PATENT CLAIM ANALYSIS

Application Number: 15960461
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["702", "181000"]

Abstract:
A variety of techniques are used automate the collection and classification of workout data gathered by a wearable physiological monitor. The classification process is staged in order to correctly and efficiently characterize a workout type. Initially, a generalized workout event is detected using motion and heart rate data. Then a location of the monitor on a user is determined. An artificial intelligence engine can then be conditionally applied (if a workout is occurring and a suitable device location is detected) to identify the type of workout. In addition to improved speed and accuracy, a workout detection process implemented in this manner can be realized with a sufficiently small computational footprint for deployment on a wearable physiological monitor.

Claim (Index 1):
A computer program product comprising computer executable code embodied in a non-transitory computer-readable medium that, when executing on a wearable physiological monitor, performs the steps of:\n receiving data from a number of sensors on the wearable physiological monitor, the data including accelerometer data and heart rate data acquired by the wearable physiological monitor; applying a threshold based on the accelerometer data and the heart rate data to identify two endpoints of an interval of increased physical activity indicative of a workout by a user of the wearable physiological monitor; dividing the data including the accelerometer data and the heart rate data into a number of sequential segments; applying a machine learning algorithm to the number of sequential segments of the accelerometer data to determine a probability that each one of the number of sequential segments includes data from one or more locations on a body of the user; selecting one of the one or more locations having a highest overall probability of being a current position from all of the number of sequential segments as a position of the wearable physiological monitor on the body of the user; and conditionally employing an automatic workout classification algorithm to detect a type of the workout only when the position is a wrist of the user, wherein the automatic workout classification algorithm includes a deep convolutional neural network trained to calculate a probability that a chunk of data including at least one of accelerometer data and heart rate data from the wearable physiological monitor during the workout is each of a number of candidate types for the workout.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 91.0
- Lexical Diversity: 1.62821
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15207924', '14750389', '15265761', '14995874', '15035090']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2048897682722811
- 35 USC 102 Novelty (BERT): 0.4906861016759719
- Combined Prediction Score: 0.2334694016126502
- Mean Citation Score: 200.28484
- Max Citation Score: 211.56848
- Similarity Product: 144.8168420456791

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test