Server-Based AI Agent for IoT Tracking Data Analysis
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Solution Overview
Problem
Existing tracking systems rely on proprietary applications and inflexible interfaces, limiting user access and value from tracking data, which is often consumed in a limited format such as smart notifications and graphical user interfaces.
Innovation Solution
A system for auto-tracking using just-in-time training and goal-seeking AI agents, comprising asset trackers, communications hubs, a base station, and a server computing device that processes raw data from asset trackers to answer user queries efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary applications and inflexible interfaces are used to access tracking data, then user access is limited and interface flexibility is reduced, but data processing capability is maintained
Solution Approach 1:
The patent extracts the data processing and analysis functionality from proprietary applications and relocates it to the server computing device. The server now performs analytical computations directly on raw tracking data, eliminating the need for complex client applications while maintaining data processing capability.
Solution Approach 2:
The server computing device is designed to perform multiple functions: storing raw data, performing analytical computations, generating insights, and providing responses to various user queries. This multi-functional approach replaces the need for multiple proprietary applications, increasing interface flexibility while reducing system complexity.
2Productivity
If device ML model-derived inference is used for data processing, then processing speed is improved, but computational resources on devices are consumed
Solution Approach 1:
The patent introduces the server computing device as an intermediary between raw data collection and user inquiry resolution. The server performs all analytical computations and data processing tasks, acting as a mediator that relieves computational burden from asset trackers and communication hubs while maintaining fast processing through centralized server resources.
3Ease of operation
If tracking data is consumed in limited formats such as smart notifications and graphical user interfaces, then data presentation is simplified, but user value and information utility are reduced
Solution Approach 1:
The system dynamically adapts data presentation formats based on user needs and query types. Rather than providing static graphical interfaces, the server can generate and deliver various types of responses including text, data structures, and customized insights, making the information system dynamic and adaptable to different user requirements while preserving full data utility.
Data Source
AI summary
In one aspect, a system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device computed data, storing the raw data of the one or more asset trackers in the server computing device without analytical computation; receiving a user query; initiating a just-in-time process to find an answer to the user query; communicating the question to an AI agent in the server computing device; with the AI agent: seeking a goal of answering the question, by breaking the question down into multiple steps, and executing the multiple steps until the goal is reached.