Calibrating a machine-learning model in a data processing environment
A system combining top-down and bottom-up approaches with A/B testing enhances marketing model accuracy and flexibility, enabling continuous evaluation and adjustment of marketing strategies and budget allocations in digital environments.
WO2026117654A1PCT designated stage Publication Date: 2026-06-04BOSTON CONSULTING GRP INC
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- BOSTON CONSULTING GRP INC
- Filing Date
- 2025-11-26
- Publication Date
- 2026-06-04
Smart Images
Figure US2025057231_04062026_PF_FP_ABST
Abstract
A method implemented by a data processing system for calibrating a machine learning model, includes training a machine learning (ML) model, wherein the ML model comprises a plurality of nodes that are connected through edges and are aggregated into a plurality of layers comprising at least an input layer and an output layer, wherein each of the edges is configured to transmit a signal from one node to another node, and wherein an output of each of the plurality of nodes is computed based on inputs of the nodes in accordance with a plurality of weights; generating one or more A / B tests that are related to the area to be improved, determining one or more outcomes from executing the one or more A / B tests; and calibrating the ML model in accordance with the determined one or more outcomes.
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