Automated Template Layout Recommendation System
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Solution Overview
Problem
Current template design for customer communications management (CCM) solutions is largely subjective and manual, leading to ineffective and time-consuming layout creation.
Innovation Solution
A system that recommends layouts for template elements based on data analysis of past template effectiveness, using a machine learning model to suggest optimal positions, sizes, fonts, and colors by analyzing user interactions and document events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual template design is used with designer judgment, then design flexibility is maintained, but template design time increases and effectiveness decreases
Solution Approach 1:
The system performs preliminary analysis of document events and layout information from historical templates before the user creates a new template. By pre-processing and storing this data in datasets, the system can quickly generate layout recommendations without requiring time-consuming manual analysis during the template creation process.
Solution Approach 2:
The system implements feedback by analyzing document events (user interactions with generated documents) and using this information to improve future layout recommendations. The system continuously learns from the effectiveness of past templates and adjusts its recommendations accordingly, creating a closed-loop system that improves template effectiveness over time.
2Productivity
If automated layout recommendation is implemented, then template design time is reduced, but system complexity increases
Solution Approach 1:
The system segments the template design process into distinct components: layout analysis, event data processing, recommendation generation, and user interface presentation. Each component handles a specific aspect of the problem, making the overall system more manageable and easier to implement despite the increased automation capability.
Solution Approach 2:
The system introduces intermediary datasets (layout information dataset and document event dataset) that act as buffers between the historical template data and the recommendation engine. These intermediaries simplify the complexity by pre-organizing data in structured formats that are easier to process and analyze.
3Reliability
If subjective designer judgment is used for layout, then design flexibility is maintained, but layout effectiveness and user engagement decrease
Solution Approach 1:
The system enables self-service by automatically generating layout recommendations based on historical data and document events, eliminating the need for designers to manually analyze effectiveness metrics. The system serves itself by using its own accumulated data to improve future template designs without requiring external expert judgment for each new template.
Data Source
AI summary
Embodiments of the present disclosure provides systems and methods for automatically recommending layout information for templates and template elements during template design/editing. A computer-implemented method includes receiving, based on a user interaction with the graphical user interface, a request to add a template element to a template, determining a recommended layout for the template element based on a document event dataset and a layout information dataset, presenting the recommended layout for the template element in the graphical user interface as a first recommendation; adding the template element to the template formatted according to the recommended layout for the template element and storing the new template in a database of templates used to electronically generate documents. The document event dataset comprises data for user action events on electronic documents generated from template and the layout information dataset comprises layout information from the template.