IP Library › Granted Patent US 12,326,512
Granted Patent B2
US 12,326,512 · App. 17/679,208 · Granted Jun 10, 2025

System and method for classifying a mode of operation of a mobile communication device in a volume based on sensor fusion

Inventors: Aviram Meidan (Yehud-Monosson, IL); Yiftach Richter (Kochav Yair, IL); Daniel Aljadeff (Kiriat Ono, IL)
Assignee: SAVERONE 2014 LTD.
G01S5/0289G01S5/06B60N2/002B60N2/0021B60N2/0024B60N2/003B60N2/0035B60N2210/40B60N2230/20B60N2230/30B60W2040/0881B60W40/09B60W40/13B60W2040/1353B60W2540/01B60W2540/221B60W2540/227B60W2540/229G01S5/021G01S13/589G01S13/931G01S2013/932G01S2013/9322
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,326,512
App. No.
17/679,208
Granted
Jun 10, 2025
Kind
B2
Abstract

A system and method for classifying a mode of operation of a mobile communication device within a defined volume, based on multiple sensors are provided herein. The method may include the following steps: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on any of: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the at least one mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; and using a computer processor to classify the mobile communication device into one of many predefined modes of operation of the mobile communication device, based on the position and the sensor readings.

Claims (39)

1. A method of classifying a mode of operation of at least one mobile communication device within a defined volume, based on at least one sensor, the method comprising:

determining, by a phone location unit, a position of the at least one mobile communication device relative to a frame of reference of the defined volume, based on at least one of: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the at least one mobile communication device and received by the phone location unit located within the defined volume and comprising a transceiver and antennas and further configured to wirelessly communicate with the at least one mobile communication device;

obtaining at least one sensor measurement related to the at least one mobile communication device, from a sensor located on at least one of: the at least one mobile communication device, within the defined volume, or outside of the defined volume; and

using a computer processor to classify the at least one mobile communication device into at least one of a plurality of predefined modes of operation of the mobile communication device, based on the position and the at least one sensor reading,

wherein inside the defined volume there is at least one person interacting with the at least one mobile communication device and wherein the mode of operation further comprises information about the at least one person, wherein the defined volume is a vehicle; and

wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a proximity sensor, said proximity sensor providing information on the proximity between the at least one mobile communication device and the at least one person, and wherein the proximity is factored in classifying the likelihood of the mode of operation, wherein a closer proximity infers a higher probability of an on-call mode of operation.

2. The method according to claim 1 , wherein the classification by the computer processor is carried out by assigning to the at least one mobile communication device a respective likelihood, to at least one mode of operation selected from a plurality of modes of operation.

3. The method according to claim 1 , wherein the modes of operation comprise at least one of: idle, on-call, sending a text message, audio input, touch screen in use, and streaming data.

4. The method according to claim 1 , wherein the obtaining the sensor measurement related to the at least one mobile communication device is from at least two inertial measurement units (IMU), wherein at least one of the IMU is located on the mobile communication device and at least one of the IMUs is located on the vehicle, and wherein a differential measurement of the IMU is used to deduce internal movement of the at least one mobile communication device within the vehicle.

5. The method according to claim 1 , wherein the position of the mobile communication unit within the defined volume is further determined to be in the vehicle's driver area, wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a motion sensor, said sensor providing information related to the vehicle's speed and wherein the vehicle's speed is factored in classifying the likelihood of the mode of operation, wherein for a mobile communication device located in the driver's area, a higher speed infers a higher probability of idle mode of operation.

6. The method according to claim 1 , wherein the vehicle comprises at least one seat with a pressure sensor indicating that said seat is occupied by the at least one person, and wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a seat pressure sensor and wherein the seat pressure sensor indication is factored in classifying the likelihood of the mode of operation, wherein a positive indication of a seat pressure sensor infers a higher probability of a use of a mobile communicating device by the at least one person occupying that seat.

7. The method according to claim 1 , wherein the at least one person is sensed by at least one vital signs sensor, and wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from at least one vital signs sensor and wherein a vital signs measurement is factored in classifying the likelihood of the mode of operation, wherein a level of vital signs outside a certain threshold or limit infers a higher probability of a non-idle mode of operation of the at least one mobile communicating device.

