AI Background Analysis Workflow for Timely Signal Detection
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Solution Overview
Problem
Existing software applications lack continuous background data analysis capabilities using artificial intelligence, requiring manual intervention for signal hunting and repetitive data analysis, leading to inefficiencies and unnecessary resource consumption.
Innovation Solution
Implement a data analysis activity (DAA) that defines queries and instructions for an AI engine to run continuously in the background, processing data sets and notifying users only when significant signals are detected, allowing for customizable and flexible AI job configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis and signal hunting are performed by data scientists, then analysis quality and insight accuracy are improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system enables self-service automated data analysis through AI agents that autonomously execute analysis workflows without requiring data scientists to manually perform repetitive signal hunting tasks. The AI agents continuously monitor data sources, execute predefined analysis protocols, and generate insights automatically, allowing the system to serve itself rather than relying on human intervention for routine analysis operations.
Solution Approach 2:
Manual mechanical processes of data analysis are replaced with automated AI-based systems. The patent substitutes human data scientists performing manual code execution and signal hunting with AI agents that automatically execute analysis workflows, process data, and generate insights through computational mechanisms, thereby eliminating the time-consuming manual operations while maintaining or improving analysis quality.
2Adaptability or versatility
If repetitive data analysis is performed manually, then flexibility in analysis customization is improved, but productivity and resource utilization deteriorate
Solution Approach 1:
The analysis system transitions from static manual workflows to dynamic automated processes. AI agents can adaptively execute analysis workflows based on real-time data conditions, automatically adjust analysis parameters, and dynamically allocate computational resources. This dynamic automation maintains the flexibility to customize analysis for different scenarios while dramatically increasing productivity through continuous automated execution without human intervention.
Solution Approach 2:
The AI-based analysis platform provides universal functionality that can handle multiple types of data analysis tasks through a single automated system. Rather than requiring separate manual processes for different analysis scenarios, the system uses multi-functional AI agents that can execute various analysis workflows, process different data sources, and generate diverse insights through a unified automated framework, thereby improving both productivity and adaptability.
3Speed
If continuous background data analysis is implemented, then insight detection timeliness is improved, but computational resource consumption increases
Solution Approach 1:
Instead of continuous uninterrupted analysis, the system implements periodic execution of analysis workflows by AI agents. The AI agents execute analysis at optimized intervals based on data change rates and business requirements, allowing computational resources to be allocated in periodic bursts rather than constant operation. This approach maintains timely insight detection while reducing overall resource consumption compared to truly continuous analysis.
Solution Approach 2:
The system dynamically changes computational parameters based on data conditions and priorities. AI agents adjust analysis intensity, data sampling rates, and computational resource allocation according to real-time requirements, enabling the system to scale resource usage up or down as needed. This parameter optimization allows timely signal detection when necessary while conserving computational resources during periods of lower priority, resolving the contradiction between speed and resource consumption.
Data Source
AI summary
Using a data analysis activity (DAA) definition, a DAA associated with a software application is triggered. An instance selector query is executed to generate a set of instance values as input for a data query. A data query to generate a data set is executed using instance values of the set of instance values. Using the data set, an instruction for an artificial intelligence (AI) engine is computed. A result based on the instruction for an AI engine is received from the AI engine. The result based on the instruction for an AI engine is stored into an AI Result History Store. Prior results from earlier DAA executions is read from the AI Result History Store. A notification to a defined target audience is sent using the software application.