# US 2025/0093159 A1 · Indoor Localization Based on Multiple Device Sensors

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- Number: US 2025/0093159 A1
- Document ID: US20250093159A1
- Status: Published application
- Kind: A1
- Application number: 18/013,327
- Filed: 2022-12-14
- Published: 2025-03-20
- Earliest claimed: 2022-12-14
- Assignees: Google Inc.
- Applicants: Google LLC
- Inventors: Dongeek Shin
- Web page: https://patentgrep.com/patent/US20250093159A1

## CPC

- G01C 21/16: by integrating acceleration or speed, i.e. inertial navigation
- G01C 21/206: {specially adapted for indoor navigation}
- G01C 21/005: {with correlation of navigation data from several sources, e.g. map or contour matching (G01C21/30 takes precedence)}
- G06Q 30/0261: {based on user location}
- H04W 4/021: Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
- H04W 4/027: {using movement velocity, acceleration information}
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Example embodiments of the present disclosure provide for an example method including obtaining location data associated with a first and second computing device. Example method can include determining an on-user device status for the each of the first and second computing devices. Example method can include obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing devices. Example method can include inputting the IMU sensor data from each of the first and second computing devices into a machine learned model. Example method can include obtaining, from the machine learned model, output data indicative of a predicted location. Example method can include comparing the output data indicative of the predicted location to data indicative of a location of a target subzone. Example method can include transmitting data which instructs a user interface of the first computing device to provide a content item for display.

## Claims

### Claim 1

A computing system, comprising:
one or more processors; and
one or more computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
obtaining location data associated with a first computing device and a second computing device, the location data indicative of the first computing device and the second computing device each being at a target location;
in response to obtaining the location data, determining an on-user device status for the each of the first and second computing devices;
in response to determining an on-user device status for each of the first and second computing devices, obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing devices;
inputting the IMU sensor data from each of the first and second computing devices into a machine learned model, wherein the machine learned model is configured to combine the IMU sensor data from each of the first and second computing devices;
obtaining, from the machine learned model, output data indicative of a predicted location;
comparing the output data indicative of the predicted location to data indicative of a location of a target subzone; and
in response to obtaining data indicative of colocation of each of the first and second computing devices within the target subzone of the target location, transmitting data which instructs a user interface of the first computing device to provide a content item for display.

### Claim 2 (depends on 1)

The system of claim 1, wherein the on-user device status for each of the first and second computing devices are indicative of each of the first and second computing devices being located on a user moving inside the target location.

### Claim 3 (depends on 1)

The system of claim 1, wherein the IMU sensor data comprises accelerometer data and gyroscope data.

### Claim 4 (depends on 3)

The system of claim 3, wherein the machine learned model is configured to transform the accelerometer data and the gyroscope data to data cartesian coordinates indicative of an absolute location.

### Claim 5 (depends on 1)

The system of claim 1, further comprising obtaining IMU sensor data from a third computing device.

### Claim 6 (depends on 5)

The system of claim 5, wherein the first computing device comprises a smartphone, the second computing device comprises a smartwatch, and the third computing device comprises earbuds.

### Claim 7 (depends on 1)

The system of claim 1, wherein the computing is performed on the first computing device.

### Claim 8 (depends on 1)

The system of claim 1, wherein the IMU sensor data from each of the first and second computing devices are combined using a fusion method.

### Claim 9 (depends on 1)

The system of claim 1, wherein the machine learned model comprises a neural network.

### Claim 10 (depends on 1)

The system of claim 1, wherein the machine learned model comprises a fully connected neural network.

### Claim 11

A computer-implemented method comprising:
obtaining location data associated with a first computing device and a second computing device, the location data indicative of the first computing device and the second computing device each being at a target location;
in response to obtaining the location data, determining an on-user device status for the each of the first and second computing devices;
in response to determining an on-user device status for each of the first and second computing devices, obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing device;
inputting the IMU sensor data from each of the first and second computing devices into a machine learned model, wherein the machine learned model is configured to combine the IMU sensor data from each of the first and second computing devices;
obtaining, from the machine learned model, output data indicative of a predicted location;
comparing the output data indicative of the predicted location to data indicative of a location of a target subzone; and
in response to obtaining data indicative of colocation of each of the first and second computing devices within the target subzone of the target location, transmitting data which instructs a user interface of the first computing device to provide a content item for display.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, wherein the on-user device status for each of the first and second computing devices are indicative of each of the first and second computing devices being located on a user moving inside the target location.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, wherein the IMU sensor data comprises accelerometer data and gyroscope data.

### Claim 14 (depends on 13)

The computer-implemented method of claim 13, wherein the machine learned model is configured to transform the accelerometer data and the gyroscope data to data cartesian coordinates indicative of an absolute location.

### Claim 15 (depends on 11)

The computer-implemented method of claim 11, further comprising obtaining IMU sensor data from a third computing device.

### Claim 16 (depends on 15)

The computer-implemented method of claim 15, wherein the first computing device comprises a smartphone, the second computing device comprises a smartwatch, and the third computing device comprises earbuds.

### Claim 17

(canceled)

### Claim 18 (depends on 11)

The computer-implemented method of claim 11, wherein the IMU sensor data from each of the first and second computing devices are combined using a fusion method.

### Claim 19 (depends on 11)

The computer-implemented method of claim 11, wherein the machine learned model comprises a neural network.

### Claim 20 (depends on 11)

The computer-implemented method of claim 11, wherein the first device is a primary computing device and the second computing device is a secondary computing device, and wherein the first device functions as a modem to facilitate transmission of data between the second device and a server computing system.

### Claim 21

A non-transitory computer readable medium embodied in a computer-readable storage device and comprising instructions that, when executed by a processor, cause the processor to perform operations, the operations comprising:
obtaining location data associated with a first computing device and a second computing device, the location data indicative of the first computing device and the second computing device each being at a target location;
in response to obtaining the location data, determining an on-user device status for the each of the first and second computing devices;
in response to determining an on-user device status for each of the first and second computing devices, obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing devices;
inputting the IMU sensor data from each of the first and second computing devices into a machine learned model, wherein the machine learned model is configured to combine the IMU sensor data from each of the first and second computing devices;
obtaining, from the machine learned model, output data indicative of a predicted location;
comparing the output data indicative of the predicted location to data indicative of a location of a target subzone; and
in response to obtaining data indicative of colocation of each of the first and second computing devices within the target subzone of the target location, transmitting data which instructs a user interface of the first computing device to provide a content item for display.