Robotic Task Planning With AI Adapters for Faster Medical Setup

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional computer-assisted medical systems struggle with inefficient task determination and require significant manual effort and long setup times for adapting to new capabilities, making them impractical for dynamic medical procedures.

Innovation Solution

A computer-assisted system with a control system that utilizes machine learning models to analyze data streams, filter tasks, and adapt to user inputs, allowing dynamic task generation and selection without extensive retraining, using a task generation model, task selection model, and robotics transformer model to control repositionable structures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional systems use fine-tuned base models to support new capabilities and tasks, then task performance improves, but system setup time and training requirements increase significantly

Engineering Contradiction:

Improvetask performanceVSAvoidsystem setup time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system segments the monolithic fine-tuned model into a frozen pre-trained base model combined with separate trainable adapter modules. This allows the bulk of the model to remain fixed while only small adapter components require training, dramatically reducing setup time while preserving task performance capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the training parameters by freezing most model weights and only training small adapter modules with fewer parameters. This parameter change enables rapid adaptation to new tasks without requiring extensive retraining of the entire model, thus reducing setup time while maintaining performance.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If systems become more complex with additional capabilities, then versatility improves, but user input requirements and setup complexity increase

Engineering Contradiction:

Improvesystem capabilitiesVSAvoidsystem setup complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements a universal frozen pre-trained base model that can serve multiple different tasks and capabilities. By combining this universal base with task-specific adapters, the system achieves versatility across multiple medical procedures and operations without proportionally increasing overall system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system uses copying by replicating small adapter modules for different tasks while sharing the common frozen base model. This allows the system to support multiple capabilities through lightweight copies rather than requiring separate complex systems for each function, reducing overall setup complexity.

Inventive Principle:

Principle #26Copying

3Ease of operation

If pre-engineered prompts are used with frozen models, then manual effort is reduced, but task execution performance deteriorates

Engineering Contradiction:

Improvemanual effortVSAvoidtask execution performance

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system introduces trainable adapter modules as intermediaries between the frozen pre-trained base model and the task-specific requirements. These adapters act as mediators that translate the general capabilities of the base model into effective task execution, achieving both reduced manual effort and maintained performance.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary training of adapter modules before deployment, so that when the system operates, the adapters are already optimized for their specific tasks. This preliminary action ensures high task execution performance while keeping the operational system easy to use with minimal manual intervention.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260008177A1Multi-task ai system for dynamic and intelligent robotic task planning based on user input

Publication Date: 2026.01.08 INTUITIVE SURGICAL OPERATIONS INC

AI summary

Systems and methods are described for determining and performing tasks for medical procedures using adaptive artificial intelligence. The system may include one or more repositionable structures configured to support respective instruments, and a control system operably coupled to the repositionable structure, the control system configured to (i) receive a plurality of data streams from one or more data sources, (ii) analyze, according to a task generation machine learning model constitution, the data streams to identify a plurality of tasks to be performed by the one or more repositionable structures; (iii) filter, according to a task selection machine learning model, the identified tasks; (iv) detect a user input and modify, based on the user-input, a data streams, a machine learning model, or operation of a repositionable structure; (v) select a task to be performed; and (vi) control the repositionable structures to perform the selected task.