Cognitive Computation Module for Multimodal Care, Driving, and Privacy
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Solution Overview
Problem
Existing healthcare systems face limitations in patient care capacity, driver safety, home and commercial security, and customer support due to the lack of systems that can effectively communicate, diagnose, and respond to human emotions and threats, while also posing privacy risks with continuous voice recording.
Innovation Solution
A computer-implemented system equipped with a cognitive computation module that can speak, listen, and see, utilizing AI and decision rules to provide diagnosis, treatment recommendations, driver assistance, and security alerts, while ensuring privacy compliance through specific command activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional automated driving systems are used, then basic driving automation is achieved, but the system fails to communicate with the driver and alert for potential threats
Solution Approach 1:
The system continuously monitors driver state through cameras and sensors, providing real-time feedback about driver alertness and emotional state. It also alerts the driver to potential threats and incoming accidents, creating a two-way communication loop that enhances both safety and system responsiveness
Solution Approach 2:
A cognitive computation module acts as an intermediary between the automated driving system and the human driver. This module processes visual, audio, and sensor data to generate contextual understanding and communicates with the driver through multiple modalities (visual alerts, audio warnings, haptic feedback), bridging the gap between machine automation and human awareness
2Adaptability or versatility
If continuous voice recording is used for customer support and security, then the system can effectively respond to commands and detect threats, but privacy risks increase
Solution Approach 1:
Instead of continuous recording, the system uses periodic activation triggered by specific wake words or commands. The voice recognition system remains in a low-power listening state and only fully activates when triggered, reducing privacy concerns while maintaining responsiveness to user needs
Solution Approach 2:
The system performs preliminary processing of audio signals to detect wake words or specific triggers before initiating full voice recognition and recording. This preliminary filtering action ensures that the system only processes and stores voice data when necessary, balancing responsiveness with privacy protection
3Reliability
If human security guards and call centers are used, then false positive alerts can be eliminated, but operational costs increase
Solution Approach 1:
The cognitive computation module autonomously analyzes video feeds, audio signals, and sensor data to distinguish between genuine threats and false positives. The system self-corrects by cross-referencing multiple data sources and eliminating false alerts without requiring constant human intervention, thereby reducing operational costs while maintaining high alert accuracy
Solution Approach 2:
The system implements feedback loops where alert outcomes are continuously analyzed to improve future detection accuracy. False positives are fed back into the system to refine detection algorithms, progressively improving reliability while maintaining automated operation to control costs
4Reliability
If multiple trained physicians are deployed to handle critical patients, then patient care quality improves, but healthcare system capacity is limited
Solution Approach 1:
The cognitive computation module serves multiple functions simultaneously: it monitors patient vital signs, analyzes medical data, provides diagnostic support, and alerts healthcare providers. This multi-functional system replaces the need for multiple specialized personnel, improving both care quality and system capacity by consolidating functions into a single intelligent platform
Data Source
AI summary
A computer implemented system to assist a user includes a cognitive computation module which can speak to, listen to and/or see. The system can assist in patient care delivery, driving, providing security, customer support and identity verification. It may be configured in a processor of the computing device which receives text data input after converting speech into text. The received text data is analysed by the module using an artificial intelligence and/or decision rules modules. The modules may create a response of text output which is converted to speech data using text to speech module and sent to the communication module to speak out. It identifies posture of human body, or the face of a human or a user using computer connected camera and also receives input from other sensors or devices connected to the computer and sends input to the cognitive communication module for analysis and response.