Interaction Experience Models for Personalized Retail and Social Engagement
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Solution Overview
Problem
Existing web-based retail experiences face technical challenges in providing personalized and engaging interactions for users, particularly in maintaining social connections and offering tailored content and services.
Innovation Solution
A system and method that utilizes processor-executable instructions to generate an interaction experience model based on user preferences and data, incorporating visual, audio, and tactile elements, and allows for social network integration to create customized retail and social experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If web-based retail experiences use standardized interfaces and content delivery, then system complexity is reduced and ease of operation is improved, but user personalization and engagement are worsened
Solution Approach 1:
The system performs preliminary actions by collecting user data, preferences, and behavioral patterns before the retail interaction occurs. This advance preparation enables the generation of personalized interaction experience models that adapt to individual users while maintaining standardized system operations.
Solution Approach 2:
The interaction experience models are dynamic and adaptable, allowing the system to adjust content, recommendations, and interface elements based on real-time user feedback and historical data. This dynamic adaptation enables personalization without requiring complex manual configuration.
2Adaptability or versatility
If the system collects and processes extensive user data for personalization, then adaptability and user engagement are improved, but device complexity and data processing requirements are worsened
Solution Approach 1:
The system segments user data into distinct categories (demographics, preferences, behavioral patterns) and processes each segment separately using specialized algorithms. This segmentation reduces overall system complexity by breaking down the complex task of personalization into manageable, modular components.
Solution Approach 2:
The patent introduces interaction experience models as intermediary structures that mediate between raw user data and the personalized retail experience. These models serve as intermediate processing layers that simplify data handling and enable personalization without requiring the entire system to directly process all user data.
3Productivity
If the system provides comprehensive social network integration and customized content, then user engagement and social connections are improved, but information processing requirements and system complexity are worsened
Solution Approach 1:
The system extracts only the most relevant social network data and user preferences needed for personalization, rather than processing entire social graphs or all available user information. This selective extraction reduces information processing load while maintaining effective personalization and social integration.
Solution Approach 2:
The system applies different levels of data processing and personalization to different user segments and interaction contexts. Not all users receive the same level of customization, and not all data points are processed with equal depth, optimizing resource allocation based on local requirements.
Data Source
AI summary
Embodiments described herein relate to systems, devices, articles, and methods for an affiliate, a first user, to define interaction experience model for a second user, a guest, such as, a retail experience based on approved plurality of items, and share that interaction experience with the second user.