Back to results
US 12,340,282 B2Granted
Anomaly detection and resolution
AssigneeMicron Technology, Inc.
InventorsDmitry Vengertsev, Zahra Hosseinimakarem, Marta Egorova
Life of this patent
Estimating the term…
Abstract
Methods, apparatuses, and systems associated with anomaly detection and resolution are described. Examples can include detecting, via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment and classifying the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model. Examples can include proceeding with the task responsive to classification of the object as a non-anomaly and the environment as non-anomalous and resolving the anomaly or the anomalous environment and proceeding with the task responsive to classification of the object as an anomaly or the environment as anomalous.
Claims20
A method, comprising:
- detecting at a processing resource and via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment;
- classifying at the processing resource the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a first machine learning model;
- responsive to the processing resource's classification of the object as an anomaly or the environment as anomalous using the first machine learning model, utilizing a second machine learning model to confirm whether or not the object or the environment is anomalous and what type of anomaly the object or the environment is;
- proceeding with the task responsive to the processing resource's classification and confirmation of the object as a non-anomaly and the environment as non-anomalous;
- determining a plurality of potential resolutions to the anomaly or the anomalous environment responsive to the processing resource's classification and confirmation of the object as an anomaly or the environment as anomalous;
- selecting one of the plurality of potential resolutions utilizing a third machine learning model;
- resolving the anomaly or the anomalous environment utilizing the selected potential resolution before proceeding with the task;
- detecting a new object or an addition to the environment;
- classifying the new object or the addition to the environment as an anomaly until a decision is made otherwise based on receipt of user instructions;
- collecting data associated with the new object or the addition to the environment;
- and
- training the first machine learning model based on the collected data and the user instructions.
9 more elements
An apparatus, comprising:
- a processing resource;
- and
- a memory resource in communication with the processing resource having instructions executable to:
- detect, via a sensor of the apparatus, an object in a path of the apparatus while the apparatus is performing a cleaning task in an environment;
- classify the object in the path of the apparatus as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a first machine learning model based on historical data associated with an object previously detected by the sensor;
- responsive to the classification of the object as an anomaly or the environment as anomalous using the first machine learning model, utilize a second machine learning model to confirm whether or not the object or the environment is anomalous and what type of anomaly the object or the environment is;
- and
- determine a response to the object in the path of the apparatus based on the classification, confirmation, and the first and the second machine learning model based on historical resolution data associated with the object previously detected by the sensor, wherein the response comprises instructions executable to:
- remove the object in the path of the apparatus and proceed with the cleaning task responsive to classification of the object in the path of the apparatus as a non-anomaly and the environment as non-anomalous;
- determine a plurality of potential resolutions to the anomaly or the anomalous environment responsive to classification and confirmation of the object in the path of the apparatus as an anomaly or the environment as anomalous;
- select one of the plurality of potential resolutions utilizing a third machine learning model;
- resolve the anomaly or the anomalous environment utilizing the selected potential resolution before proceeding with the task;
- detect a new object or an addition to the environment;
- classify the new object or the addition to the environment as an anomaly until a decision is made otherwise based on receipt of user instructions;
- collect data associated with the new object or the addition to the environment;
- and
- train the first machine learning model based on the collected data and the user instructions.
14 more elements
A system, comprising:
- a plurality of delivery robots communicatively coupled to one another, wherein each one of the plurality of delivery robots comprises:
- a processing resource;
- and
- a memory resource in communication with the processing resource having instructions executable to:
- detect, via a sensor, an object in a path of the delivery robot while the delivery robot is performing a delivery task in an environment;
- classify the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a first machine learning model based on historical data associated with an object previously detected by the sensor and historical data received from other delivery robots of the plurality of delivery robots;
- responsive to the classification of the object as an anomaly or the environment as anomalous using the first machine learning model, utilize a second machine learning model to confirm whether or not the object or the environment is anomalous, what type of anomaly the object or the environment is, and a plurality of potential resolutions to the anomaly or the anomalous environment;
- utilizing a third machine learning model, select one of the plurality of potential resolutions as a response to the object based on the classification, the confirmation, and resolution data associated with the object previously detected by the sensor in the environment and historical data received from other delivery robots of the plurality of delivery robots, wherein the response comprises instructions executable to:
- proceed with the delivery task responsive to classification and confirmation of the object in the path of the delivery robot as a non-anomaly and the environment as non-anomalous;
- resolve the anomaly or the environment utilizing the selected potential resolution before proceeding with the delivery task responsive to classification and confirmation of the object in the path of the delivery robot as an anomaly or the environment as anomalous;
- update the first machine learning model and the second machine learning model based on the classification, confirmation, and determined response;
- detect a new object or an addition to the environment;
- classify the new object or the addition to the environment as an anomaly until a decision is made otherwise based on receipt of user instructions;
- collect data associated with the new object or the addition to the environment;
- and
- train the first machine learning model based on the collected data and the user instructions.
13 more elements
Dependencies are inferred from the claim text.