18671630. METHODS AND APPARATUS TO CORRECT ERRORS IN AUDIENCE MEASUREMENTS FOR MEDIA ACCESSED USING OVER-THE-TOP DEVICES simplified abstract (The Nielsen Company (US), LLC)

METHODS AND APPARATUS TO CORRECT ERRORS IN AUDIENCE MEASUREMENTS FOR MEDIA ACCESSED USING OVER-THE-TOP DEVICES

Organization Name

The Nielsen Company (US), LLC

Inventor(s)

Kumar Nagaraja Rao of Fremont CA (US)

Kamer Toker Yildiz of Campbell CA (US)

Jennifer Haskell of Sunnyvale CA (US)

Cristina Ion of Belmont CA (US)

Mimi Zhang of San Francisco CA (US)

METHODS AND APPARATUS TO CORRECT ERRORS IN AUDIENCE MEASUREMENTS FOR MEDIA ACCESSED USING OVER-THE-TOP DEVICES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18671630 titled 'METHODS AND APPARATUS TO CORRECT ERRORS IN AUDIENCE MEASUREMENTS FOR MEDIA ACCESSED USING OVER-THE-TOP DEVICES

Abstract: Methods and apparatus to correct errors in measuring audiences of over-the-top media are disclosed. In some examples, the methods and apparatus identify a first set of data from a first data source, the first set of data different from a second set of data from a second data source. In some examples, the methods and apparatus generate a third set of data based on the second set of data from the second data source. In some examples, the methods and apparatus generate a model based on a difference between the first set of data and the third set of data. In some examples, the methods and apparatus apply the model to the first set. In some examples, the methods and apparatus assign viewership to an impression associated with the first set of data by imputing viewership associated with the second set of data to the first set of data.

• Simplified Explanation:

This patent application discusses methods and tools for correcting errors in measuring audiences of over-the-top media by comparing data sets and generating models to improve accuracy.

• Key Features and Innovation:

- Identification of data discrepancies from different sources - Generation of a model based on data differences - Application of the model to correct errors in audience measurement - Imputation of viewership data to improve accuracy

• Potential Applications:

- Media analytics - Advertising effectiveness measurement - Content optimization for streaming platforms

• Problems Solved:

- Inaccurate audience measurement in over-the-top media - Data discrepancies from multiple sources - Lack of a systematic approach to correct errors in viewership data

• Benefits:

- Improved accuracy in measuring audience viewership - Enhanced advertising targeting - Better content optimization for streaming services

• Commercial Applications:

Title: Audience Measurement Correction Technology This technology can be used in media analytics companies, advertising agencies, and streaming platforms to enhance audience measurement accuracy, improve advertising effectiveness, and optimize content delivery.

• Questions about Audience Measurement Correction Technology:

1. How does this technology address the challenges of measuring audiences in over-the-top media? - This technology addresses the challenges by identifying data discrepancies, generating models, and applying them to correct errors in audience measurement. 2. What are the potential implications of using this technology in advertising and content optimization? - The potential implications include improved targeting, better ROI on advertising spend, and enhanced user engagement on streaming platforms.

Original Abstract Submitted

Methods and apparatus to correct errors in measuring audiences of over-the-top media are disclosed. In some examples, the methods and apparatus identify a first set of data from a first data source, the first set of data different from a second set of data from a second data source. In some examples, the methods and apparatus generate a third set of data based on the second set of data from the second data source. In some examples, the methods and apparatus generate a model based on a difference between the first set of data and the third set of data. In some examples, the methods and apparatus apply the model to the first set. In some examples, the methods and apparatus assign viewership to an impression associated with the first set of data by imputing viewership associated with the second set of data to the first set of data.