PATENT CLAIM ANALYSIS

Application Number: 16055403
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["455", "456100"]

Abstract:
Systems and methods for discovering a device having an unknown location includes: a first signal source having a first known location and a second signal source having a second known location, the first signal source and the second signal source transmit radio signals using a first radio frequency band during a first search cycle; a sampling receiver having a known location that samples radio signals transmitted by the first signal source and the second signal source during the first search cycle; a disoriented sampling receiver having an unknown location that samples radio signals transmitted by the first signal source and the second signal source during the first search cycle; and a signal processor: aggregates the samples of the radio signals from the sampling receiver and the disoriented sampling receiver; analyzes the aggregated samples of the radio signals; and determines a first set of possible locations of the disoriented sampling receiver.

Claim (Index 11):
The method according to  claim 10 , further comprising:\n implementing a trained machine learning model that predicts the second radio frequency band used in reducing the possible locations of the at least one radio signal receiver having the unknown location, wherein the implementing includes:\n providing machine learning input into the trained machine learning model, machine learning input comprising two or more of:\n superposition patterns of coherence signals identified from the one or more samples of the radio signals collected by the at least one radio signal receiving devices having the known location and by the at least one radio signal receiving device having the unknown location during the first period, \n the known locations of the two or more signal sources and the sampling receiver, and \n a phase angle between sub-carrier signals; and \n \n identifying, by the trained machine learning model, the second radio frequency band based on the machine learning input.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 96.0
- Lexical Diversity: 3.3125
- Patent Class: 455.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['10615964', '10717317', '11003460', '12852443', '14855559']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6659545015773007
- 35 USC 102 Novelty (BERT): 0.4764822335101555
- Combined Prediction Score: 0.6470072747705863
- Mean Citation Score: 143.587084
- Max Citation Score: 147.57056
- Similarity Product: 103.87586412849426

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

Dataset: test