18009293. MODEL ORCHESTRATOR simplified abstract (GOOGLE LLC)
Contents
MODEL ORCHESTRATOR
Organization Name
Inventor(s)
Francesco Nerieri of San Francisco CA (US)
Di-Fa Chang of Cupertino CA (US)
Lan Huang of Menlo Park CA (US)
Xinlong Bao of Los Altos CA (US)
MODEL ORCHESTRATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 18009293 titled 'MODEL ORCHESTRATOR
The patent application describes methods, systems, and apparatus for determining attributions for unattributed outcomes across different content channels.
- The method involves receiving outcome data representing unattributed outcomes and attribution data representing modeled attributions from outcome models.
- Each set of modeled attribution includes a measure between unattributed outcomes and exposures, which are updated based on criteria for determining updated attributions.
- The updated attributions indicate new attributions of outcomes to corresponding exposures.
Potential Applications: - Marketing analytics - Digital advertising optimization - Content performance tracking
Problems Solved: - Identifying the impact of different content channels on outcomes - Improving attribution accuracy in marketing campaigns
Benefits: - Enhanced understanding of the effectiveness of marketing efforts - Optimization of advertising strategies based on accurate attributions
Commercial Applications: - Marketing agencies - E-commerce platforms - Digital advertising companies
Questions about the technology: 1. How does this technology improve attribution accuracy in marketing campaigns? 2. What criteria are used to determine updated attributions in the method described in the patent application?
Frequently Updated Research: - Stay updated on the latest advancements in marketing attribution models and analytics to enhance the effectiveness of advertising strategies.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining attributions for unattributed outcomes across different content channels. The method includes receiving, by a model orchestrator, outcome data representing a set of unattributed outcomes, where each unattributed outcome does not have an observed attribution to an exposure of a set of predetermined exposures. The attribution data representing a set of modeled attributions from each outcome model of a plurality of outcome models are received by the model orchestrator, where each set of modeled attribution includes a respective measure between one or more unattributed outcomes and one or more exposures. The respective measures are updated based on one or more criteria for determining one or more updated attributions, where each updated attribution indicates a new attribution of a respective outcome from the set of unattributed outcomes to a corresponding exposure of the set of predetermined exposures.
