PATENT CLAIM ANALYSIS

Application Number: 15873806
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-06
Patent Classification: ["375", "298000"]

Abstract:
Method, apparatus and systems for object tracking are disclosed. In one example, a disclosed method includes obtaining at least one time series of channel information (CI) of a wireless multipath channel using: a processor, a memory communicatively coupled with the processor and a set of instructions stored in the memory. The at least one time series of channel information is extracted from a wireless signal transmitted between a Type 1 heterogeneous wireless device at a first position in a venue and a Type 2 heterogeneous wireless device at a second position in the venue through the wireless multipath channel. The wireless multipath channel is impacted by a current movement of an object in the venue. The method also includes determining a spatial-temporal information of the object based on at least one of: the at least one time series of channel information, a time parameter associated with the current movement, and a past spatial-temporal information of the object. The at least one time series of channel information is preprocessed. Associated computation may be shared among the processor, the Type 1 heterogeneous wireless device and the Type 2 heterogeneous wireless device.

Claim (Index 26):
The system of  claim 21 , wherein the object tracking server, the Type 1 heterogeneous device and the Type 2 heterogeneous device are further configured to:\n preprocess the at least one time series of channel information, which comprises at least one of: doing nothing, de-noising, smoothing, conditioning, enhancement, restoration, feature extraction, weighted averaging, low-pass filtering, bandpass filtering, high-pass filtering, median filtering, ranked filtering, quartile filtering, percentile filtering, mode filtering, linear filtering, nonlinear filtering, finite impulse response (FIR) filtering, infinite impulse response (IIR) filtering, moving average (MA) filtering, auto-regressive (AR) filtering, auto-regressive moving average (ARMA) filtering, thresholding, soft thresholding, hard thresholding, soft clipping, local maximization, local minimization, optimization of a cost function, neural network, machine learning, supervised learning, unsupervised learning, semi-supervised learning, transform, Fourier transform, Laplace, Hadamard transform, transformation, decomposition, selective filtering, adaptive filtering, derivative, first order derivative, second order derivative, higher order derivative, integration, zero crossing, indicator function, absolute conversion, convolution, multiplication, division, another transform, another processing, another filter, a third function, and another preprocessing; and compute a similarity score based on a pair of temporally adjacent CI of the time series of CI,\n wherein the similarity score is at least one of: a time reversal resonating strength (TRRS), a correlation, a cross-correlation, an auto-correlation, a covariance, a cross-covariance, an auto-covariance, an inner product of two vectors, a distance score, a discriminating score, a metric, a neural network output, a deep learning network output, and another score, and \n wherein the channel information is associated with at least one of:\n signal strength, signal amplitude, signal phase, \n attenuation of the wireless signal through the wireless multipath channel, \n received signal strength indicator (RSSI), \n channel state information (CSI), \n an equalizer information, \n a channel impulse response, \n a frequency domain transfer function, \n information associated with at least one of: a frequency band, a frequency signature, a frequency phase, a frequency amplitude, a frequency trend, a frequency characteristics, a frequency-like characteristics, an orthogonal decomposition characteristics, and a non-orthogonal decomposition characteristics, \n information associated with at least one of: a time period, a time signature, a time amplitude, a time phase, a time trend, and a time characteristics, \n information associated with at least one of: a time-frequency partition, a time-frequency signature, a time-frequency amplitude, a time-frequency phase, a time-frequency trend, and a time-frequency characteristics, \n information associated with a direction, an angle of arrival, an angle of a directional antenna, and a phase, and \n another channel information, \n of the wireless signal through the wireless multipath channel, \n \n wherein the spatial-temporal information of the object is determined based on the similarity score.

Metadata:
- Claim Count in Document: 80.0
- Percentile: 86.0
- Lexical Diversity: 2.60759
- Patent Class: 375.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15384217', '16060710', '15612630', '14605611', '12328917']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.50346820710499
- 35 USC 102 Novelty (BERT): 0.4883006410412221
- Combined Prediction Score: 0.5019514504986131
- Mean Citation Score: 148.795826
- Max Citation Score: 163.84986999999995
- Similarity Product: 104.84283896781204

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test