PATENT CLAIM ANALYSIS

Application Number: 16055361
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["342", "451000"]

Abstract:
Systems and methods of using a machine learning model to detect physical characteristics of an environment based on radio signal data include at a radio signal receiver, collecting noise floor signal data comprising radio signal data from an environment within a predetermined proximity of the radio signal receiver; implementing a trained deep machine learning classifier that is trained to classify one or more physical characteristics of the environment based on the radio signal data; generating machine learning input based on the radio signal data collected by the radio signal receiver; receiving the machine learning input at the trained deep machine learning classifier; and generating by the trained deep machine learning model one or more classification labels identifying the one or more physical characteristics of the environment based on the noise floor signal data.

Claim (Index 1):
A system for deploying a deep machine learning classifier that classifies pre-trained sensory conditions within a physical environment based on radio frequency noise floor signal data, the system comprising:\n one or more radio signal receivers that:\n are set to receive radio signals from an unused radio frequency band, wherein the unused radio frequency band relates to a radio frequency band that is not used by one or more active radio signal sources within a predetermined distance of the one or more radio signal receivers; \n collect radio signal data within the unused radio frequency band from one or more regions within the predetermined distance of each of the one or more radio signal receivers; \n a machine learning system that includes a trained deep machine learning classifier, wherein the trained deep machine learning classifier is trained to identify pre-trained sensory conditions based on the collected radio signal data, wherein the machine learning system:\n receives machine learning input comprising the collected radio signal data from the one or more radio signal receivers; \n outputs one or more classification labels that identifies one or more pre-trained sensory conditions within the one or more regions based on the collected radio signal data.

Metadata:
- Claim Count in Document: 41.0
- Percentile: 96.0
- Lexical Diversity: 2.62264
- Patent Class: 342.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15955485', '14330202', '15961465', '14879787', '15604473']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5519062409585674
- 35 USC 102 Novelty (BERT): 0.4807185507025425
- Combined Prediction Score: 0.544787471932965
- Mean Citation Score: 190.833904
- Max Citation Score: 201.8988
- Similarity Product: 144.58870357918738

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