PATENT CLAIM ANALYSIS

Application Number: 16032731
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2020-01
Patent Classification: ["340", "870020"]

Abstract:
A computer-implemented method for learning a model to predict movements of a person in bed is presented. The method includes receiving first data from a plurality of first sensors installed on a bed, receiving second data from a plurality of second sensors installed on the person, and learning a model to predict the second data based on the first data by assuming a sensing range of motion intensity by the plurality of first sensors is greater than a sensing range of motion intensity by the plurality of second sensors.

Claim (Index 1):
A computer-implemented method for learning a model to predict movements of a person in bed, the method comprising:\n receiving first data from a plurality of first sensors installed on a bed; receiving second data from a plurality of second sensors installed on the person; and learning, via a learning procedure, an estimation function by employing an objective function, modified by motion intensity training data, to output adjusted motion intensity data of the person in bed to predict the second data based on the first data by assuming a sensing range of motion intensity by the plurality of first sensors is greater than a sensing range of motion intensity of the person in bed by the plurality of second sensors.

Metadata:
- Claim Count in Document: 28.0
- Percentile: 95.0
- Lexical Diversity: 2.325
- Patent Class: 340.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['10897488', '15562751', '13231639', '10898116', '11482593']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6544743032913747
- 35 USC 102 Novelty (BERT): 0.5013887752527418
- Combined Prediction Score: 0.6391657504875115
- Mean Citation Score: 136.03502799999998
- Max Citation Score: 140.51675
- Similarity Product: 80.63296236047148

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

Dataset: test