Machine Learning Model Based Condition and Property Detection

Number: 20220384040
Published: 2022-12-01
Filed: 2022-05-24
Assignee: Disney Enterprises Inc.
Inventors: Comito; Keith, Hale; Gregory Brooks, Kumar; Komath Naveen
CPC: G16H50/70; G06N20/20; G16H40/67; G06N3/045; G16H50/20
Verdict: Set aside health/medical ML, unrelated domain
Source: Google Patents · FreePatentsOnline

Abstract

A system for performing machine language (ML) model based condition and property detection includes a computing platform having processing hardware and a system memory storing a software code that includes a trained ML model. The processing hardware is configured to execute the software code to receive a dataset, and perform an analysis of the dataset, using a first stage of the trained ML model, to detect a presence of a predetermined data attribute. The processing hardware is further configured to execute the software code to predict, using a second stage of the trained ML model when the analysis of the dataset detects the presence of the predetermined data attribute, a probability that the predetermined data attribute is indicative of a condition or a property.

Background

BACKGROUND

Condition and property detection, such as various types of diagnostics for instance, may be performed in a variety of ways that can differ considerably depending on the condition or property serving as the subject of analysis, but often rely on manual processes. As one example, medical diagnostics often require extraction and testing of a blood or tissue sample, or expert review and interpretation of images or test results generated by sophisticated testing equipment such as computerized tomography (CT) or magnetic resonance imaging (MRI) scanners, electrocardiogram (ECG) machines, and the like. As another example, diagnostics performed on industrial equipment or other machines typically require human inspection, or at the very least review of sensor data by a trained human technician. Despite the diversity of the diagnostic techniques in use, a common element among many is the need for a human having some level of expertise to participate in the process. However, given the costliness of such human involvement, there exists a need in the art for automated solutions capable of inferentially interpreting diagnostic data.

Claims

1. A system comprising: a computing platform including a processing hardware and a system memory; a software code including a trained machine learning (ML) model stored in the system memory; the processing hardware configured to execute the software code to: receive a dataset; perform an analysis of the dataset, using a first stage of the trained ML model, to detect a presence of a predetermined data attribute; and predict, using a second stage of the trained ML model when the analysis of the dataset detects the presence of the predetermined data attribute, a probability that the predetermined data attribute is indicative of a condition or a property. || 7. The system of claim || 1: wherein performing the analysis of the dataset comprises detecting, using the first stage of the trained ML model, one or lore temporal segments of the dataset that include the predetermined data attribute, and wherein predicting, using the second stage of the trained ML model, predicts whether at least one of the one or more temporal segments including the predetermined data attribute is indicative of the condition or the property. || 11. A method for use by a system including a computing platform having a processing hardware and a system memory storing a software code including a trained machine learning (ML) model, the method comprising: receiving, by the software code executed by the processing hardware, a dataset; performing an analysis of the dataset, by the software code executed by the processing hardware and using a first stage of the trained ML model, to detect a presence of a predetermined data attribute; and predicting, by the software code executed by the processing hardware and using a second stage of the trained ML model when the analysis of the dataset detects the presence of the predetermined data attribute, a probability that the predetermined data attribute is indicative of a condition or a property. || 15. The method of clan herein the dataset includes or is derived from time-based diagnostic test data. || 17. The method of claim || 11: wherein performing the analysis of the dataset comprises detecting, using the first stage of the trained ML model one or more temporal segments of the dataset that include the predetermined data attribute, and. wherein the predicting, using the second stage of the trained ML model, predicts whether at least one of the one or more temporal segments including the predetermined data. attribute is indicative of the condition or the property.