Self-learning Digital Assistant for Unknown Commands
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Solution Overview
Problem
Smart computing devices often fail to respond to user commands as they are not programmed to perform unknown tasks, leading to frustration for users.
Innovation Solution
Implementing a learning mechanism in digital assistants that monitors user activity after an unrecognized command, using sensors and software to determine response patterns and create a response profile to execute the task associated with the command.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a digital assistant uses a fixed set of known commands and responses, then the device complexity is reduced and ease of operation is improved, but the adaptability to new or unknown commands deteriorates
Solution Approach 1:
The digital assistant performs self-learning by automatically monitoring user activities and inferring response patterns without requiring manual programming. The system serves itself by acquiring new knowledge through observation of user behavior, enabling it to adapt to unknown commands while maintaining ease of operation.
Solution Approach 2:
The system implements a feedback mechanism where user activities are monitored and fed back to the digital assistant after an unrecognized command is detected. This feedback loop enables the system to learn from actual user behavior and improve its response capabilities iteratively.
2Adaptability or versatility
If the digital assistant enters a failure state and monitors user activity to learn response patterns, then the adaptability to unknown commands is improved, but the loss of time increases due to monitoring and learning processes
Solution Approach 1:
The system performs preliminary learning by monitoring user activities in advance and building a knowledge base of response patterns before they are needed. This preliminary action stores learned information for future use, reducing the time loss when handling unknown commands as the system already has pre-acquired knowledge to draw upon.
3Measurement precision
If the digital assistant monitors user activity on multiple computing devices, then the measurement precision of response patterns is improved, but the device complexity increases
Solution Approach 1:
The digital assistant is designed with multi-functionality to monitor and process user activities across multiple types of computing devices (smartphones, tablets, computers, smart home devices). This universal capability allows the system to gather comprehensive data from diverse sources, improving measurement precision of response patterns while managing complexity through a unified monitoring framework.
Data Source
AI summary
A response activity pattern may be ascertained from a set of computing devices obtained during a monitoring mode of operation. The monitoring mode of operation can be initiated when a user command is determined to be a new or unknown command. The response activity pattern may be used to generate a response profile indicating an activity to carry out using one or more user devices to perform a task associated with the user command. When an indication of a previously unknown user command to perform a task is received, the generated response profile can be used to perform the designed task by carrying out the activity using the one or more user device. In this way, the activity that should be carried out to perform the task can be learned based on the monitored user activity related to the one or more user devices.