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 19):
The object tracking server of  claim 11 , wherein the set of instructions causes the object tracking server further to:\n compute at least one time series of power information (PI) based on the at least one time series of CI,\n wherein each PI is associated with a CI, with real part of the PI computed based on at least one of: magnitude, square of magnitude, phase, real part, imaginary part and another function, of the CI; \n compute a first function of the at least one time series of PI,\n wherein the first function comprises an operation on at least one of: an autocorrelation function, a square of autocorrelation function, an auto-covariance function, a square of auto-covariance function, an inner product, an auto-correlation-like function, and a covariance-like function, \n wherein the operation comprises at least one of: a polynomial function, a linear function, a nonlinear function, a filtering, a de-noising, a smoothing, a conditioning, an enhancement, a restoration, a feature extraction, a weighted averaging, a high-pass filtering, a low-pass filtering, a bandpass filtering, a median filtering, a quartile filtering, a percentile filtering, a mode filtering, a linear filtering, a nonlinear filtering, a finite impulse response (FIR) filtering, an infinite impulse response (IIR) filtering, a moving average (MA) filtering, an auto-regressive (AR) filtering, an auto-regressive moving average (ARMA) filtering, a selective filtering, an adaptive filtering, a thresholding, a soft thresholding, a hard thresholding, a soft clipping, a first derivative, a second derivative, a higher order derivative, a local maximization, a local minimization, an optimization of a cost function, a neural network, a machine learning, a supervised learning, an unsupervised learning, a semi-supervised learning, a zero crossing, an absolute function, an indicator function, a Fourier transform, a Laplace transform, a Hadamard transform, another transform, a transformation, a decomposition, a derivative, a first order derivative, a second order derivative, a higher order derivative, a convolution, a multiplication, a division, zero crossing, an indicator function, an absolute conversion, a preprocessing, a post-processing, and another operation; \n determine at least one characteristics of the first function,\n wherein the at least one characteristics comprises: a local maximum, a local minimum, a local extremum, a constrained (with argument within a constraint) maximum, a constrained minimum, a constrained extremum, a maximum slope, a minimum slope, a constrained maximum slope, a constrained minimum slope, a maximum higher order derivative, a minimum higher order derivative, a constrained higher order derivative, a zero-crossing, a constrained zero-crossing, a zero-crossing of slope, a zero-crossing of higher order derivative, and another characteristics; and \n identify at least one argument of the first function associated with the at least one characteristics of the first function, wherein the spatial-temporal information of the object is determined based on the at least one argument of the first function.

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): 0.5038890196350864
- 35 USC 102 Novelty (BERT): 0.4867060850255523
- Combined Prediction Score: 0.5021707261741329
- Mean Citation Score: 148.795826
- Max Citation Score: 163.84986999999995
- Similarity Product: 106.4601727210015

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