Optical Code Access to Asset-Specific LLM Agents in Logistics
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Solution Overview
Problem
Existing logistics management systems face challenges in efficiently accessing and integrating with various data stores due to platform-specific user interfaces, and configuring large language models (LLMs) to imitate large numbers of assets is computationally infeasible and cumbersome.
Innovation Solution
The use of optical codes to quickly and accurately lookup custom response instructions for LLMs, enabling them to imitate a vast number of assets by leveraging prompt engineering and integrating with a data store of LLM custom responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single pre-trained LLM is used to selectively imitate extremely large numbers of different assets, then the system can provide natural language access to asset information, but it becomes computationally and algorithmically infeasible due to token limits and other technical limitations
Solution Approach 1:
The patent segments the single LLM system into multiple specialized LLM agents, each trained to imitate a specific asset type or category. This division allows each agent to operate within token limits while collectively covering extremely large numbers of assets. The segmentation resolves the contradiction by distributing the computational burden across multiple smaller, manageable agents rather than requiring one oversized LLM.
Solution Approach 2:
The patent introduces an intermediary component that routes natural language queries to the appropriate LLM agent based on the asset type. This intermediary layer manages the complexity of coordinating multiple agents, allowing the system to maintain ease of operation for users while handling the computational complexity internally through intelligent query routing and agent coordination.
2Reliability
If manually training an LLM to selectively imitate extremely large numbers of different assets is performed, then the system can achieve accurate asset imitation, but it becomes computationally expensive, cumbersome, and slow
Solution Approach 1:
The patent applies preliminary action by pre-training specialized LLM agents during an offline phase, each trained on specific asset types. This preliminary training allows the agents to be ready for deployment without requiring manual training at runtime. When queries arrive, the pre-trained agents can immediately provide accurate responses, eliminating the need for time-consuming manual training during operation.
Solution Approach 2:
The patent implements dynamics by allowing the system to dynamically select and deploy appropriate pre-trained agents based on query requirements. The system can adaptively route queries to the most suitable agent without requiring retraining, maintaining both accuracy and speed. This dynamic agent selection resolves the contradiction by keeping training activities separate from operational time constraints.
3Loss of information
If platform-specific user interfaces are used to access information in data stores, then the system can provide comprehensive access to asset information, but it presents a steep learning curve and is difficult and cumbersome for users
Solution Approach 1:
The patent implements universality by creating a single natural language interface that can access and manipulate asset information across all asset types and data stores. This universal interface replaces multiple platform-specific interfaces, allowing users to interact with diverse asset information through a consistent, intuitive natural language paradigm rather than learning multiple specialized interfaces.
Solution Approach 2:
The patent substitutes the mechanical interaction with traditional graphical user interfaces with a more natural, language-based interaction model. By replacing the mechanical clicking, navigating, and form-filling of platform-specific interfaces with natural language processing, the system maintains comprehensive information access while dramatically improving ease of operation through more intuitive human-computer interaction.
Data Source
AI summary
A method for automatically configuring a machine learning model and providing access to the configured machine learning model is performed by a system comprising one or more processors. The method includes: receiving a transmission from a mobile device that includes decoded information based on an optical code associated with an asset, the decoded information including a lookup portion; retrieving custom response instructions associated with the asset from a storage location determined based on the lookup portion of the decoded information; transmitting the custom response instructions to a machine learning model; and transmitting instructions to the mobile device to display an interface at a display of the mobile device, the interface configured to enable a user of the mobile device to interact with the machine learning model subject to the custom response instructions.