US 2026/0037567 A1Published application

Multi-machine learning model system for unstructured data

AssigneeHewlett Packard Enterprise Development LP

InventorsSatish Kumar Mopur, Narsimha Nikhil Raj Padal, Gunalan Perumal Vijayan, Sridhar Balachandriah, Kavi Chitra C

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Abstract

Systems and methods are provided for analyzing unstructured data using at least two machine learning models in a multi-machine learning model system, including (1) a supervised machine learning model that may be implemented as a transformer classifier-based entity recognition model operating on known entities (“crisp” entities), and (2) an unsupervised machine learning model that may be implemented as a transformer embedding-based model operating on unknown entities (“hazy” entities). The combination of the two models may execute a hierarchical and cascaded analysis of the input data that combines a clustering technique with a density-driven segregation of entities. Output of the multi-model system may help identify potential important information and non-relevant information to quickly examine critical incidents as well as possible non-relevant information.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, at a computer system, unstructured data generated by a remote system;
determining and tagging, by the computer system, entity categories in the unstructured data to generate tagged data, wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories;
converting, by the computer system, the entity categories to a set of key-value pairs, wherein individual ones of the set of key-value pairs comprise a respective key corresponding to a type of activity or event associated with the unstructured data and a respective value indicative of a characteristic of the type of activity or event;
providing, by the computer system, the key-value pairs to a classifier module that is configured to tokenize the tagged data, wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category;
feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities;
determining, by the computer system, clusters in the tagged data and the newly tagged data;
converting the clusters to a structured sentence based on the unstructured data;
and
providing the structured sentence to a user interface.

6 more elements

A system comprising:

a memory;
and
a processor that are configured to execute machine readable instructions stored in the memory for causing the processor to:
receive unstructured data generated by a remote system;
determine and tag entity categories in the unstructured data to generate tagged data, wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories;
convert the entity categories to a set of key-value pairs, wherein individual ones of the set of key-value pairs comprise a respective key corresponding to a type of activity or event associated with the unstructured data and a respective value indicative of a characteristic of the type of activity or event;
provide the key-value pairs to a classifier module that is configured to tokenize the tagged data, wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category;
feed back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities;
determine clusters in the tagged data and the newly tagged data;
convert the clusters to a structured sentence based on the unstructured data;
and
provide the structured sentence to a user interface.

9 more elements

A non-transitory computer-readable storage medium storing a plurality of instructions executable by a processor, the plurality of instructions when executed by the processor cause the processor to:

receive unstructured data generated by a remote system;
determine and tag entity categories in the unstructured data to generate tagged data, wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories;
convert the entity categories to a set of key-value pairs, wherein individual ones of the set of key-value pairs comprise a respective key corresponding to a type of activity or event associated with the unstructured data and a respective value indicative of a characteristic of the type of activity or event;
provide the key-value pairs to a classifier module that is configured to tokenize the tagged data, wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category;
feed back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities;
determine clusters in the tagged data and the newly tagged data;
convert the clusters to a structured sentence based on the unstructured data;
and
provide the structured sentence to a user interface.

6 more elements

Dependencies are inferred from the claim text.