8. A system for classifying a mode of operation of at least one mobile communication device within a defined volume, based on at least one sensor, the system comprising:

a phone locating unit configured to determine a position of the at least one mobile communication device relative to a frame of reference of the defined volume, based on at least one of: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the at least one mobile communication device and received by the phone location unit located within the defined volume and comprising a transceiver and antennas and further configured to wirelessly communicate with the at least one mobile communication device;

a plurality of non-RF sensors configured to obtain at least one sensor measurement related to the at least one mobile communication device, from a sensor located on at least one of: the at least one mobile communication device, within the defined volume, or outside of the defined volume; and

a computer processor configured to classify the at least one mobile communication device into at least one of a plurality of predefined modes of operation of the mobile communication device, based on the position and the at least one sensor reading,

wherein inside the defined volume there is at least one person interacting with the at least one mobile communication device and wherein the mode of operation further comprises information about the at least one person;

wherein the defined volume is a vehicle; and

wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a proximity sensor, said proximity sensor providing information on the proximity between the at least one mobile communication device and the at least one person, and wherein the proximity is factored in classifying the likelihood of the mode of operation, wherein a closer proximity infers a higher probability of an on-call mode of operation.

9. The system according to claim 8 , wherein the classification by the computer processor is carried out by assigning to the at least one mobile communication device a respective likelihood, to at least one mode of operation selected from a plurality of modes of operation.

10. The system according to claim 8 , wherein the modes of operation comprise at least one of: idle, on-call, sending a text message, audio input, touch screen in use, and streaming data.

11. The system according to claim 8 , wherein the obtaining the sensor measurement related to the at least one mobile communication device is from at least two inertial measurement units (IMU), wherein at least one of the IMU is located on the mobile communication device and at least one of the IMUs is located on the vehicle, and wherein a differential measurement of the IMU is used to deduce internal movement of the at least one mobile communication device within the vehicle.

12. The system according to claim 8 , wherein the position of the mobile communication unit within the defined volume is further determined to be in the vehicle's driver area, wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a motion sensor, said sensor providing information related to the vehicle's speed and wherein the vehicle's speed is factored in classifying the likelihood of the mode of operation, wherein for a mobile communication device located in the driver's area, a higher speed infers a higher probability of idle mode of operation.

13. The system according to claim 8 , wherein the vehicle comprises at least one seat with a pressure sensor indicating that said seat is occupied by the at least one person, and wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a seat pressure sensor and wherein the seat pressure sensor indication is factored in classifying the likelihood of the mode of operation, wherein a positive indication of a seat pressure sensor infers a higher probability of a use of a mobile communicating device by a person occupying that seat.

14. The system according to claim 8 , wherein the at least one person is sensed by at least one vital signs sensor, and wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from at least one vital signs sensor and wherein a vital signs measurement is factored in classifying the likelihood of the mode of operation, wherein a level of vital signs outside a certain threshold or limit infers a higher probability of a non-idle mode of operation of the at least one mobile communicating device.

15. A non-transitory computer-readable medium for classifying a mode of operation of at least one mobile communication device within a defined volume, based on at least one sensor, the computer-readable medium comprising a set of instructions that when executed cause at least one computer processor to:

determine a position of the at least one mobile communication device relative to a frame of reference of the defined volume, based on at least one of: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the at least one mobile communication device and received by a phone location unit located within the defined volume and comprising a transceiver and antennas and further configured to wirelessly communicate with the at least one mobile communication device;

obtain at least one sensor measurement related to the at least one mobile communication device, from a sensor located on at least one of: the at least one mobile communication device, within the defined volume, or outside of the defined volume; and

classify the at least one mobile communication device into at least one of a plurality of predefined modes of operation of the mobile communication device, based on the position and the at least one sensor reading,

wherein inside the defined volume there is at least one person interacting with the at least one mobile communication device and wherein the mode of operation further comprises information about the at least one person;

wherein the defined volume is a vehicle; and

wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a proximity sensor, said proximity sensor providing information on the proximity between the at least one mobile communication device and the at least one person, and wherein the proximity is factored in classifying the likelihood of the mode of operation, wherein a closer proximity infers a higher probability of an on-call mode of operation.

