Patent ID: 11870563
Assignee: APPLE INC.
Field: Digital communication (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 12:
13. A method of predicting actions for a mobile device, the method comprising performing by the mobile device:
accessing a semi-supervised machine learning model, the semi-supervised machine learning model being trained by:
collecting a tagged sample set by, for each respective tagged sample of a plurality of tagged samples in the tagged sample set, the tagged sample set including a respective first data point that includes one or more sensor values from one or more signals emitted by one or more signal sources located within an area, a respective action of a plurality of actions, and a respective label representing the respective action at a respective location of a plurality of locations;
collecting an untagged sample set, each respective untagged sample of a plurality of untagged samples in the untagged sample set including a respective second data point, the respective second data point including one or more sensor values from one or more signals emitted by the one or more signal sources and measured by the mobile device using one or more sensors;

training the semi-supervised machine learning model using the plurality of tagged samples in the tagged sample set and the plurality of untagged samples in the untagged sample set, the semi-supervised machine learning model being trained to classify an input data point as corresponding to a particular location of the plurality of locations, the training the semi-supervised machine learning model comprising:
accessing the plurality of tagged samples in the tagged sample set, wherein the respective first data point and the respective label in each respective tagged sample form an input-output pair;
determining whether the respective second data point of the respective untagged sample is associated with a first target location of the plurality of locations; and
upon determining that the respective second data point of the respective untagged sample is associated with the first target location of the plurality of locations, assigning the respective untagged sample as a quasi-tagged sample with a respective label corresponding to the first target location;
performing the training of the semi-supervised machine learning model using the plurality of tagged samples in the tagged sample set and the quasi-tagged sample;

obtaining a current data point by measuring one or more sensor values;
inputting the current data point to the semi-supervised machine learning model to obtain a predicted location, the predicted location corresponding to a predicted action of the plurality of actions; and
performing or providing the predicted action.