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 11):
The method of  claim 1 , further comprising:\n aligning a first section of a first time duration of a first CI time series and a second section of a second time duration of a second CI time series; computing a map comprising a plurality of links between first items of the first section and second items of the second section, wherein each of the plurality of links associates a first item with a first time stamp with a second item with a second time stamp; computing a mismatch cost between the aligned first section and the aligned second section; applying the at least one classifier based on the mismatch cost, wherein:\n the mismatch cost comprises at least one of: an inner product, an inner-product-like quantity, a quantity based on correlation, a quantity based on covariance, a discriminating score, a distance, a Euclidean distance, an absolute distance, an L_ 1  distance, an L_ 2  distance, an L_k distance, a weighted distance, a distance-like quantity and another similarity value, between the first vector and the second vector, and a function of: an item-wise cost between a first item of the first section of the first CI time series and a second item of the second section of the second CI time series associated with the first item by a link of the map, and a link-wise cost associated with the link of the map, \n the aligned first section and the aligned second section are represented respectively as a first vector and a second vector that have a same vector length, and \n the mismatch cost is normalized by the same vector length.

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): 0.5492859188246898
- 35 USC 102 Novelty (BERT): 0.5055294449719854
- Combined Prediction Score: 0.5449102714394194
- Mean Citation Score: 255.455284
- Max Citation Score: 274.34125
- Similarity Product: 188.5899869596958

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