AI Predictive Output System for Data Search Efficiency
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Solution Overview
Problem
Users face challenges in efficiently searching and analyzing vast amounts of information in electronic networks and databases, leading to frustration and inability to determine relevant data for future actions, especially when dealing with contextually-relevant information from multiple data sources that fluctuate with seasonality and variability.
Innovation Solution
A system and method utilizing artificial intelligence and machine learning models, integrated with a semantic architecture, to analyze and provide predictive outputs and key drivers by filtering and generating contextually-relevant data, allowing users to access relevant information automatically and dynamically, without the need to switch between applications, through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users search for information manually in electronic networks and databases, then they can access data, but the process becomes inefficient and frustrating due to the vast amount of information
Solution Approach 1:
The system performs preliminary actions by automatically retrieving, filtering, and analyzing data from multiple sources before the user needs it. The AI process proactively identifies relevant information based on user profiles and queries, eliminating the need for users to manually search through vast databases.
Solution Approach 2:
The system provides self-service by automatically generating predictive outputs and key driver analyses without requiring users to manually query or filter data. The AI process autonomously retrieves relevant information from multiple sources and presents it in a formatted response, freeing users from manual information gathering tasks.
2Reliability
If users manually search for relevant information, then they can find data, but they cannot efficiently determine future actions due to lack of predictive analysis
Solution Approach 1:
The system incorporates feedback mechanisms where the AI process continuously analyzes user queries and data patterns to refine predictive outputs. The system learns from user interactions and adjusts its analysis to provide more accurate predictions about future actions, improving reliability over time.
Solution Approach 2:
The system changes parameters by transforming raw data into predictive outputs with different levels of granularity and temporal resolution. The AI process adjusts data parameters based on user needs, providing forecasts at appropriate timeframes and levels of detail to enable reliable future action determination.
3Loss of information
If the system provides detailed predictive data, then users get comprehensive information, but the complexity of data processing increases
Solution Approach 1:
The system segments the complex data processing task into distinct functional components: the AI process handles high-level analysis and predictive modeling, while the data retrieval process handles low-level data gathering from multiple sources. This segmentation allows each component to specialize, reducing overall system complexity while maintaining information completeness.
Solution Approach 2:
The AI process acts as an intermediary between the user's information needs and the raw data in multiple sources. It translates complex queries into data retrieval operations, filters and processes the raw data, and presents simplified predictive outputs, thereby reducing the complexity burden on the user while maintaining comprehensive information.
4Adaptability or versatility
If the system integrates multiple data sources, then users get comprehensive contextual information, but the variability and seasonality of data makes analysis difficult
Solution Approach 1:
The system applies dynamics by continuously adapting its data retrieval and analysis strategies based on the variability and seasonality patterns it detects in multiple data sources. The AI process dynamically adjusts its filtering and predictive modeling to account for changing patterns, maintaining measurement precision despite the inherent variability of data from diverse sources.
Data Source
AI summary
A method for providing predictive outputs and key drivers may include receiving a prompt from a user, providing the prompt to an artificial intelligence process, receiving, from the artificial intelligence process, an analysis of the prompt, the analysis including one or more of: an identified type of predictive data requested, one or more identified metrics related to the prompt, one or more identified data attributes related to the prompt, a determined granularity of data for a response, one or more filters applied to data related to the prompt, and a determined timeframe of analysis for a response; retrieving data related to the prompt, applying the one or more filters to the data, generating the identified type of predictive data according to the one or more metrics, the one or more data attributes, the granularity of data, and the timeframe of analysis, and presenting the generated predictive data to the user.