Cross-Domain Decision Aid System for Web Preference Capture
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Solution Overview
Problem
Existing decision systems on the web fail to effectively capture and utilize user preferences at the domain level, relying solely on explicit user inputs and lacking the ability to derive preferences from implicit behavior, which limits their ability to provide meaningful and personalized results.
Innovation Solution
A cross-domain decision aid system that uses Selection Criteria and Overall Stated Importance to drive an intelligent agent, which processes user behavior and preferences across multiple domains, providing personalized recommendations by combining implicit and explicit user data to offer tailored solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies solely on explicit user inputs to capture preferences, then the implementation is simple, but the preference capture accuracy is insufficient
Solution Approach 1:
The system implements feedback loops where user interactions (clicks, selections, time spent) are continuously monitored and fed back to refine preference models. This allows the system to learn from implicit behaviors and improve preference capture accuracy over time without requiring complex manual configuration
Solution Approach 2:
The patent replaces traditional explicit preference declaration mechanisms (mechanical surveys and forms) with implicit preference detection through analysis of user behavior patterns (clicks, selections, navigation). This substitution captures preferences more accurately while maintaining system simplicity
2Measurement precision
If the system captures user preferences at the account level only, then the data structure is simple, but the domain-specific preference accuracy is insufficient
Solution Approach 1:
The system segments user preferences into domain-specific components, creating separate preference profiles for different subject matter areas (e.g., healthcare, finance, technology). This segmentation allows precise capture of domain-specific preferences while organizing data in a structured, manageable way
Solution Approach 2:
The patent adds a domain dimension to the traditional account-level preference structure. Instead of a flat preference database, it creates a multi-dimensional structure where preferences are organized by both user account and domain subject matter, enabling precise domain-specific personalization
3Measurement precision
If the system uses traditional statistics from disjointed user clicks, then the data collection is simple, but the user intent synthesis accuracy is insufficient
Solution Approach 1:
The system merges multiple data sources (explicit user inputs, implicit click behavior, selection patterns, time spent on items) into a unified preference model. This integration synthesizes user intent more accurately by combining signals from different behavioral dimensions rather than relying on disjointed click statistics
Solution Approach 2:
The patent introduces an intermediary preference synthesis layer that processes and interprets raw user behaviors before presenting results. This intermediary layer translates disjointed clicks and selections into meaningful preference signals, improving intent synthesis accuracy without requiring direct complex analysis of raw data
4Loss of information
If the decision aid provides comprehensive results, then the information completeness is high, but the user decision-making efficiency is reduced
Solution Approach 1:
The system applies local quality by personalizing results based on captured user preferences and behaviors. Instead of providing uniform comprehensive results to all users, it tailors the information presentation to match individual user needs and priorities, making the information more relevant and easier to process
Solution Approach 2:
The patent implements preliminary action by pre-filtering and organizing results according to user preferences before presentation. The system proactively prepares and structures information based on captured preferences, so users receive pre-processed, personalized results that require less time to evaluate
Data Source
AI summary
A system and method for problem solving in multiple domains on the web is provided. Two facets of preference are applied regardless of domain: first, criteria selected by the user which indicates which elements relate to the user, and second, level of importance to the user. For each decision aid that the user saves to his/her member account, a series of methods applied thereto assist the user in making decisions through intelligent agent expertise, as well as through related eCommerce, social networking, guided content search and delivery of context-rich content. Relevancy of results is also calculated. Depending on characteristics inherent in a particular domain, one of two primary methods is employed. The Multi-Product method uses ontology and a neural network engine to reveal the subset of relevant results based on any combination of user inputs, implicitly and explicitly derived. The Single-Product method maps inputs to results using sub-category analysis of fit and then applies user-centric filters and discounting rules to return meaningful coaching and relevancy of results.