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US 11,537,878 B2Granted
Machine-learning models to leverage behavior-dependent processes
AssigneeCerebri AI Inc.
InventorsGabriel M. Silberman, Alain Briançon, Gregory Klose, Michael Wegan, Lee Harper, Andrew Kraemer, Arun Prakash
Life of this patent
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Abstract
Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
Claims25
A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
- obtaining, with a computer system, a first set of training data,
- the first set of training data comprising a time-series of events that are caused by an actor entity, and
- at least some events of the time-series of events comprising a plurality of attributes;
- selecting, with the computer system, a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time;
- training, with the computer system, a first machine-learning model on the first set of training data by optimizing parameters of the first machine-learning model with a first objective function based on an accuracy of the first machine-learning model in predicting attributes of the second subset based on the attributes of the first subset;
- generating, with the computer system, a virtual set of training data, comprising:
- virtual events in a third interval after the first interval;
- and
- virtual events in a fourth interval after the third interval;
- training, with the computer system, a second machine-learning model on the virtual set of training data by optimizing parameters of the second machine-learning model with a second objective function based on an accuracy of the second machine-learning model in predicting attributes of the fourth subset based on the attributes of the third subset;
- and
- storing, with the computer system, the trained second machine-learning model in memory.
9 more elements
A method, comprising:
- obtaining, with a computer system, a first set of training data,
- the first set of training data comprising a time-series of events that are caused by an actor entity, and
- at least some events of the time-series of events comprising a plurality of attributes;
- selecting, with the computer system, a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time;
- training, with the computer system, a first machine-learning model on the first set of training data by optimizing parameters of the first machine-learning model with a first objective function based on an accuracy of the first machine-learning model in predicting attributes of the second subset based on the attributes of the first subset;
- generating, with the computer system, a virtual set of training data, comprising:
- virtual events in a third interval after the first interval;
- and
- virtual events in a fourth interval after the third interval;
- training, with the computer system, a second machine-learning model on the virtual set of training data by optimizing parameters of the second machine-learning model with a second objective function based on an accuracy of the second machine-learning model in predicting attributes of the fourth subset based on the attributes of the third subset;
- and
- storing, with the computer system, the trained second machine-learning model in memory.
9 more elements
A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
- obtaining, with a computer system, a first set of training data,
- the first set of training data comprising a time-series of events that are caused by an actor entity, and
- at least some events of the time-series of events comprising a plurality of attributes;
- selecting, with the computer system, a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time;
- training, with the computer system, a first machine-learning model on the first set of training data by optimizing parameters of the first machine-learning model with a first objective function based on a first performance metric of the first machine-learning model in predicting attributes of the second subset based on the attributes of the first sub set;
- generating, with the computer system, a virtual set of training data, comprising:
- virtual events in a third interval after the first interval, and
- virtual events in a fourth interval after the third interval;
- training, with the computer system, a second machine-learning model on the virtual set of training data by optimizing parameters of the second machine-learning model with a second objective function based on a second performance metric of the second machine-learning model in predicting attributes of the fourth subset based on the attributes of the third subset;
- and
- storing, with the computer system, the trained second machine-learning model in memory.
8 more elements
Dependencies are inferred from the claim text.