17816843. AUTOMATIC IMPLEMENTATION OF A SETTING FOR A FEATURE OF A DEVICE USING MACHINE LEARNING simplified abstract (QUALCOMM Incorporated)

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AUTOMATIC IMPLEMENTATION OF A SETTING FOR A FEATURE OF A DEVICE USING MACHINE LEARNING

Organization Name

QUALCOMM Incorporated

Inventor(s)

Malathi Gottam of Guntur (IN)

Rajeshwar Kurapaty of Hyderabad (IN)

Vikash Garodia of Hyderabad (IN)

Uma Mehta of Hyderabad (IN)

Vishnu Priyanka Gujjula of Hyderabad (IN)

AUTOMATIC IMPLEMENTATION OF A SETTING FOR A FEATURE OF A DEVICE USING MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17816843 titled 'AUTOMATIC IMPLEMENTATION OF A SETTING FOR A FEATURE OF A DEVICE USING MACHINE LEARNING

Simplified Explanation

The patent application describes a device that can automatically adjust its settings based on sensor data and user-controlled changes. Here is a simplified explanation of the abstract:

  • A device detects a characteristic associated with itself using a sensor.
  • Based on this sensor data, the device automatically implements a first setting for a user-controllable feature.
  • If the user makes a change to the first setting, the device detects it.
  • The device then obtains second sensor data from the sensor.
  • Using a machine learning model, the device automatically implements a second setting for the feature based on the second sensor data.
  • The machine learning model is trained to identify settings based on information about the user-controlled change to the first setting.

Potential Applications:

  • Smart home devices that adjust settings based on user preferences and environmental conditions.
  • Wearable devices that adapt their functionality based on user activities and biometric data.
  • Autonomous vehicles that optimize driving settings based on road conditions and user preferences.

Problems Solved:

  • Eliminates the need for manual adjustment of device settings by automatically adapting to user preferences and changing conditions.
  • Provides a personalized user experience by considering both user-controlled changes and sensor data.
  • Reduces the cognitive load on users by automating the adjustment of device settings.

Benefits:

  • Improved user convenience and satisfaction by automatically adjusting device settings.
  • Enhanced device performance and efficiency by optimizing settings based on sensor data.
  • Enables a more personalized and adaptive user experience.
  • Reduces the need for constant user intervention and manual adjustments.


Original Abstract Submitted

In some aspects, a device may obtain first sensor data from a sensor configured to detect a characteristic associated with the device. The device may cause automatic implementation of a first setting for a feature of the device that is controllable by a user, the first setting based at least in part on the first sensor data. The device may detect a user-controlled change to the first setting for the feature. The device may obtain second sensor data from the sensor. The device may cause automatic implementation of a second setting, for the feature, that is identified by a machine learning model based at least in part on the second sensor data. The machine learning model may be trained to identify a setting for the feature based at least in part on information relating to the user-controlled change to the first setting. Numerous other aspects are described.

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