Augmented vision examination techniques using machine learning

A system comprising:

• one or more computing devices;
• and
• one or more storage devices storing instructions that, when executed by the one or more computing devices, causes the one or more computing devices to perform operations comprising:
• obtaining, from a plurality of external data sources, context data, wherein the context data comprises (i) historical vision examination data for a patient received from one or more databases, (ii) a current state of a vision examination being administered for the patient;
• determining a candidate command for adjusting a hardware component of optometric equipment based on the current state of the vision examination;
• generating prompt data for one or more trained machine learning models based on the candidate command, wherein the prompt data specifies a natural language query of the candidate command for a proposed change to a configuration of the hardware component;
• providing the prompt data to a hosting system associated with the one or more trained machine learning models;
• obtaining, from the hosting system, model output data generated by the one or more trained machine learning models in response to providing the prompt data;
• parsing the model output data based on the candidate command to generate one or more command-specific executable instructions for the hardware component;
• and
• providing output data representing the one or more command-specific executable instructions for output, wherein the output data, when received by a client device, causes the client device to initiate a change to a configuration of the hardware component.

At least one non-transitory computer-readable storage device storing instructions that, when received by one or more processors, causes the one or more processors to perform operations comprising:

• obtaining, from a plurality of external data sources, context data, wherein the context data comprises (i) historical vision examination data for a patient received from one or more databases, (ii) a current state of a vision examination being administered for the patient;
• determining a candidate command for adjusting a hardware component of optometric equipment based on the current state of the vision examination;
• generating prompt data for one or more trained machine learning models based on the candidate command, wherein the prompt data specifies a natural language query of the candidate command for a proposed change to a configuration of the hardware component;
• providing the prompt data to a hosting system associated with the one or more trained machine learning models;
• obtaining, from the hosting system, model output data generated by the one or more trained machine learning models in response to providing the prompt data;
• parsing the model output data based on the candidate command to generate one or more command-specific executable instructions for the hardware component;
• and
• providing output data representing the one or more command-specific executable instructions for output, wherein the output data, when received by a client device, causes the client device to initiate a change to a configuration of the hardware component.