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 21):
A system having a processor, a memory communicatively coupled with the processor and a set of instructions stored in the memory for recognizing an event, comprising:\n a first transmitter in a venue and configured for:\n for each of at least one known event happening in the venue in a respective training time period , transmitting a respective training wireless signal through a wireless multipath channel impacted by the known event in the venue in the training time period associated with the known event; \n at least one first receiver in the venue, wherein each of the at least one first receiver is configured for:\n for each of the at least one known event happening in the venue,\n receiving asynchronously the respective training wireless signal through the wireless multipath channel, \n obtaining, asynchronously based on the respective training wireless signal, at least one time series of training channel information (training CI time series) of the wireless multipath channel between the first receiver and the first transmitter in the training time period associated with the known event, and \n pre-processing the at least one training CI time series; \n \n a second transmitter in the venue and configured for:\n for a current event happening in the venue in a current time period, transmitting a current wireless signal through the wireless multipath channel impacted by the current event in the venue in the current time period associated with the current event; \n at least one second receiver in the venue, wherein each of the at least one second receiver is configured for:\n for the current event happening in the venue in the current time period,\n receiving asynchronously the current wireless signal through the wireless multipath channel, \n obtaining, asynchronously based on the current wireless signal, at least one time series of current channel information (current CI time series) of the wireless multipath channel between the second receiver and the second transmitter in the current time period associated with the current event, and \n pre-processing the at least one current CI time series; and \n \n an event recognition engine configured for:\n training at least one classifier for the at least one known event based on the at least one training CI time series; 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): 0.5506356107619179
- 35 USC 102 Novelty (BERT): 0.4985361995148125
- Combined Prediction Score: 0.5454256696372074
- Mean Citation Score: 255.455284
- Max Citation Score: 274.34125
- Similarity Product: 213.90051530823112

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