AI Server Component Identification via Camera Input
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Solution Overview
Problem
Conventional hard copy and website-based manuals for server setup and troubleshooting are costly, prone to errors, and time-consuming due to the need for physical printing and manual searching, which can lead to configuration errors and diminished user experiences.
Innovation Solution
A dynamic digital information retrieval system using machine learning object detection models, such as Faster R-CNN, to identify server components from camera-derived inputs, providing real-time information retrieval without the need for physical documents, enabling efficient and accurate component look-up.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hard copy manuals are provided, then users can access server setup and troubleshooting information, but printing and shipping costs increase and printing errors occur
Solution Approach 1:
The patent replaces physical hard copy manuals with digital copies accessible through a support website. Users can access manual content online instead of receiving physical copies, eliminating printing and shipping costs while maintaining information accuracy through digital distribution.
Solution Approach 2:
The patent substitutes the mechanical distribution system (printing and shipping physical manuals) with a digital system (online access through support website). This replacement eliminates the need for physical production and distribution while providing the same information access functionality.
2Loss of substance
If support website-based manuals are used, then printing and shipping costs are eliminated, but locating and navigating the manual becomes time-consuming
Solution Approach 1:
The patent implements an automated system where the server device itself provides information about its components. The system automatically identifies server components and retrieves relevant manual information without requiring users to manually search or navigate through documentation, making the system serve itself rather than requiring active user search.
Solution Approach 2:
The patent pre-processes and organizes manual information in a structured format that can be automatically retrieved and displayed. The system prepares information in advance so that when a user needs assistance, the relevant content is already organized and ready for immediate display, eliminating the need for time-consuming manual search.
3Loss of information
If users manually locate server components and match them to manual portions, then information can be found, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual visual identification and matching processes with automated image recognition technology. The system captures images of server components and automatically identifies them through image processing, eliminating the need for users to manually locate and match components to manual portions.
Solution Approach 2:
The patent introduces an image recognition system as an intermediary between the physical server components and the manual information. This intermediary automatically translates visual component information into identifiable data, which then retrieves the appropriate manual information, bridging the gap between physical hardware and documentation without requiring manual matching.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for dynamic digital information retrieval are provided herein. An example computer-implemented method includes training a machine learning object detection model using server component images and one or more features of the server component images; determining a type of server device captured by at least one camera-derived input, wherein determining the type of server device comprises analyzing the at least one camera-derived input using the machine learning object detection model; identifying one or more server components captured by the at least one camera-derived input by analyzing, within a context of the determined type of server device, the at least one camera-derived input using the machine learning object detection model; and outputting, to at least one display, information pertaining to the identified server components, wherein the information is retrieved from a data source related to the determined type of server device.