Autonomous LLM Execution for Bio-Activity Discovery Pipelines
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Solution Overview
Problem
Existing systems for biological discovery pipelines are inefficient, rigid, and require user-defined configurations, leading to slow and inaccurate execution of complex workflows.
Innovation Solution
An AI tech-bio system utilizing a language machine learning model as an autonomous reasoner to navigate and execute multiple layers of a bio-activity discovery pipeline, autonomously interacting with tech-bio exploration tools to generate and retrieve bio-activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems are used to execute biological discovery pipeline workflows, then user configurations can be applied, but the execution is slow and inefficient
Solution Approach 1:
The language machine learning model autonomously executes the bio-activity discovery pipeline workflows without requiring user configurations or manual intervention. The model self-services by understanding natural language queries, automatically selecting appropriate tools and parameters, executing workflows, and interpreting results, thereby eliminating the time-consuming manual setup and configuration steps in existing systems
Solution Approach 2:
The patent replaces the manual mechanical process of user configuration and workflow setup with an intelligent automated system. The language machine learning model substitutes the mechanical interaction between users and system interfaces with natural language processing, automatically translating user intent into executed workflows without manual parameter setting or tool selection
2Adaptability or versatility
If existing systems are used for biological discovery pipelines, then basic functionality is provided, but the systems are rigid and limited by user configurations
Solution Approach 1:
The system transitions from static, pre-configured workflows to dynamic, adaptive workflows driven by natural language queries. The language machine learning model dynamically adjusts workflow parameters, tool selection, and execution steps based on the specific query context, enabling the system to adapt to diverse biological discovery needs without requiring users to manually reconfigure settings for each task
Solution Approach 2:
The language machine learning model serves as a universal interface that handles multiple types of bio-activity discovery tasks through a single natural language processing mechanism. Instead of requiring separate configurations for different workflow types, the model understands various query formats and automatically routes them to appropriate tools and parameters, making the system versatile across different biological discovery scenarios
3Reliability
If complex biological discovery pipeline workflows are executed in existing systems, then comprehensive analysis is possible, but the execution becomes inefficient and error-prone
Solution Approach 1:
The language machine learning model acts as an intermediary between user queries and the complex bio-activity discovery pipeline tools. It translates natural language intent into precise tool calls and parameter settings, mediating the complexity of workflow execution while maintaining reliability. The model ensures accurate tool selection and parameter passing, reducing errors that typically arise from manual configuration of complex workflows
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing language machine learning model (LLM) as autonomous reasoners to navigate and execute multiple layers of a computerized bio-activity discovery pipeline of a tech-bio exploration system. In particular, the disclosed systems can utilize an LLM that learns to access one or more tech-bio exploration tools to execute one or more processes and/or tasks in a bio-activity discovery pipeline. For instance, the disclosed systems can provide an interactive query prompt interface to enable users to provide tech-bio queries (as prompts) and utilize the LLM with the prompts to execute one or more tasks in the bio-activity discovery pipeline to generate and/or retrieve bio-activity data for the query. Moreover, the disclosed systems can utilize one or more LLMs to autonomously utilize and/or interact with one or more tech-bio tools in the bio-activity discovery pipeline to generate and/or obtain bio-activity data.