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US 11,386,295 B2Granted
Privacy and proprietary-information preserving collaborative multi-party machine learning
AssigneeCerebri AI Inc.
InventorsGabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
Life of this patent
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Abstract
Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
Claims20
A tangible, non-transitory, machine-readable medium, storing instructions that when executed by one or more processors effectuate operations comprising:
- obtaining, with one or more processors, a first trained machine learning model, wherein:
- the first machine learning model is trained on a first training set that includes data the first entity is not permitted to provide to a second entity,
- the first trained machine learning model is configured to output tokens, and
- the tokens do not reveal more than a threshold amount of information about the data the first entity is not permitted to provide to the second entity;
- receiving, with one or more processors, a first set of input features with the first trained machine learning model and, in response, outputting a first token;
- and
- causing, with one or more processors, the first token and a first value associated with the first token to be input into a second trained machine learning model accessible to the second entity, wherein the first value associated with the first token is a token-context value corresponding to the first token.
4 more elements
A method, comprising:
- obtaining, with one or more processors, a first trained machine learning model, wherein:
- the first machine learning model is trained on a first training set that includes data the first entity is not permitted to provide to a second entity,
- the first trained machine learning model is configured to output tokens, and
- the tokens do not reveal more than a threshold amount of information about the data the first entity is not permitted to provide to the second entity;
- receiving, with one or more processors, a first set of input features with the first trained machine learning model and, in response, outputting a first token;
- and
- causing, with one or more processors, the first token and a first value associated with the first token to be input into a second trained machine learning model accessible to the second entity, wherein the first value associated with the first token is a token-context value corresponding to the first token.
4 more elements
Dependencies are inferred from the claim text.