# US 11,906,647 B2 · Person location determination using multipath

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- Number: US 11,906,647 B2
- Document ID: US11906647B2
- Status: Granted
- Kind: B2
- Application number: 18/139,612
- Filed: 2023-04-26
- Published: 2023-11-02
- Granted: 2024-02-20
- Earliest claimed: 2022-04-29
- Assignees: Koko Home, Inc.
- Applicants: Koko Home, Inc.
- Inventors: Itay Cnaan-On; Ali Rostami; Bradley Michael Eckert; Saurabh Gupta
- Web page: https://patentgrep.com/patent/US11906647B2

## CPC

- G01S 5/0273: {using multipath or indirect path propagation signals in position determination}
- G01S 13/89: for mapping or imaging
- G01S 7/417: {involving the use of neural networks}
- G01S 13/42: Simultaneous measurement of distance and other co-ordinates (indirect measurement G01S13/46)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES

## Abstract

In one aspect, a method of determining a location of a user within an indoor space, includes emitting a radiofrequency signal into the indoor space, receiving backscattered training radiofrequency signals, including multipath, for at least one location within the indoor space, converting the received training signals into a point cloud for each location of the at least one location, assigning a signature for each location based on the point cloud for each location, receiving additional radiofrequency signals, including multipath, converting the additional radiofrequency signals into an additional point cloud, and determining a location of the user by comparing the additional point cloud to the assigned signatures.

## Claims

### Claim 1

A method of determining a location of a user within an indoor space, comprising:
emitting a radiofrequency signal into the indoor space;
receiving backscattered training radiofrequency signals, including multipath, for at least one location within the indoor space;
converting the received training signals into a point cloud for each location of the at least one location;
assigning a signature for each location based on the point cloud for each location;
receiving additional radiofrequency signals, including multipath;
converting the additional radiofrequency signals into an additional point cloud; and
determining a location of the user by comparing the additional point cloud to the assigned signatures.

### Claim 2 (depends on 1)

The method of claim 1, wherein the assigning a signature includes training a machine learning model with the point cloud and wherein the determining the location is performed by the trained machine learning model.

### Claim 3 (depends on 2)

The method of claim 2, wherein the machine learning model incorporates a k-nearest neighbors algorithm, a support vector machine algorithm, or naive bayes algorithm for matching the additional point cloud to the recorded training signature for each location.

### Claim 4 (depends on 1)

The method of claim 1, further comprising receiving training doppler data as the user performs an action and assigning a doppler signature associated with the user action.

### Claim 5 (depends on 4)

The method of claim 4, further comprising receiving additional doppler data in conjunction with the additional radiofrequency signals to determine a current user action.

### Claim 6 (depends on 5)

The method of claim 5, performing an intervention based on the determined location and the determined current user action.

### Claim 7 (depends on 6)

The method of claim 6, wherein the determined current user action includes falling.

### Claim 8 (depends on 1)

The method of claim 1, further comprising determining at least one vital sign from the additional radiofrequency signals and performing an intervention based on the determined location and the determined at least one vital sign.

### Claim 9 (depends on 8)

The method of claim 8, wherein the intervention includes providing feedback to a user to improve sleep.

### Claim 10

A non-transitory computer readable medium having stored thereon instructions to cause to a computer to execute a method, the method comprising:
receiving backscattered training radiofrequency data, including multipath, for at least one location within an indoor space;
converting the received training data into a point cloud for each location of the at least one location;
assigning a signature for each location based on the point cloud for each location;
receiving additional radiofrequency data, including multipath;
converting the additional radiofrequency data into an additional point cloud; and
determining a location of a user by comparing the additional point cloud to the assigned signatures.

### Claim 11

A system, comprising:
at least one processor;
at least one antenna configured to emit a radiofrequency signal into an indoor space;
a non-transitory memory storing instructions that cause one or more processors of the at least one processor to execute a method comprising:
receiving backscattered training radiofrequency data, including multipath, for at least one location within the indoor space;
converting the received training data into a point cloud for each location of the at least one location;
assigning a signature for each location based on the point cloud for each location;
receiving additional radiofrequency data, including multipath;
converting the additional radiofrequency data into an additional point cloud; and
determining a location of a user by comparing the additional point cloud to the assigned signatures.

### Claim 12 (depends on 11)

The system of claim 11, wherein the assigning a signature includes training a machine learning model with the point cloud and wherein the determining the location is performed by the trained machine learning model.

### Claim 13 (depends on 12)

The system of claim 12, wherein the machine learning model incorporates a k-nearest neighbors algorithm, a support vector machine algorithm, or naive bayes algorithm.

### Claim 14 (depends on 11)

The system of claim 11, wherein the method further includes receiving training doppler data as the user performs an action and assigning a doppler signature associated with the user action.

### Claim 15 (depends on 14)

The system of claim 14, wherein the method further includes receiving additional doppler data in conjunction with the additional radiofrequency signals to determine a current user action.

### Claim 16 (depends on 15)

The system of claim 15, wherein the method further comprises performing an intervention based on the determined location and the determined current user action.

### Claim 17 (depends on 16)

The system of claim 16, wherein the determined current user action includes falling.

### Claim 18 (depends on 11)

The system of claim 11, wherein the method further comprises determining at least one vital sign from the additional radiofrequency signals and performing an intervention based on the determined location and the determined at least one vital sign.

### Claim 19 (depends on 18)

The system of claim 18, wherein the intervention includes providing feedback to the user to improve sleep.

### Claim 20 (depends on 11)

The system of claim 11, wherein the method further comprises performing analysis of user movement patterns over a predetermined time based on the determined locations over the predetermined time.