Edge Computing Remote Reporting System with ML Ranking
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Solution Overview
Problem
Financial institution clients and advisors face challenges in accessing reporting information remotely, particularly while away from their systems, and existing solutions lack real-time interaction and seamless data integration across different data structures.
Innovation Solution
A system utilizing smart glasses with edge computing and machine learning to enable remote, interactive reporting, where users can request reports via natural language, with machine learning models ranking and generating reports based on past access and enterprise-wide usage, and an integrated reporting platform converting requests into executable instructions across various data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote voice access is implemented using smart glasses, then accessibility to reporting information is improved, but latency and response time worsen
Solution Approach 1:
The system segments processing between edge devices (smart glasses, mobile devices) and centralized servers. Edge devices perform local processing for immediate response to user inputs, while centralized servers handle complex report generation tasks. This segmentation reduces latency by eliminating the need to transmit all processing requests to remote servers.
Solution Approach 2:
The system pre-generates and caches report data and visualizations before users request them. Machine learning models predict which reports users will need based on their behavior patterns, and these reports are prepared in advance on centralized servers. When users access reports remotely, the data is already processed and ready for immediate delivery, reducing perceived latency.
2Adaptability or versatility
If multiple proprietary reporting engines are integrated, then compatibility with various data structures is improved, but system complexity worsens
Solution Approach 1:
The system introduces a standardized JSON request format as an intermediary layer between users and multiple proprietary reporting engines. All user requests are converted to this standard format, and the system automatically translates them into engine-specific formats. This intermediary simplifies the architecture by providing a uniform interface that abstracts away the complexity of integrating multiple different reporting engines with varying data structures.
3Measurement precision
If machine learning models are used to generate customized reports, then report relevance and accuracy are improved, but processing time worsens
Solution Approach 1:
Machine learning models continuously analyze user behavior patterns, data access frequencies, and report preferences in the background to pre-generate personalized report configurations. When users access the system, these pre-computed recommendations are immediately presented, eliminating the need for real-time analysis during user interactions. This preliminary processing maintains high accuracy while minimizing perceived processing time.
Data Source
AI summary
Systems, methods, and apparatus are provided for intelligent, integrated, and interactive remote reporting. A remote natural language request for a report may be received from a user at an edge device. A first machine learning model may generate a list of existing reports based on past usage by the user. If no existing report satisfies the request, a second, enterprise-level machine learning model may map the request to relevant data sets and rank the mapped data sets along with additional related data sets based on enterprise-wide usage. An integrated reporting platform may receive selected data sets and report parameters as a JSON request, convert the request to compatible executable instructions, and generate the report. The integrated reporting platform may be a wrapper layer encompassing multiple proprietary reporting engines. Feedback from the integrated reporting platform may be applied to update the machine learning models.