Building Context Graphs for Adaptive User Data Presentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Building management systems lack dynamic user interfaces that adapt to user preferences and contextual information, presenting only pre-designed data without the ability to change based on user interests or building context.
Innovation Solution
A building management system that uses a graph database to receive unstructured user questions, extract context, and retrieve data to compose presentations dynamically, incorporating data ingestion and dynamic user experience services to provide visual, textual, or audible outputs based on user input and building data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static, predefined user interface application is used to present building data, then the system structure is simple and easy to implement, but the interface cannot adapt to user preferences or building context dynamically
Solution Approach 1:
The patent implements a dynamic user interface that automatically adapts to user preferences and building context by using machine learning models to generate personalized presentations of building data, transforming the static interface into a dynamic system that evolves based on user interactions and contextual information
Solution Approach 2:
The system employs automated content generation and curation mechanisms that allow the interface to self-adjust and self-optimize based on user behavior patterns, eliminating the need for manual configuration while maintaining high adaptability to individual user needs and building contexts
2Adaptability or versatility
If a developer-designed user interface presents only predefined information, then the implementation is straightforward, but the interface cannot change based on user interest or building context
Solution Approach 1:
The system pre-processes and structures building data using knowledge graphs and ontologies before presentation, organizing information in advance according to multiple potential user perspectives and building contexts, enabling rapid adaptation without losing contextual relationships during runtime
Solution Approach 2:
The patent introduces a machine learning-based content generation intermediary that acts as a mediator between the raw building data and the user interface, translating structured data into personalized presentations while preserving contextual information through learned relationships and patterns
3Adaptability or versatility
If a static data analytics system is deployed, then the system is stable and reliable, but it cannot dynamically meet user needs or preferences
Solution Approach 1:
The system implements continuous feedback loops where user interactions with the interface are monitored and fed back to the machine learning models, which automatically adjust the data presentation and analytics based on observed user preferences and behavior patterns, enabling dynamic adaptation while maintaining system stability
Solution Approach 2:
The automated content generation system performs self-optimization by continuously learning from user interactions and building context data, automatically adjusting analytics parameters and presentation formats without requiring manual reconfiguration, thereby achieving high adaptability through automation
Data Source
AI summary
A building system includes one or more storage devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive an unstructured user question from a user device of a user and query a graph database based on the unstructured user question to extract context associated with the unstructured user question from contextual information of a building stored by the graph database, wherein the graph database stores the contextual information of the building through nodes and edges between the nodes, wherein the nodes represent equipment, spaces, people, and events associated building and the edges represent relationships between the equipment, spaces, people, and events. The instructions further cause the one or more processors to retrieve data from one or more data sources based on the context and compose a presentation based on the retrieved data.