US 2026/0300783 A1Published application

Computer-based systems and methods for machine learning based exception predictions

OwnerNeha Singh, Jenny Lin +14inventors

InventorsNeha Singh, Jenny Lin, Santosh Vazarkar, Kishore Seshagiri, Christopher Bell, Fabian Knoch, Martin Walker, Matthew Pountain, Jitendra Kumnar, Ravikrishna Kodamagulla, Prabhakar Akella, Sebastian Castillosanchez, Vivekananda Avvari Naga, Srinivas Sagi, Jom J Varghese, Carolyn Jones

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Abstract

A failure prediction method including a predicting flow and a model training flow, the predicting flow including receiving a natural language input from a client computer, translating the input into a task by a LLM, selecting a ML model dedicated to the task, receiving first data, converting the first data to second data of a predetermined format, immediately applying, the ML model on the second data for predicting an output and providing a corresponding explanation, storing the second data and the output into historical data in a storage layer, translating the output and the explanation into a prediction in the natural language by the LLM, and transmitting the prediction to the client computer and iterating the predicting flow for a predetermined number of time; and the model training flow including retrieving the historical data from the storage layer, and training the ML model on the historical data.

Claims20

Dependent claims

A method, comprising:

predicting, by at least one computing device, an output and corresponding explanation associated with first data, wherein the predicting comprises:
receiving, by at least one computing device, at least one input in a natural language from a first client computer;
translating, by the at least one computing device executing a large language model (LLM), the at least one input into at least one task;
selecting, by the at least one computing device, at least one predetermined machine learning (ML) model dedicated to the at least one task;
receiving, by the at least one computing device, the first data from at least one second client computer;
converting, by the at least one computing device, the first data to second data of a predetermined format;
immediately applying, by the at least one computing device, the at least one predetermined ML model on the second data for predicting the output and providing the explanation;
storing, by the at least one computing device, the second data and the output into historical data in a storage layer;
translating, by the at least one computing device executing the LLM, the output and the explanation into a prediction in the natural language;
and
transmitting, by the at least one computing device, the prediction to the first client computer;
iterating, by the at least one computing device, the predicting for a predetermined number of time;
retrieving, by at least one computing device, the historical data from the storage layer;
and
training, by the at least one computing device, the at least one predetermined machine learning (ML) model on the historical data, wherein the training comprises:
extracting, by the at least one computing device, a plurality of attributes form the historical data;
identifying, by the at least one computing device, relevant attributes from the plurality of attributes for predictive analysis;
generating, by the at least one computing device, a train dataset from a first portion of the historical data associated with the relevant attributes;
generating, by the at least one computing device, a test dataset from a second portion of the historical data associated with the relevant attributes;
training, by the at least one computing device, the at least one predetermined ML model on the train dataset;
and
testing, by the at least one computing device, the at least one predetermined ML model on the test dataset.

19 more elements

A system comprising:

one or more processors;
and
a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to:
predict an output and corresponding explanation associated with first data, wherein the predicting comprises:
receiving at least one input in a natural language from a first client computer;
translating, by executing a large language model (LLM), the at least one input into at least one task;
selecting at least one predetermined machine learning (ML) model dedicated to the at least one task;
receiving the first data from at least one second client computer;
converting, the first data to second data of a predetermined format;
immediately applying the at least one predetermined ML model on the second data for predicting the output and providing the explanation;
storing the second data and the output into historical data in a storage layer;
translating, by executing the LLM, the output and the explanation into a prediction in the natural language;
and
transmitting the prediction to the first client computer;
iterate the predicting for a predetermined number of time;
retrieve the historical data from the storage layer;
and
train the at least one predetermined machine learning (ML) model on the historical data, wherein the training comprises:
extracting a plurality of attributes form the historical data;
identifying relevant attributes from the plurality of attributes for predictive analysis;
generating a train dataset from a first portion of the historical data associated with the relevant attributes;
generating a test dataset from a second portion of the historical data associated with the relevant attributes;
training the at least one predetermined ML model on the train dataset;
and
testing the at least one predetermined ML model on the test dataset.

22 more elements

Dependencies are inferred from the claim text.