Neurological Intent Detection for Automatic Application State Transition
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Solution Overview
Problem
Users of computer applications with multiple states often face inefficiencies and errors due to the need to manually switch between states for different operations, which prolongs task completion time and increases user frustration.
Innovation Solution
A computer system that utilizes neurological user intention data to automatically change application states, allowing intended operations to be executed without manual input, by detecting and mapping neurological signals to corresponding application states and operations through a state machine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual state switching is required to perform different operations, then application state control is precise and reliable, but user operation time increases and productivity decreases
Solution Approach 1:
The system automatically detects neurological user intention data and changes application states without requiring manual user input. The computer system serves itself by interpreting neurological signals and autonomously transitioning between application states, eliminating the time-consuming manual state switching process while maintaining precise state control.
Solution Approach 2:
The patent replaces manual mechanical input methods (keyboard, mouse) with neurological detection systems. By using EEG or other neurological sensing technologies, the system directly captures user intent from brain signals, substituting the traditional mechanical interaction chain with a more direct neurological-to-digital signal conversion process.
2Ease of operation
If manual menu selection is used to change application states, then state transition accuracy is maintained, but user frustration increases and ease of operation deteriorates
Solution Approach 1:
The system continuously monitors neurological user intention data and provides real-time feedback by automatically adjusting application states to match detected user intent. This closed-loop feedback mechanism ensures that the application state accurately reflects what the user intends to do, maintaining reliability while dramatically improving ease of operation.
Solution Approach 2:
The system detects neurological intentions before the user actually performs manual actions. By anticipating user intent through neurological signal detection and pre-positioning the application in the desired state, the system ensures accurate state execution is already in place before the user completes their intended operation.
3Productivity
If neurological detection is implemented to enable automatic state changes, then productivity increases and time loss is reduced, but device complexity increases
Solution Approach 1:
The neurological detection system is designed to serve multiple functions: detecting user intent for state changes, monitoring user attention levels, and potentially controlling other application parameters. This multi-functionality justifies the added complexity by providing comprehensive control capabilities beyond simple state switching.
Solution Approach 2:
The patent introduces a neurological detection intermediary layer that translates complex brain signals into simple application control commands. This intermediary processing layer manages the complexity by breaking down neurological signal interpretation into manageable steps, converting raw neurological data into meaningful state transition triggers.
Data Source
AI summary
Computer systems, methods, and storage media for changing the state of an application by detecting neurological user intent data associated with a particular operation of a particular application state, and changing the application state so as to enable execution of the particular operation as intended by the user. The application state is automatically changed to align with the intended operation, as determined by received neurological user intent data, so that the intended operation is performed. Some embodiments relate to a computer system creating or updating a state machine, through a training process, to change the state of an application according to detected neurological data.