- Number
- 12755839
- Published
- 2026-10-06
- Filed
- 2022-05-24
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Comito; Keith, Hale; Gregory Brooks, Kumar; Komath Naveen
- CPC
- G16H50/20; G06N20/20
- Verdict
- Set aside dropped in weekly review
- In edition
- 2026-W41
- Source
- Google Patents · FreePatentsOnline
The keeper's note
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 …
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 (1) 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 trained ML model including: (i) an input stage created based on a first ML model trained to detect one or more predetermined data attributes, the input stage trained using sliding window convolutions with stride length being a tunable hyperparameter of the input stage and a training dataset generated by a second ML model trained using an output of the first ML model and that has been annotated to identify the one or more predetermined data attributes, to predict bounding timestamps and to detect temporal segments of a test dataset that include the predetermined one or more data attributes, and (ii) an output stage trained using another training dataset that has been annotated to correlate the one or more predetermined data attributes with one of a condition or a property; the processing hardware configured to execute the software code to: receive a dataset; perform an analysis of the dataset, using the input stage of the trained ML model, to detect a presence of at least one predetermined data attribute, the at least one predetermined data attribute comprising an audio attribute including a voiced bilabial nasal sound or audio content corresponding to operation of a machine, wherein performing the analysis comprises automatically segmenting the dataset by identifying one or more temporal segments that include the at least one predetermined data attribute and extracting the one or more temporal segments based on bounding timestamps predicted by the input stage; and: (a) predict, by applying the output stage to the extracted one or more temporal segments when the analysis of the dataset detects the audio attribute including the voiced bilabial nasal sound, a probability that the at least one predetermined data attribute is indicative of a respiratory condition or an infectious disease status of a human subject, or (b) predict, by applying the output stage to the extracted one or more temporal segments when the analysis of the dataset detects the audio attribute including the audio content corresponding to the operation of the machine, a probability that the at least one predetermined data attribute is indicative of a malfunction by the machine. ||
4. The system of claim || 1: wherein predicting, using the output stage of the trained ML model, predicts whether at least one of the one or more temporal segments including the at least one predetermined data attribute is indicative of the respiratory condition or the infectious disease status of the human subject, or the malfunction by the machine. ||
7. 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 including: (i) an input stage created based on a first ML model trained to detect one or more predetermined data attributes, the input stage trained using sliding window convolutions with stride length being a tunable hyperparameter of the input stage and a training dataset generated by a second ML model trained using an output of the first ML model and that has been annotated to identify the one or more predetermined data attributes, to predict bounding timestamps and to detect temporal segments of a test dataset that include the predetermined one or more data attributes, and (ii) an output stage trained using another training dataset that has been annotated to correlate the one or more predetermined data attributes with one of a condition or a property, 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 the input stage of the trained ML model, to detect a presence of at least one predetermined data attribute, the at least one-predetermined data attribute comprising an audio attribute including a voiced bilabial nasal sound or audio content corresponding to operation of a machine, wherein performing the analysis comprises automatically segmenting the dataset by identifying one or more temporal segments that include the at least one predetermined data attribute and extracting the one or more temporal segments based on bounding timestamps predicted by the input stage; and: (a) predicting, by the software code executed by the processing hardware using the output stage applied to the extracted one or more temporal segments when the analysis of the dataset detects the audio attribute including the voiced bilabial nasal sound, a probability that the at least one predetermined data attribute is indicative of a respiratory condition or an infectious disease status of a human subject, or (b) predicting, by the software code executed by the processing hardware using the output stage applied to the extracted one or more temporal segments when the analysis of the dataset detects the audio attribute including the audio content corresponding to the operation of the machine, a probability that the at least one predetermined data attribute is indicative of a malfunction by the machine. ||
10. The method of claim || 7: wherein predicting, using the output stage of the trained ML model, predicts whether at least one of the one or more temporal segments including the at least one predetermined data attribute is indicative of the respiratory condition or the infectious disease status of the human subject, or the malfunction by the machine.