Model Orchestrator for Cross-Channel Attribution Conflict Resolution
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Solution Overview
Problem
Existing techniques for attributing online outcomes across different content channels are inaccurate and generate conflicting results due to lack of cross-channel data sharing, leading to unattributed or incorrectly attributed outcomes.
Innovation Solution
A system that uses a model orchestrator to collect and update attributions from multiple outcome models, applying criteria such as probability distributions, geographical location, and similarity values to resolve conflicts and determine accurate attributions for unattributed outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple independent outcome models are used to attribute outcomes from different content channels, then each model can operate independently on its own data channel, but conflicting attribution results and unattributed outcomes occur due to lack of cross-channel data sharing
Solution Approach 1:
The patent introduces an orchestrator as an intermediary component that receives data from multiple independent outcome models operating on different content channels. The orchestrator coordinates these models by collecting their individual attribution results and applying resolution logic to produce a unified, non-conflicting attribution. This mediator enables the independent models to work autonomously while ensuring reliable, consistent attributions across channels through centralized coordination.
2Object-affected harmful factors
If multiple outcome models operate independently on different data channels, then data sharing restrictions can be maintained, but attribution accuracy decreases due to unattributed outcomes
Solution Approach 1:
The patent merges the attribution results from multiple independent outcome models that operate on different data channels. The orchestrator collects attribution data from each model and combines them into a unified attribution framework. This merging process allows the system to maintain data sharing restrictions (each model keeps its data private) while achieving improved attribution precision by synthesizing insights from multiple models through the orchestrator's coordination logic.
3Adaptability or versatility
If multiple outcome models are used to predict attributions for unattributed outcomes, then more data channels can be covered, but conflicting attribution results are generated
Solution Approach 1:
The orchestrator implements a feedback mechanism that receives attribution results from multiple outcome models and applies resolution logic to detect and resolve conflicts. When conflicting attributions are identified, the orchestrator uses predefined rules and criteria to determine the most accurate attribution, providing feedback to the system. This feedback loop ensures that cross-channel coverage is maintained while attribution consistency is preserved by eliminating conflicts through systematic resolution.
Data Source
AI summary
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.