Event masking

Patent metadata

Field	Value
Publication number	US-12400628-B2
Application number	US-202017634707-A
Country	US
Kind code	B2
Filing date	Aug 4, 2020
Priority date	Aug 14, 2019
Publication date	Aug 26, 2025
Grant date	Aug 26, 2025

Abstract

An apparatus, method and computer program is described for detecting a pre-event signature based on sensor data, wherein the pre-event signature is indicative of a prediction of a first event; determining an event masking action corresponding to the first event; and causing performance, in response to the detection of the pre-event signature, of the event masking action for masking the first event.

First claim

The invention claimed is:

1. An apparatus comprising at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured, with the at least one processor, to cause the apparatus to:
detect a pre-event signature based on sensor data by querying a pre-event signature database to determine whether the sensor data matches at least one of a plurality of pre-event signatures stored in the pre-event signature database, wherein the pre-event signature is indicative of a prediction of a first event, and wherein the sensor data is determined to match the at least one of a plurality of pre-event signatures based on a similarity score of the sensor data and the at least one of a plurality of pre-event signatures satisfying a threshold, and wherein respective ones of the plurality of pre-event signatures comprise one or more parameters comprising at least an associated time period during which the first event is predicted to occur, wherein the associated time period comprises a time period after the pre-event signature has occurred;
determine an event masking action corresponding to the first event; and
cause performance, in response to the detection of the pre-event signature, of the event masking action for masking the first event before the associated time period has occurred, wherein the first event occurs during the event masking action, and wherein the event masking action reduces or masks an effect of the first event.

2. The apparatus as claimed in claim 1 , wherein the pre-event signature comprises one or more of: an audible signature, a visual signature, an environmental signature, or a haptic signature.

3. The apparatus as claimed in claim 1 , wherein the event masking action is one or more of: a visual action, an audible action, a haptic action, or an environmental action, and wherein the first event is one or more of: a visual event, an audible event, a haptic event or an environmental event.

4. The apparatus as claimed in claim 1 , wherein the first event comprises an audible event and the event masking action comprises an audio suppression action.

5. The apparatus as claimed in claim 1 , wherein the pre-event signature is determined using a first model comprising a machine learning model.

6. The apparatus as claimed in claim 5 , wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to:
train the first model, wherein training the first model comprises determining a correlation between one or more sensor data features and the first event.

7. The apparatus as claimed in claim 6 , wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to:
associate one or more sensor data features with the first event, responsive to at least one of a uniqueness or a repeatability of the one or more sensor data features in relation to the first event is higher than a first threshold, wherein the first model is trained using sensor data features associated with the first event.

8. The apparatus as claimed in claim 1 , wherein the first event comprises an environmental event, and wherein the environmental event comprises a change in temperature, a change in humidity, or a change in smell.

9. The apparatus as claimed in claim 1 , wherein the first event comprises a haptic event, and wherein the haptic event comprises a vibration or a movement of an object.

10. The apparatus as claimed in claim 1 , wherein the first event comprises a visual event, and wherein the visual event comprises a light turning on, a light turning off, or a movement of an object.

11. The apparatus as claimed in claim 1 , wherein the first event comprises a combination of two or more of: a visual event, an audible event, a haptic event, or an environmental event.

12. The apparatus as claimed in claim 1 , wherein the one or more parameters further comprise a start time and an end time associated with the first event, a frequency associated with the first event, a wavelength associated with the first event, or an amplitude associated with the first event.

13. The apparatus as claimed in claim 1 , wherein the event masking action comprises a same type as the first event, and wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to:
responsive to the first event comprising a visual event, cause performance of the event masking action comprising a visual action;
responsive to the first event comprising an audible event, cause performance of the event masking action comprising an audible action;
responsive to the first event comprising a haptic event, cause performance of the event masking action comprising a haptic action; and
responsive to the first event comprising an environmental event, cause performance of the event masking action comprising an environmental action.

14. The apparatus as claimed in claim 1 , wherein reducing the effect of the first event comprises cancelling the first event.

15. The apparatus as claimed in claim 1 , wherein the event masking action reduces a perception of the first event by one or more users.

16. A method comprising:
detecting a pre-event signature based on sensor data by querying a pre-event signature database to determine whether the sensor data matches at least one of a plurality of pre-event signatures stored in the pre-event signature database, wherein the pre-event signature is indicative of a prediction of a first event, and wherein the sensor data is determined to match the at least one of a plurality of pre-event signatures based on a similarity score of the sensor data and the at least one of a plurality of pre-event signatures satisfying a threshold, and wherein respective ones of the pre-event signatures comprise one or more parameters comprising at least an associated time period during which the first event is predicted to occur, wherein the associated time period comprises a time period after the pre-event signature has occurred;
determining an event masking action corresponding to the first event; and
causing performance, in response to the detection of the pre-event signature, of the event masking action for masking the first event before the associated time period has occurred, wherein the first event occurs during the event masking action, and wherein the event masking action reduces, masks, or cancels an effect of the first event.

17. The method as claimed in claim 16 , wherein:
the first event is one or more of: a visual event, an audible event, a haptic event or an environmental event; and
the event masking action is one or more of: a visual action, an audible action, a haptic action or an environmental action.

18. The method as claimed in claim 16 , wherein the pre-event signature is determined using a first model, and wherein the method further comprises:
training the first model based on at least a correlation between one or more sensor data features and the first event.

19. The method as claimed in claim 16 , wherein the first event comprises an audible event and the event masking action comprises an audio suppression action.

20. A non-transitory computer-readable medium having stored thereon computer-instructions for causing an apparatus to perform at least the following:
detecting a pre-event signature based on sensor data by querying a pre-event signature database to determine whether the

Assignees

• Nokia Technologies Oy

Inventors

• Wright Christopher
• Duffy David
• Cronie Harm

Classifications

• G06Q50/18

  Legal services · CPC title

• G06Q10/06311

  Scheduling, planning or task assignment for a person or group · CPC title

• G10K11/1752 Primary

  Masking · CPC title

• G06Q10/06 Primary

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title

• G06F3/165 Primary

  Management of the audio stream, e.g. setting of volume, audio stream path · CPC title