Machine-Learned Organization Image Verification in Search Results
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Solution Overview
Problem
Existing content serving systems struggle to effectively differentiate between verified and unverified organization images and names in search results, leading to potential user confusion and misinformation.
Innovation Solution
A computer system utilizing machine-learned models to determine the acceptability of organization images and verify organization names in real-time, ensuring only trusted and verified content is presented with images and names in search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learned models are used to verify organization images and names in real-time, then the reliability of search results is improved, but the device complexity increases
Solution Approach 1:
The system performs verification of organization images and names in advance before presenting search results. Machine-learned models pre-process and validate the authenticity of organization content, so that when search results are generated, only verified content is displayed. This preliminary verification action ensures reliability without adding complexity to the real-time search process.
Solution Approach 2:
The patent introduces an intermediary verification layer between content submission and search result presentation. Machine-learned models act as mediators that automatically assess organization images and names, determining their authenticity without requiring manual review. This intermediary system handles the complexity internally while presenting simple, verified results to users.
2Loss of information
If organization images and names are verified before presentation, then the loss of information is reduced, but the loss of time increases
Solution Approach 1:
The verification system operates autonomously using machine-learned models that automatically assess organization images and names without requiring manual intervention. The system self-services the verification process, quickly determining authenticity through automated analysis. This reduces both the time required for verification and prevents misinformation from being presented, as the system independently makes verification decisions.
3Productivity
If sponsored content is allowed without verification, then the productivity of content delivery is improved, but the object-affected harmful factors increase
Solution Approach 1:
The patent applies verification selectively to specific attributes of sponsored content, particularly organization images and names, rather than verifying entire content pieces. This local quality approach verifies only the critical identification elements that prevent user confusion, while allowing sponsored content to be delivered efficiently. The verification is applied locally to specific fields rather than the entire content delivery process.
Data Source
AI summary
Techniques for presenting a search result with an improved user interface. A computer system can receive, from a user device, a request for a content item. Additionally, the system can select, based on the request, a first content item from a plurality of content items. The first content item can be associated with an organization image and an organization name of an organization. Moreover, the system can process, using one or more machine-learned model, the organization image to determine whether the organization image is acceptable to be presented in the search result. Subsequently, the system can transmit, to the user device, the first content item and the organization image to be presented in the search result.