Machine Learning Search Optimization Feedback Loop
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Solution Overview
Problem
Current search engines fail to effectively incentivize users for their submitted information and struggle to provide relevant advertisements, leading to less useful search results for users and inefficient advertising expenditures for advertisers due to conflicting motivations between user and advertiser needs.
Innovation Solution
A machine-learning based platform that compensates users for their information and allows advertisers to target specific users with personalized advertisements, using user search requests, transaction details, and profile information to optimize search engine results and advertising effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If search engine providers prioritize paid advertisements in search results, then advertiser revenue is improved, but user search result quality deteriorates
Solution Approach 1:
The patent introduces a feedback mechanism as an intermediary between advertisers and users. Advertisers pay for advertisements, but the system provides feedback on whether users actually purchased the advertised products. This mediator layer allows the system to collect data on advertising effectiveness while maintaining separate evaluation of search result quality, resolving the contradiction between prioritizing advertiser revenue and maintaining user experience.
2Productivity
If search engines collect and utilize user information for targeted advertising, then advertising effectiveness is improved, but user privacy and control deteriorate
Solution Approach 1:
The patent implements a feedback loop where advertisers receive information about whether users actually purchased their advertised products. This feedback mechanism allows the system to improve advertising effectiveness by learning from actual user behavior while maintaining user control, as users can opt-out of the feedback collection. The feedback system transforms raw user data into aggregated effectiveness metrics that protect individual privacy while improving overall advertising performance.
3Illumination intensity
If advertisers pay for prominent advertisement placement, then advertisement visibility is improved, but advertising efficiency deteriorates
Solution Approach 1:
The patent introduces feedback on actual purchase outcomes to advertisers. This feedback allows advertisers to evaluate whether their paid prominent placement actually resulted in purchases, enabling them to adjust their spending and targeting strategies. The feedback mechanism transforms the black-box advertising spend into a measurable performance metric, improving advertising efficiency by allowing data-driven optimization of visibility investments.
Data Source
AI summary
Machine-learning based systems and methods are described for optimizing search engine results. A server receives, via a computer network, and associates, via a user profile, user information including search requests, transaction details, and/or profile information, for which a user receives purchasing units. The server executes a machine-learning component to predict, based on the user information, a user action score defining a probability of a user to engage in a new transaction. The server executes a search engine optimization component that receives, from the user's device, a new search request causing the search engine optimization component to generate a search engine offer associated with the new transaction. The server transmits the search engine offer to, and receives an acceptance from, computing device(s) of search engine market participant(s), the acceptance causing a targeted advertisement and search results to be returned to the user device in response to the new search request.