Persistent Memory Storage for Digital Assistant Response Accuracy
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Solution Overview
Problem
Digital assistants often fail to maintain long-term memory of user interactions, leading to decreased user experience due to repeated inquiries for information and inaccurate responses.
Innovation Solution
Implement a persistent storage configuration in digital assistants to indicate types of information to be stored, allowing for continuous recording and retrieval of user interactions, enhancing response accuracy and pertinence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital assistants do not implement persistent storage, then device complexity is low, but response accuracy and user experience deteriorate due to inability to remember key information
Solution Approach 1:
The storage configuration is segmented into multiple storage areas, each dedicated to storing specific types of information (e.g., user profile, interaction history, preferences). This segmentation allows the system to maintain response accuracy by retrieving relevant information from appropriate storage areas while managing complexity through organized categorization rather than a monolithic storage approach.
Solution Approach 2:
The system performs preliminary action by pre-configuring storage areas and establishing storage configurations before actual interactions occur. This allows the digital assistant to be ready to retrieve and utilize relevant information immediately during interactions, improving response accuracy without adding complexity during the interaction process itself.
2Measurement precision
If digital assistants implement persistent storage configuration, then response accuracy improves, but interaction time increases due to storage and retrieval operations
Solution Approach 1:
Different storage areas are created with local quality characteristics tailored to specific information types. Each storage area is optimized for its specific purpose (e.g., fast access for frequently used information, structured storage for complex data), allowing efficient retrieval operations that minimize time loss while maintaining high response accuracy.
Solution Approach 2:
The system implements feedback mechanisms where interaction outcomes and user responses are fed back into the storage configuration. This allows the system to learn from previous interactions and optimize future retrieval operations, reducing time loss over time while continuously improving response accuracy through adaptive storage management.
Data Source
AI summary
Embodiments of this specification describe technologies for interaction with digital assistant. One method comprises: determining whether the interaction information is associated with a first type of information indicated by a persistent storage configuration of the digital assistant; in response to an output of the machine learning model indicating that the interaction information is associated with the first type of information and the first type of information is not stored, storing the first type of information into a storage area corresponding to the first type; in response to the output of the machine learning model indicating that the interaction information is associated with the first type of information and the first type of information has been stored, reading the stored first type of information from the storage area corresponding to the first type; and determining, based on the stored or read first type of information, a response of the digital assistant.