16. A server for classifying a mode of operation of at least one mobile communication device within a defined volume, based on at least one sensor, the server comprising:

a network interface configured to receive, from a phone location unit, a position of the at least one mobile communication device relative to a frame of reference of the defined volume, based on at least one of: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the at least one mobile communication device and received by the phone location unit located within the defined volume and comprising a transceiver and antennas and further configured to wirelessly communicate with the at least one mobile communication device;

a memory interface configured to store at least one sensor measurement related to the at least one mobile communication device, from a sensor located on at least one of: the at least one mobile communication device, within the defined volume, or outside of the defined volume; and

a processing device configured to classify the at least one mobile communication device into at least one of a plurality of predefined modes of operation of the mobile communication device, based on the position and the at least one sensor reading,

wherein inside the defined volume there is at least one person interacting with the at least one mobile communication device and wherein the mode of operation further comprises information about the at least one person;

wherein the defined volume is a vehicle; and

wherein the obtaining at least one sensor measurement related to the at least one mobile communication device is from a proximity sensor, said proximity sensor providing information on the proximity between the at least one mobile communication device and the at least one person, and wherein the proximity is factored in classifying the likelihood of the mode of operation, wherein a closer proximity infers a higher probability of an on-call mode of operation.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 27, 2022
From: MEIDAN, AVIRAM; RICHTER, YIFTACH; ALJADEFF, DANIEL
To: SAVERONE 2014 LTD.
Reel/Frame 061883/0280 →
Continuity (2)
Provisional Application 63223980 · Jul 21, 2021
Related Publication 20230026593A1 · Jan 26, 2023
References Cited (42)
US 9055407B1 · Riemer · 2015 [cited by applicant]
US 9654932B1 · Mukhtar · 2017 [cited by examiner]
US 9681361B2 · Tuluca · 2017 [cited by examiner]
US 9714037B2 · DeRuyck · 2017 [cited by examiner]
US 9749866B2 · Caldwell · 2017 [cited by examiner]
US 9854086B1 · McSchooler · 2017 [cited by examiner]
US 10075581B2 · Cohen · 2018 [cited by examiner]
US 10158977B2 · Cordova · 2018 [cited by examiner]
US 10278039B1 · Matus · 2019 [cited by examiner]
US 10412212B2 · Cohen · 2019 [cited by examiner]
US 10447846B2 · Moir · 2019 [cited by examiner]
US 10557917B2 · Shoshan et al. · 2020 [cited by applicant]
US 10785604B1 · Kumar · 2020 [cited by examiner]
US 10872618B2 · Phan Le · 2020 [cited by examiner]
US 11038801B2 · McKeefery · 2021 [cited by examiner]
US 11107175B2 · Zilberman · 2021 [cited by examiner]
US 11122159B1 · Alsolami · 2021 [cited by examiner]
US 11338733B2 · Cordova · 2022 [cited by examiner]
US 12174309B2 · Meidan · 2024 [cited by examiner]
US 12231899B2 · Meidan · 2025 [cited by examiner]
US 20140187219A1 · Yang · 2014 [cited by applicant]
US 20160046298A1 · DeRuyck · 2016 [cited by applicant]
US 20160205238A1 · Abramson et al. · 2016 [cited by applicant]
US 20170105098A1 · Cordova · 2017 [cited by applicant]
US 20170150360A1 · Caldwell · 2017 [cited by examiner]
US 20170244831A1 · Tuluca · 2017 [cited by examiner]
US 20180093672A1 · Terwilliger · 2018 [cited by applicant]
US 20190387365A1 · Spruyt · 2019 [cited by examiner]
US 20200223358A1 · Cordova · 2020 [cited by applicant]
US 20200252339A1 · McKeefery · 2020 [cited by applicant]
US 20210004414A1 · Silverstein · 2021 [cited by applicant]
US 20220032924A1 · Jeihani · 2022 [cited by examiner]
US 20230023156A1 · Meidan · 2023 [cited by examiner]
US 20230027582A1 · Meidan · 2023 [cited by examiner]
US 20230185942A1 · Hanebeck · 2023 [cited by applicant]
CA 3016599 · 2017 [cited by applicant]
CN 202313288 · 2012 [cited by applicant]
CN 108921418 · 2018 [cited by applicant]
EP 2939133 · 2018 [cited by applicant]
EP 3343306 · 2019 [cited by applicant]
WO WO2023161922A1 · 2023 [cited by examiner]
Cheng et al; You're Driving and Texting: detecting drivers using personal smart phones by leveraging inertial sensors; Sep. 13, 2013 (Year 2013). [cited by applicant]