Insight Generation System for Data Repository Analysis
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Solution Overview
Problem
Existing data repository systems struggle to effectively analyze and display insights to users, as they often limit the type and quantity of analyses presented, making relevant interpretations and analyses not apparent, and their relevance varies by user and context.
Innovation Solution
A system and method for generating and displaying insights using repository data, which involves receiving user requests, creating analyses based on attributes, selecting insights, generating recommended actions, and recursively performing further analyses and insights based on user instructions, utilizing machine learning models to choose attributes and prioritize actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple analyses and interpretations are generated from repository data, then the quantity and variety of information available to users increases, but the complexity of the system and difficulty of presenting relevant information increases
Solution Approach 1:
The system segments the large set of generated analyses and interpretations by organizing them into hierarchical categories and groups based on relevance, type, and user context. This segmentation allows the system to manage complex information by breaking it down into manageable, organized portions that can be selectively presented to users without overwhelming system complexity.
Solution Approach 2:
The system dynamically changes parameters such as the number of analyses displayed, the depth of interpretation, and the level of detail based on user preferences, context, and system state. By adjusting these parameters, the system can adapt the quantity and complexity of presented information to match user needs, resolving the contradiction between providing comprehensive analyses and maintaining manageable system complexity.
2Reliability
If comprehensive data analyses are generated, then the relevance and value of information to users improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-organizing repository data into structured formats, pre-generating potential analyses and interpretations, and pre-establishing relevance criteria based on user profiles and context. This preliminary preparation reduces the time required for real-time analysis while maintaining high relevance, as the system only needs to select and present from pre-computed options rather than generating everything from scratch.
Solution Approach 2:
The system applies partial action by generating and presenting only the most relevant subset of analyses and interpretations rather than all possible analyses. By using relevance scoring, filtering, and selective presentation, the system provides sufficient information value to users without incurring the computational cost and time expenditure of generating every possible analysis, thus resolving the contradiction between comprehensiveness and efficiency.
3Adaptability or versatility
If the system adapts analyses to individual user needs and contexts, then the relevance of information to each user improves, but the complexity of personalization and customization increases
Solution Approach 1:
The system implements universality by creating a standardized framework for personalization that can serve multiple users with different needs and contexts. Rather than building separate customization systems for each user, the system uses a universal set of parameters, filters, and adaptation rules that can be configured once and applied across all users, reducing the overall complexity while maintaining high adaptability to individual needs.
Solution Approach 2:
The system uses feedback mechanisms to adapt to user needs by monitoring user interactions, preferences, and context information, then adjusting the generation and presentation of analyses accordingly. This feedback-driven adaptation allows the system to become increasingly personalized over time without requiring complex manual configuration, as the system automatically learns and adjusts based on user responses, resolving the contradiction between adaptability and complexity.
Data Source
AI summary
Disclosed herein are systems and methods for intelligent generation and display of insights using information in a data repository. For example, disclosed herein are methods for generating and displaying insights using initial data from a data repository, and intelligently/automatically proposing, generating, and displaying further insights using previously-generated insights.