Autonomous Robotic Patient Care via Deep Reinforcement Learning

Solution Overview

Problem

The rising costs and inaccuracies in healthcare due to caregiver shortages and overextension, leading to potential medical errors and reduced quality of care.

Innovation Solution

Implementing autonomous learning systems, specifically using deep reinforcement learning and adaptive models, integrated with robotic devices to autonomously monitor, diagnose, and provide treatment to patients.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If more caregivers are hired to improve care quality, then healthcare quality improves, but healthcare costs increase

Engineering Contradiction:

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent implements autonomous robots that perform self-learning and self-evolution through deep reinforcement learning, enabling the system to improve care quality automatically without requiring additional human caregivers. The robotic system serves itself by continuously learning from patient data and optimizing treatment protocols, thereby maintaining high care quality while avoiding the cost increase associated with hiring more personnel.

Inventive Principle:

Principle #25Self-service

2Productivity

If existing caregivers work more hours to meet demand, then care coverage improves, but medical errors increase

Engineering Contradiction:

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces the mechanical system of human caregivers with autonomous robotic systems that do not suffer from fatigue or overextension. The robotic devices can maintain consistent performance levels regardless of operational duration, thereby providing continuous care coverage without the increasing error rates that occur when human caregivers work excessive hours.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If autonomous robotic systems are implemented to improve care quality, then medical errors are reduced, but system complexity increases

Engineering Contradiction:

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent designs autonomous robotic systems with multi-functional capabilities that can perform monitoring, diagnosis, and treatment delivery through integrated treatment algorithms. By consolidating multiple healthcare functions into a single autonomous platform, the system reduces medical errors across various care tasks while avoiding the proportional increase in complexity that would result from deploying separate specialized systems for each function.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12283378B2Providing healthcare via autonomous, self-learning, and self-evolutionary processes

Publication Date: 2025.04.22

AI summary

System, methods, and other embodiments described herein relate to autonomous assessment and treatment of a patient. In one embodiment, a method includes, responsive to acquiring, in a managing device, sensor data characterizing a condition of the patient, determining a diagnosis for the condition according to a correlation of the sensor data with a subset of markers. The method includes selecting, using a treatment model, a treatment algorithm from a set of treatment algorithms for performing therapeutic delivery using a robotic device. The method includes causing the robotic device to perform the therapeutic delivery according to the treatment algorithm selected by the treatment model.