Dynamic control for data capture

A mobile electronic device comprising:

• a housing;
• a lens;
• a button;
• communication circuitry to wirelessly communicate;
• one or more sensors to output sensor data representative of environment lighting, image framing, and image clarity;
• a battery to power the mobile electronic device;
• memory;
• and
• processor logic circuitry to:
• recognize a person,
• utilize a machine learning model to determine a capture moment based on the environment lighting, the image framing, the image clarity, and an excitement level of the environment,
• trigger capture of an image in response to the determination of the capture moment,
• determine feedback data based on user interaction with the image, the user interaction to include at least one of an image save operation or an image delete operation, and
• train the machine learning model to determine a future capture moment based on the feedback data.

A mobile electronic device comprising:

• a housing;
• a lens;
• a button;
• communication circuitry to wirelessly communicate;
• one or more sensors to output sensor data representative of environment lighting, image framing, and image clarity;
• a battery to power the mobile electronic device;
• memory;
• and
• processor logic circuitry to:
• recognize a person,
• utilize a machine learning model to determine a capture moment based on the environment lighting, the image framing, the image clarity, and an expression of the recognized person,
• trigger retention of an image in response to the determination of the capture moment,
• determine feedback data based on user interaction with the image, the user interaction to include at least one of an image save operation or an image delete operation, and
• train the machine learning model to determine a future capture moment based on the feedback data.

A mobile electronic device comprising:

• a housing;
• a lens;
• a button;
• means for sending wireless communications;
• means for sensing, the sensing means to output data representative of environment lighting, image framing, and image clarity;
• means for powering the mobile electronic device;
• memory;
• and
• means for processing to:
• recognize a person,
• utilize a machine learning model to determine a capture moment based on the environment lighting, the image framing, the image clarity, and an excitement level of the environment,
• trigger capture of an image in response to the determination of the capture moment,
• determine feedback data based on user interaction with the image, the user interaction to include at least one of an image save operation or an image delete operation, and
• train the machine learning model to determine a future capture moment based on the feedback data.

A mobile electronic device comprising:

• a housing;
• a lens;
• a button;
• means for sending wireless communications;
• means for sensing, the sensing means to output data representative of environment lighting, image framing, and image clarity;
• means for powering the mobile electronic device;
• memory;
• and
• means for processing to:
• recognize a person,
• utilize a machine learning model to determine a capture moment based on the environment lighting, the image framing, the image clarity, and an expression of the recognized person,
• trigger retention of an image in response to the determination of the capture moment,
• determine feedback data based on user interaction with the image, the user interaction to include at least one of an image save operation or an image delete operation, and
• train the machine learning model to determine a future capture moment based on the feedback data.