PATENT CLAIM ANALYSIS

Application Number: 16191363
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-05
Patent Classification: ["705", "002000"]

Abstract:
A device receives historical information associated with an individual to be monitored, wherein the historical information includes at least one of information associated with a health history of the individual, health histories of other individuals, activities of the individual, or activities of the other individuals. The device receives monitored information associated with the individual from one or more client devices associated with the individual, and pre-processes the monitored information to generate pre-processed monitored information that is understood by the trained machine learning model. The device processes the pre-processed monitored information, with a trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual, and performs one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities.

Claim (Index 7):
The method of  claim 1 , wherein the machine learning model includes one or more of:\n a neural network model, a deep learning model, a clustering model, a classification model, or a numerical regression model.

Metadata:
- Claim Count in Document: 47.0
- Percentile: 98.0
- Lexical Diversity: 2.92593
- Patent Class: 705.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['12949845', '16014477', '15900306', '16141636', '13869403']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1164525186687127
- 35 USC 102 Novelty (BERT): 0.4663606095155376
- Combined Prediction Score: 0.1514433277533952
- Mean Citation Score: 147.636686
- Max Citation Score: 154.60034
- Similarity Product: 116.65606092405557

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

Dataset: test