Decision Engine for Product Comparison and Ranking
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Solution Overview
Problem
Current search engines and faceted search tools fall short in assisting users in comparing products and making informed decisions, as they primarily rely on keyword-based searches and faceted classification systems that may not accurately reflect user interests, leading to irrelevant results and inefficient decision-making processes.
Innovation Solution
A computer-implemented method and system that integrates a decision engine with a marketing engine to provide vendor-related data based on user inputs, using weighted scores and faceted classification to match user preferences with relevant product options, facilitating a more informed decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If keyword-based search queries are used to provide product information, then users can find relevant web pages and documents, but the actual comparison of products and decision-making processes are left to users without systematic assistance
Solution Approach 1:
The system segments the decision-making process into distinct components: a decision engine that handles product comparison logic and a marketing engine that handles advertising delivery. This segmentation allows each engine to specialize in its function while working together to provide comprehensive product information and comparisons to users.
Solution Approach 2:
The patent introduces an intermediary system that connects search engines with product comparison capabilities. This intermediary processes user search queries and automatically retrieves structured product comparison data, bridging the gap between simple keyword search and comprehensive product analysis without requiring users to manually compare products.
2Productivity
If search advertising is sold and delivered on the basis of keywords with running auctions, then advertisers can reach target audiences, but advertisements are positioned based on click-through rates rather than user decision-making relevance
Solution Approach 1:
The system dynamically adjusts advertising delivery by integrating real-time product comparison data from the decision engine. Advertisements are not statically positioned based solely on historical click-through rates, but dynamically optimized based on current user preferences and product comparison metrics, making the advertising system adaptive to changing user needs.
Solution Approach 2:
The patent implements a feedback mechanism where the marketing engine receives structured data from the decision engine about user preferences and product comparisons. This feedback loop allows the system to refine advertising delivery based on actual user decision-making patterns, improving the precision of advertising relevance over time.
3Ease of operation
If faceted classification systems are used to organize product data, then users can apply multiple filters to access product datasets, but the systems may not accurately reflect user interests leading to inefficient decision-making
Solution Approach 1:
The decision engine performs preliminary analysis of user preferences and product data before the user completes their search. By pre-processing and structuring product comparison information based on anticipated user needs, the system reduces the time users spend filtering and comparing products, delivering ready-to-analyze comparison data directly to users.
Data Source
AI summary
A method for comparing decision options includes storing a set of decision options. The method includes determining for each decision option of the set of decision options, a plurality of factors, wherein each of the plurality of factors defines a respective metric. The method includes receiving a plurality of user preference indicators, each indicator corresponding to a respective factor of the plurality of factors. The method includes ranking the set of decision options in response to receiving a first user preference indicator. The method includes providing the user with a ranked list of the set of decision options in response to receiving the first user preference indicator. The method includes reranking the set of decision options in response to receiving a second user preference indicator and providing the user with a reranked list of the set of decision options in response to receiving the second user preference indicator.