Personal Assistant State Machine for Resource-Constrained Devices
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Solution Overview
Problem
Resource-constrained mobile devices face challenges in processing power and memory, limiting their ability to interact effectively with personal assistant modules, as they often rely on cloud-based services that lack access to local data points, leading to ambiguous requests.
Innovation Solution
A method where a personal assistant module on a resource-constrained device receives a request, uploads data to an online semantic processor, generates a finite state machine based on local resources, and transitions to appropriate states using signals from the device, such as contact lists, interaction history, and media files, to provide accurate outputs without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If personal assistant module is implemented on cloud-based services, then processing power and memory capacity are improved, but access to local data points and signals is lost
Solution Approach 1:
The personal assistant module is segmented into two parts: a cloud-based semantic processor that handles complex processing tasks, and a local component that maintains access to device-specific data points and signals. This segmentation allows the system to leverage cloud computing power while preserving access to local information through the state machine implementation on the resource-constrained device.
Solution Approach 2:
A state machine acts as an intermediary between the cloud-based semantic processor and local device resources. The state machine receives the processed request from the cloud, analyzes local data points and signals, and determines the appropriate response by bridging the gap between cloud processing and local information access.
2Loss of information
If personal assistant module is implemented on resource-constrained device, then access to local data points is improved, but processing power and memory capacity are reduced
Solution Approach 1:
The system segments processing tasks between the resource-constrained device and cloud services. The device implements a state machine that handles local data access and simple processing, while complex semantic processing is offloaded to the cloud-based semantic processor, optimizing the use of limited local resources.
Solution Approach 2:
The state machine is generated as a copy or representation of the personal assistant's decision-making logic that can be executed locally on the resource-constrained device. This allows the device to perform autonomous operations based on local data without requiring continuous cloud connectivity or extensive local processing capabilities.
3Power
If cloud-based semantic processor is used, then processing capability is improved, but request ambiguity increases due to lack of local context
Solution Approach 1:
The state machine performs preliminary actions by analyzing local data points and signals before submitting requests to the cloud-based semantic processor. This preliminary analysis enriches the request with contextual information, allowing the cloud processor to generate more accurate responses without sacrificing its processing capability.
Solution Approach 2:
The system implements feedback loops where the state machine continuously monitors local signals and data points, compares them with cloud-generated responses, and adjusts its behavior accordingly. This feedback mechanism ensures that the personal assistant maintains high request accuracy by incorporating real-time local context into its interactions.
Data Source
AI summary
Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).