TV Ad Targeting via Hashed Identity Mapping
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Solution Overview
Problem
Current advertising technologies face challenges in effectively targeting and delivering video advertisements across multiple media platforms due to fragmented viewer data, limited data models, and restrictions on data aggregation, leading to inefficient ad placement and inability to track viewer behavior accurately.
Innovation Solution
A system that utilizes deep learning and identity hashing to combine online and offline data, creating a unified platform for purchasing video advertising inventory, allowing for targeted TV advertising by mapping consumer behavior across devices and media providers while protecting user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data aggregation from multiple media platforms is performed to improve ad targeting accuracy, then advertising precision is improved, but data privacy protection is compromised
Solution Approach 1:
The patent introduces hash identifiers as an intermediary mechanism that enables data aggregation across media platforms without exposing raw personal information. The hash identifier acts as a mediator that links online and offline viewing data while maintaining privacy protection, allowing advertisers to target ads accurately without direct access to sensitive user data.
Solution Approach 2:
The patent creates a copied representation of user identity through hash identifiers rather than using actual personal identifiers. This copying approach allows the system to track and aggregate viewing behavior across different platforms while the original sensitive data remains protected and inaccessible to advertisers.
2Productivity
If comprehensive viewer data collection is implemented to improve ad delivery optimization, then advertising effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct components: online viewing data collection, offline viewing data collection, hash identifier generation, data matching, and ad delivery optimization. This segmentation allows each component to be handled independently, reducing overall system complexity while maintaining comprehensive data utilization.
Solution Approach 2:
The hash identifier serves as an intermediary that simplifies the complexity of matching online and offline viewing data. Instead of directly correlating complex user profiles across platforms, the system uses the hash identifier as a simple key to link data sets, significantly reducing processing complexity.
3Speed
If real-time ad slot bidding is performed to improve market responsiveness, then advertising agility is improved, but computational load increases
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and organizing viewing data, generating hash identifiers, and preparing audience profiles before ad slots become available. This preliminary preparation reduces the computational burden during real-time bidding, enabling fast market response without excessive resource consumption during critical decision moments.
Solution Approach 2:
The system uses copied data representations (hash identifiers and aggregated audience profiles) instead of processing raw comprehensive user data during real-time bidding. This copying approach maintains the speed and responsiveness needed for real-time decisions while significantly reducing computational resource requirements.
Data Source
AI summary
Within an advertising buying platform, advertising buyers are able to direct advertising content to TV/OTT/VOD inventory that matches targeted market segments. The advertising buyers select from a list of available market segments, and the system pairs the market segments to an anonymous dataset of media consumers (using a database of hashed online IDs). The dataset of hashed online IDs is then mapped to external OEM IDs (such as from a Smart TV) via the functions of a hashed identity mapping method. This permits the advertiser to target advertising content to a population of TV viewers. Also described herein are methods of bidding on programmatic TV content.