Multi-output model for joint individual feature prediction
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Solution Overview
Problem
Existing approaches fail to effectively predict individual-level information from aggregate data, particularly when multiple unobserved features are involved, limiting insights in fields like healthcare and e-commerce where privacy and cost constraints lead to data aggregation.
Innovation Solution
An analytics system that transforms individual-level data into a higher dimensional space, generates bag-wise mean embeddings, and trains a multi-output model using both transformed data and aggregate information to predict unobserved features, enabling joint prediction across multiple features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is aggregated across a population to ensure privacy and reduce storage costs, then privacy protection and storage efficiency are improved, but the ability to gain meaningful insights from individual-level information deteriorates
Solution Approach 1:
The patent introduces an intermediary model (multi-output model with bag-wise mean embeddings) that mediates between aggregate data and individual-level predictions. The model acts as a bridge, taking aggregate statistics as input and producing individual-level feature predictions without requiring access to actual individual-level data, thus preserving privacy while recovering useful information.
Solution Approach 2:
The patent transforms the problem by changing parameters from individual-level data to aggregate statistics as input, and uses a multi-output model to predict multiple individual-level features simultaneously. The transformation to higher dimensional space with bag-wise mean embeddings allows the model to work with aggregated parameters while recovering individual-level information.
2Device complexity
If traditional data analysis approaches are used on aggregate data, then computational simplicity is maintained, but the precision of predicting individual-level features deteriorates
Solution Approach 1:
The patent applies dimensionality change by transforming individual-level information to a higher dimensional space using bag-wise mean embeddings. This transformation allows the model to capture complex relationships in aggregate data and predict multiple individual-level features with higher precision, overcoming the limitations of traditional approaches that work directly with raw aggregate statistics.
Solution Approach 2:
The multi-output model serves multiple functions simultaneously: it predicts multiple unobserved individual-level features from aggregate data, captures inter-task relationships between different features, and works with transformed high-dimensional representations. This universal approach improves prediction accuracy across multiple features rather than treating each prediction task separately.
3Device complexity
If separate models are trained for each unobserved feature, then model simplicity is maintained, but the ability to capture inter-task relationships deteriorates
Solution Approach 1:
The patent merges multiple prediction tasks into a single multi-output model that simultaneously predicts multiple unobserved individual-level features. By combining these tasks, the model captures inter-task relationships and correlations between different features, improving overall prediction accuracy compared to training separate models independently.
Solution Approach 2:
The multi-output model performs multiple prediction functions simultaneously, capturing relationships between different unobserved features while working with aggregate data. This universal model structure allows the system to leverage correlations across tasks, improving prediction performance for all features together rather than in isolation.
Data Source
AI summary
An analytics system jointly predicts values for multiple unobserved individual-level features using aggregate data for those features. Given a dataset, a transformation is applied to individual-level information for the dataset to generate transformed data in a higher dimensional space. Bag-wise mean embeddings are generated using the transformed data. The bag-wise mean embeddings and aggregate data for unobserved individual-level features for the dataset are used to train a model to jointly predict values for the unobserved individual-features for data instances. In particular, a given data instance can be transformed to a representation in a higher dimensional space. Given this representation, the trained model predicts values for the unobserved individual-level features for the data instance, and the data instance can be augmented with the predicted values.