公开(公告)号：US20220383189A1

公开(公告)日：2022-12-01

申请号：US17554895

申请日：2021-12-17

Applicant: Apple Inc.

Inventor： Joseph Yitan Cheng ,  Amruta Pai ,  Erdrin Azemi ,  Matthias R. Hohmann

IPC: G06N20/00 ,  G06N5/02

Abstract: Methods and systems are provided for predicting cognitive load. A computing device receives sensor measurements from sensors. The sensor measurements correspond to characteristics of a user during the performance of a task. For each sensor, the computing device derives, from the sensor measurements of the sensor, a set of features predictive of the cognitive load of the user; generates, from those features, a self-attention vector that characterizes each feature of the set of features relative to another feature; and defines a feature vector from the features and the self-attention vector. The computing device generates an input feature vector from the feature vector of at least one sensor. The computing device then uses a machine-learning model to generate an indication of the cognitive load of the user during the performance of a task from the feature vector.

公开(公告)号：US12165052B2

公开(公告)日：2024-12-10

申请号：US16945695

申请日：2020-07-31

Applicant: Apple Inc.

Inventor： Siddharth Khullar ,  Nicholas E. Apostoloff ,  Amruta Pai

IPC: G06N3/08 ,  G06N3/045 ,  G16H40/60

Abstract: In some examples, an individually-pruned neural network can estimate blood pressure from a seismocardiogram (SCG). In some examples, a baseline model can be constructed by training the model with SCG data and blood pressure measurement from a plurality of subjects. One or more filters (e.g., the filters in the top layer of the network) can be ranked by separability, which can be used to prune the model for each unseen user that uses the model thereafter, for example. In some examples, individuals can use individually-pruned models to calculate blood pressure using SCG data without corresponding blood pressure measurements.