim 9 (depends on 8)

The computing system of claim 8, wherein the model is at least one of: a neural network or a Markov model.

### Claim 10

A method comprising:
receiving, from a server, first-party data comprising usage metrics for wireless devices of a first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the first set of users;
ingesting the first-party data into a model trained using usage metrics for wireless devices of a second set of users different from the first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the second set of users;
querying the model based on a particular behavioral pattern represented by the usage metrics of the first-party data for wireless devices of a particular segment of the first set of users; and
based on the querying, outputting, by the model, one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern.

### Claim 11 (depends on 10)

The method of claim 10, wherein the model is configured to analyze behavioral patterns of usage of wireless devices represented by usage metrics input into the model to determine one or more characteristics of users of the wireless devices that exhibited the behavioral patterns.

### Claim 12 (depends on 10)

The method of claim 10, wherein the particular behavioral pattern comprises a pattern of exposure of the particular segment to one or more particular types of multimedia content.

### Claim 13 (depends on 10)

The method of claim 10, further comprising:
transmitting, to an external computing system, the one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern to facilitate use of the one or more determined characteristics by the external computing system to select targeted content.

### Claim 14 (depends on 10)

The method of claim 10, wherein the usage metrics for the wireless devices of the first set of users comprises one or more of a frequency of usage or a duration of usage of one or more applications installed on the wireless devices.

### Claim 15 (depends on 10)

The method of claim 10, further comprising:
receiving additional usage metrics for wireless devices of a third set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the third set of users;
updating the model using the additional usage metrics;
querying the updated model based on the particular behavioral pattern;
based on the querying of the updated model, outputting, by the model, one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern; and
transmitting, to an external computing system, the one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.

### Claim 16

A non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by a processor, cause performance of a set of operations comprising:
receiving, from a server, first-party data comprising usage metrics for wireless devices of a first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the first set of users;
ingesting the first-party data into a model trained using usage metrics for wireless devices of a second set of users different from the first set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the second set of users;
querying the model based on a particular behavioral pattern represented by the usage metrics of the first-party data for wireless devices of a particular segment of the first set of users; and
based on the querying, outputting, by the model, one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern.

### Claim 17 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, wherein the particular behavioral pattern comprises a pattern of exposure of the particular segment to one or more particular types of multimedia content.

### Claim 18 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, the set of operations further comprising:
transmitting, to an external computing system, the one or more determined characteristics of the particular segment that exhibited the particular behavioral pattern to facilitate use of the one or more determined characteristics by the external computing system to select targeted content.

### Claim 19 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, wherein the usage metrics for the wireless devices of the first set of users comprises one or more of a frequency of usage or a duration of usage of one or more applications installed on the wireless devices.

### Claim 20 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, the set of operations further comprising:
receiving additional usage metrics for wireless devices of a third set of users, the usage metrics representing behavioral patterns of usage of the wireless devices by respective ones of the third set of users;
updating the model using the additional usage metrics;
querying the updated model based on the particular behavioral pattern;
based on the querying of the updated model, outputting, by the model, one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern; and
transmitting, to an external computing system, the one or more updated characteristics of the particular segment that exhibited the particular behavioral pattern.