Hyper-Personalized Experience Blocks for Rule-Based GUI Rendering
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Solution Overview
Problem
Existing technologies fail to provide a personalized user experience tailored to individual users, resulting in a common experience for all customers.
Innovation Solution
A method and system that select and render Graphical User Interface (GUI) elements based on user profiles, using a data model schema and rules to provide hyper-personalized experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single common experience is served to all customers using pre-compiled code, then device complexity is reduced and ease of manufacture is improved, but user engagement and satisfaction deteriorate due to lack of personalization
Solution Approach 1:
The user experience is segmented into multiple experience blocks, each representing a distinct functional or content unit on the page. Each block can be independently configured with different states (e.g., collapsed/expanded, visible/hidden, different content variations), allowing granular control over personalization without requiring complete customization of the entire interface. This segmentation enables flexible composition of personalized experiences from reusable components.
Solution Approach 2:
The system implements dynamic state selection for experience blocks based on user profiles, device characteristics, and contextual factors. The state of each block (such as expansion status, visibility, or content variation) is determined dynamically at runtime rather than being fixed in advance, enabling the interface to adapt and transform based on real-time user data and preferences.
2Productivity
If pre-compiled code is used for content delivery, then processing speed and productivity are improved, but measurement precision of user preferences deteriorates
Solution Approach 1:
User profiles are pre-computed and stored before actual content delivery occurs. The system performs preliminary analysis of user behavior, preferences, and characteristics to create ready-to-use profile data structures. This preliminary action enables rapid content delivery during actual interactions while maintaining high precision in personalization decisions, as the heavy computational work of preference analysis is completed in advance.
Solution Approach 2:
A data model schema acts as an intermediary layer between raw user data and the experience block configuration. The schema defines structured relationships and validation rules that ensure user preference data is accurately captured, interpreted, and mapped to appropriate experience block states, thereby maintaining measurement precision while enabling efficient processing.
3Adaptability or versatility
If customized experience blocks are implemented for each user, then user engagement and satisfaction are improved, but device complexity and difficulty of operation increase
Solution Approach 1:
Experience blocks are designed as universal, reusable components that can serve multiple users and contexts. Each block type has a defined set of possible states and configurations that work across different users and scenarios. This universality reduces system complexity by providing a finite set of proven patterns rather than requiring unique customizations for each user, while still enabling rich personalization through selective combination and configuration of these universal blocks.
4Adaptability or versatility
If experience blocks with multiple states are used, then adaptability of user interface is improved, but manufacturing complexity and time increase
Solution Approach 1:
The system manages complexity by controlling the number and nature of states available for each experience block. Rather than allowing arbitrary customizations, the system defines a manageable set of discrete states (e.g., expanded/collapsed, visible/hidden, specific content variants) that can be efficiently selected and configured based on user profiles, thereby reducing configuration time while maintaining adequate interface flexibility.
Data Source
AI summary
This disclosure relates to a method and system for hyper-personalization of user experience. The method includes receiving a data request from a user device of a user upon accessing a page of an application. The page may include a plurality of experience blocks. Each of the plurality of experience blocks may include one or more states. Each of the one or more states may include a unique layout of GUI elements. The method further includes selecting a state from the one or more states for each of the plurality of experience blocks in a data model schema, based on a set of rules and a first user profile of the user associated with the first data request. Further, the method includes rendering the page with each of the plurality of experience blocks in the selected state on the user device to provide a hyper-personalized user experience to the user.