Multi-User Product Customization via Profile Matching
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Solution Overview
Problem
Current systems lack the ability to assist users in purchasing products or services for others, failing to provide personalized and customized offerings that cater to the preferences and profiles of multiple users.
Innovation Solution
A system and method that receives profile and preference information from multiple users, matches offerings with users based on their profiles, and allows for customized proposals with payment options, enabling users to select and propose offerings that align with the interests and preferences of others, with the option to customize the offering based on user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a system allows users to purchase products or services for others with personalized offerings, then user satisfaction and customization capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex purchasing process into distinct modules: profile information collection, offering selection, proposal generation, and payment processing. Each module handles a specific aspect of the customization task, making the overall system more manageable despite its versatility.
Solution Approach 2:
The system introduces an intermediary platform that mediates between the purchaser and the vendee. This intermediary handles the complex matching of offerings to user profiles, generates personalized proposals, and manages payment coordination, thereby enabling high customization without requiring direct complex interactions between users.
2Measurement precision
If the system matches offerings with multiple users based on profile information, then personalization accuracy is improved, but information processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing detailed profile information from users, including preferences, demographics, and behavioral data. This pre-processing of data enables faster and more accurate matching when offerings need to be recommended, as the foundation for comparison is already in place.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses to proposals and purchasing patterns are fed back into the profile database. This continuous refinement of profile information improves matching accuracy over time while the system learns to optimize processing efficiency based on actual user behavior patterns.
3Adaptability or versatility
If the system provides discounted prices and extra services for specific offerings, then customer value and appeal are improved, but pricing complexity and vendor management difficulty increase
Solution Approach 1:
The system applies local quality by providing different pricing and service levels for different offerings and different user segments. Rather than a uniform pricing structure, the system tailors discounts and extra services to specific offerings and specific user profiles, making the pricing appear customized and valuable while managing complexity through localized adjustments rather than system-wide changes.
Data Source
AI summary
A system and method allows one or more users to purchase an offering of goods or services using at least three computer systems, by one user making a proposal for the two to jointly consume the offering, and the other user accepting the proposal.