How Software Recommends Dashboards Based on What You Click

This patent describes a system that watches how you interact with data dashboards and uses AI to suggest other dashboards you might find useful, making it easier to find the information you need.

What it covers

The patent describes a system that shows a user a 'dashboard interface' on a device. It then 'tracks user inputs' like clicks or searches, both while the dashboard is open and afterwards. Using 'predictive analytics,' which is a type of AI, the system tries to guess what kind of data the user is looking for. It then finds other dashboards that have similar data and 'outputs a recommendation' to the user. For example, if a user frequently clicks on sales figures for a specific region, the system might recommend a dashboard showing detailed marketing spend for that same region.

What it doesn't cover

• —Does not cover systems that recommend individual data points or reports, only entire dashboard interfaces or portions thereof.
• —Does not cover generating new dashboards from scratch, only recommending existing ones.
• —Does not cover recommendations based solely on explicit user queries or preferences, but rather 'tracked user input patterns'.
• —Does not cover systems that do not use predictive analytics to determine the user's predicted data type.
• —Does not cover recommending non-dashboard content, such as articles or products.

The clever bit

The novelty lies in combining the tracking of a user's interaction patterns with predictive analytics to infer their current data interest, and then using that inference to recommend entirely different, but related, dashboard interfaces. This moves beyond simple keyword matching to anticipate user needs based on behavior.

Why it matters

In today's data-rich environments, users often struggle to find the specific information they need within vast collections of dashboards. This patent aims to make data discovery more efficient by intelligently suggesting relevant dashboards. It helps users navigate complex business intelligence systems, reducing the time spent searching and potentially improving the speed and quality of data-driven decisions.

Real-world examples

• 1.Microsoft Power BI's suggested content
• 2.Tableau's recommendation engine for related dashboards
• 3.Google Looker's smart suggestions
• 4.Custom business intelligence platforms with AI-driven insights
• 5.CRM dashboards with contextual recommendations