LDAF Ambient Sensing for Respiration and User Presence Detection

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Solution Overview

Problem

There is a need to repurpose existing components in mobile computing devices to provide additional on-device sensing functionalities beyond basic health monitoring, such as sleep detection, respiration sensing, and user presence detection.

Innovation Solution

Incorporating a laser detect auto-focus (LDAF) sensor in mobile devices for ambient sensing, which uses laser pulses to measure distances and detect subtle range changes for applications like respiration rate detection and stress monitoring.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If existing components are repurposed for additional sensing functionalities, then device complexity is reduced, but measurement precision for new sensing applications may be compromised

Engineering Contradiction:
Improvedevice complexityVSAvoidmeasurement precision
Core Design Contradiction:
Device complexityVSMeasurement precision

Solution Approach 1:

The patent applies multi-functionality by enabling the LDAF sensor to perform both its original autofocus function and new ambient sensing functions (respiration detection, sleep monitoring, user presence detection) using the same hardware component. This allows the device to expand sensing capabilities without adding dedicated sensors for each function, thereby reducing overall device complexity while maintaining measurement precision through software-based signal processing tailored to each application.

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If LDAF sensor is used for ambient sensing applications, then sensing capabilities are enhanced, but the sensor's original autofocus function may be affected

Engineering Contradiction:
Improvesensing capabilitiesVSAvoidautofocus reliability
Core Design Contradiction:
Adaptability or versatilityVSReliability

Solution Approach 1:

The patent implements dynamics by dynamically allocating the LDAF sensor between autofocus and ambient sensing modes based on operational context. The system can switch between functions or operate both simultaneously with different priority levels, allowing the sensor to adapt its behavior to current needs. This dynamic approach ensures that autofocus reliability is maintained when needed while enabling enhanced sensing capabilities during appropriate conditions.

Inventive Principle:
Principle #15Dynamics

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The LDAF sensor enables accurate detection of respiration rates and user presence by measuring distance changes with millimeter precision, enhancing the device's sensing capabilities for health-related applications.

Implementation Method 1

a laser detect auto-focus (LDAF) sensor operable to perform ambient sensing of a user and/or an environment proximate to the user

Methodology Applied
Scientific EffectLaser: Laser

Implementation Method 2

uses laser pulses to measure distances and detect subtle range changes

Methodology Applied
Scientific EffectLight detection and ranging (LIDAR): LIDAR

Data Source

PatentUS20260060562A1Laser-Based Ambient Sensing for Mobile Computing Devices
Publication Date: 2026.03.05 GOOGLE LLC
  • US20260060562A1 patent drawing
  • US20260060562A1 patent drawing
  • US20260060562A1 patent drawing

AI summary

Computing systems, computing devices, and computer-implemented methods are provided. In one aspect, a mobile computing device includes a laser-based sensor such as a laser detect auto-focus sensor of an image capture assembly having an image capture device. The mobile computing device further includes one or more computing devices configured to perform one or more operations. For instance, the operations may include generating, with one or more laser-based sensors of a mobile computing device over a period of time, sensor data indicative of a distance between the mobile computing device and at least one surface, and generating, with one or more machine-learned models based on the sensor data, ambient sensing data including at least one biometric associated with a user of the mobile computing device.