PATENT CLAIM ANALYSIS

Application Number: 16203299
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-03
Patent Classification: ["370", "252000"]

Abstract:
Apparatus, systems and methods for recognizing and classifying events in a venue based on a wireless signal are disclosed. In one example, a disclosed system comprises a first transmitter, a second transmitter, at least one first receiver, at least one second receiver, and an event recognition engine, in the venue. The first transmitter transmits a training wireless signal through a wireless multipath channel impacted by a known event in the venue in a training time period associated with the known event. Each first receiver receives asynchronously the training wireless signal, and obtains, asynchronously based on the training wireless signal, at least one time series of training channel information of the wireless multipath channel between the first receiver and the first transmitter. The second transmitter transmits a current wireless signal through the wireless multipath channel impacted by a current event in a current time period associated with the current event. Each second receiver receives asynchronously the current wireless signal, and obtains, asynchronously based on the current wireless signal, at least one time series of current channel information of the wireless multipath channel between the second receiver and the second transmitter. The event recognition engine trains a classifier based on the training channel information; and apples the classifier to: classify the current channel information and associate the current event with at least one of: a known event, an unknown event and another event.

Claim (Index 27):
An event recognition engine of a wireless monitoring system, comprising:\n a processor; a memory communicatively coupled with the processor; and a set of instructions stored in the memory which, when executed, causes the processor to perform:\n for each of at least one known event happening in a venue in a respective training time period, obtaining, from each of at least one first receiver in the venue, at least one time series of training channel information (training CI time series) of a wireless multipath channel impacted by the known event, wherein the first receiver extracts the training CI time series from a respective training wireless signal received from a first transmitter in the venue through the wireless multipath channel between the first receiver and the first transmitter in the training time period associated with the known event, \n training at least one classifier for the at least one known event based on the at least one training CI time series, \n for a current event happening in the venue in a current time period, obtaining, from each of at least one second receiver in the venue, at least one time series of current channel information (current CI time series) of the wireless multipath channel impacted by the current event, wherein the second receiver extracts the current CI time series from a current wireless signal received from a second transmitter in the venue through the wireless multipath channel between the second receiver and the second transmitter in the current time period associated with the current event, and \n applying the at least one classifier to:\n classify at least one of: the at least one current CI time series, a portion of a particular current CI time series, and a combination of the portion of the particular current CI time series and a portion of an additional CI time series, and \n associate the current event with at least one of: a known event, an unknown event and another event, wherein a training CI time series associated with a first receiver and a current CI time series associated with a second receiver have at least one of: \n different starting times, \n different time durations, \n different stopping times, \n different counts of items in their respective time series, \n different sampling frequencies, \n different sampling periods between two consecutive items in their respective time series, and \n channel information (CI) with different features.

Metadata:
- Claim Count in Document: 35.0
- Percentile: 98.0
- Lexical Diversity: 3.80597
- Patent Class: 370.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16203317', '16200616', '16101444', '15873806', '16060710']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5514142349193574
- 35 USC 102 Novelty (BERT): 0.494499848827065
- Combined Prediction Score: 0.5457227963101281
- Mean Citation Score: 255.455284
- Max Citation Score: 274.34125
- Similarity Product: 228.50950161427247

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