Voice Assistant Profile Stacking for Secure Public-Space Requests
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Solution Overview
Problem
Computing devices in public spaces face challenges in accurately processing remote procedure calls and network transmissions due to ambiguities in entity policies, leading to erroneous operations and resource wastage.
Innovation Solution
A data processing system performs speaker recognition to identify an electronic account, pushes a profile to a stacked data structure, and disassembles it upon a trigger event, ensuring secure and efficient processing of voice-based instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speaker recognition and profile stacking are implemented to improve security and accuracy in public computing devices, then entity identification accuracy and security are improved, but device complexity and processing overhead increase
Solution Approach 1:
The profile data is segmented into multiple layers within a stacked data structure, with each layer representing a different aspect or level of entity information. This segmentation allows the system to process and access specific profile components efficiently while maintaining overall security and accuracy requirements.
Solution Approach 2:
The patent introduces a dimensional aspect to profile storage by implementing a stacked data structure with multiple layers. This dimensional organization transforms flat profile data into a hierarchical structure, enabling more efficient access patterns and security controls without increasing overall complexity.
2Reliability
If stacked profile data structures are maintained continuously to ensure accurate entity identification, then security and processing accuracy are improved, but memory usage and processing resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring profile data into a stacked format before it is needed for entity identification. This preliminary organization enables faster access and more efficient memory utilization during actual processing operations.
Solution Approach 2:
The stacked data structure enables the system to discard less relevant or lower-priority profile layers when they are not needed, while maintaining quick access to critical layers. This selective retention and discarding optimizes memory usage while preserving processing accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces resource consumption, processor utilization, and bandwidth by parsing voice-based instructions, selecting appropriate profiles, and disassembling the stack when necessary, enhancing security and efficiency in public settings.
Implementation Method 1
a sensor configured to detect the input audio signal
Data Source
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AI summary
Processing stacked data structures is provided. A system receives an input audio signal detected by a sensor of a local computing device, identifies an acoustic signature, and identifies an account corresponding to the signature. The system establishes a session and a profile stack data structure including a first profile layer having policies configured by a third-party device. The system pushes, to the profile stack data structure, a second profile layer retrieved from the account. The system parses the input audio signal to identify a request and a trigger keyword. The system generates, based on the trigger keyword and the second profile layer, a first action data structure compatible with the first profile layer. The system provides the first action data structure for execution. The system disassembles the profile stack data structure to remove the first profile layer or the second profile layer from the profile stack data structure.