Machine Learning Image Selection for Conversion-Relevant Display
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Solution Overview
Problem
Existing image presentation methods in delivery applications often display irrelevant or low-quality images, leading to user confusion and wasted computing resources, as well as a suboptimal user experience due to inefficient image selection.
Innovation Solution
A central server computer employs a scoring algorithm that combines a conversion component and an uncertainty component to dynamically select and rotate images based on user preferences and conversion rates, using machine learning techniques to optimize image selection and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image presentation methods are used, then implementation is simple, but image relevance and quality are poor leading to user confusion
Solution Approach 1:
The patent replaces traditional mechanical/image-based selection methods with machine learning algorithms. The scoring algorithm uses computational models to evaluate multiple images based on user preferences, service provider data, and contextual factors, substituting simple display logic with intelligent automated selection that improves image relevance without requiring manual intervention.
Solution Approach 2:
The system enables self-service through automated image selection where the machine learning model independently evaluates and selects optimal images without human intervention. The algorithm continuously learns from user interactions and automatically adjusts image selection based on conversion rates and user preferences, making the system self-optimizing.
2Adaptability or versatility
If multiple images are displayed to users, then image variety increases, but computing resources are wasted on low-quality or irrelevant images
Solution Approach 1:
The system evaluates a larger set of candidate images than traditionally displayed, using the scoring algorithm to assess multiple images beyond what would normally be shown. By computing scores for more images and then selecting only the top candidates, the system achieves better adaptability while managing computational resources through efficient scoring and selection rather than processing all possible images equally.
Solution Approach 2:
The patent changes the parameters of image selection by introducing multiple scoring dimensions including user preference matching, service provider relevance, image quality metrics, and conversion probability. These parameter changes enable the system to adaptively select images based on weighted criteria, improving versatility while optimizing resource allocation by focusing computation on high-potential candidates.
3Ease of operation
If static image selection is used, then system complexity is low, but user experience is suboptimal due to inefficient image selection
Solution Approach 1:
The patent transforms static image selection into a dynamic system where image choices adapt in real-time based on user interactions, conversion data, and contextual factors. The machine learning model continuously updates image scores and selections based on incoming data, making the system responsive and adaptive rather than fixed, thereby improving user experience through personalized and context-aware image presentation.
4Measurement precision
If machine learning scoring algorithm is implemented, then image selection accuracy improves, but computational requirements increase
Solution Approach 1:
The patent segments the image selection process into distinct computational stages: initial scoring of candidate images, filtering based on threshold scores, and final selection from reduced candidates. This segmentation allows the system to apply complex machine learning scoring only to necessary candidates rather than all possible images, improving selection precision while reducing overall computational energy consumption through progressive filtering.
Data Source
AI summary
A method is disclosed. The method includes receiving, by a server computer, a plurality of images associated with one or more service providers. The server computer then receives an inquiry request, and determines an image of the plurality of images. The image is selected in response to a composite score based on a scoring algorithm scoring each image in the plurality of images. The scoring algorithm comprises a conversion component and an uncertainty component. The server computer provides an inquiry response comprising the image to the end user device operated by the end user.