Intermediate Representation Alignment for Confidential Multi-Facility Models

The patent introduces an intermediate representation as a mediator between raw data and the prediction model. This intermediate representation transforms data from multiple apparatuses into a standardized format that preserves essential information for model training while removing facility-specific identifiers and confidential information. The intermediate representation acts as a buffer that enables data integration without direct exposure of sensitive raw data.

The patent applies parameter changes by transforming data from its original form into an intermediate representation with modified parameters. This transformation changes the data structure, feature representation, and information content to achieve a balance between utility for prediction modeling and protection of confidentiality. The intermediate representation uses different parameter encodings that maintain predictive value while obscuring sensitive information.

The intermediate representation serves as a common language that enables cross-facility data integration without requiring direct access to facility-specific data. Each facility can independently generate intermediate representations from their local data, and these representations can be aggregated to train a generalized prediction model. This mediator approach allows the system to learn apparatus differences and correct them while maintaining facility autonomy and data confidentiality.

The intermediate representation is designed to be universal across different facilities and apparatus types. It captures essential operational patterns and characteristics that are common to multiple facilities while accommodating facility-specific variations. This universality enables a single prediction model to be trained on aggregated data from multiple facilities and then applied generally across all of them, improving model versatility without compromising individual facility data security.

The intermediate representation enables a two-stage approach: first, each facility independently creates intermediate representations from their local data, maintaining confidentiality; second, these intermediate representations are aggregated across facilities to train a more accurate prediction model. The intermediate representation acts as the mediator that allows this collaborative model improvement without exposing sensitive raw data from any single facility.

The patent segments the data processing into distinct stages: local data transformation into intermediate representations at each facility, followed by aggregation and model training using these segmented representations. This segmentation allows each facility to contribute to the collective model improvement while maintaining independent control over their raw data, thereby improving model accuracy through more diverse training data without sacrificing confidentiality.