Object Identification ML for Accurate Listing Price Suggestions
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Solution Overview
Problem
Users face difficulties in attracting potential buyers and setting appropriate prices for items listed on online platforms due to inexperience, unfamiliarity with the platform, and misidentification of objects for sale, leading to slow sales, frustration, and potential disengagement.
Innovation Solution
A system utilizing machine-learning algorithms to identify objects, suggest prices, and adjust pricing strategies based on historical data and user input, incorporating neural networks and computer vision for accurate identification and price recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users list items for sale on the platform without guidance, then they can quickly start selling, but they may set unreasonable prices or misidentify objects leading to slow sales and frustration
Solution Approach 1:
The patent introduces an intermediary system comprising image recognition algorithms and machine learning models that act as a mediator between the user and the listing process. The system automatically analyzes uploaded images to identify objects, retrieve relevant attributes, and suggest appropriate pricing based on historical data, thereby eliminating the need for users to manually identify objects or set prices while maintaining high accuracy
Solution Approach 2:
The system enables self-service by automatically performing object identification, attribute extraction, and pricing recommendations without requiring user expertise. The machine learning models autonomously process uploaded images, compare them against training data, and generate listing suggestions that users can accept or modify, making the process both easy and reliable
2Adaptability or versatility
If users manually identify and price items without assistance, then they have full control over listings, but sales may be slow due to inexperience and unreasonable expectations
Solution Approach 1:
The system implements feedback mechanisms where machine learning models analyze user interactions, sale outcomes, and market trends to continuously improve pricing recommendations. The system provides feedback to users about expected sale timelines and price adjustments needed, enabling users to make informed decisions that balance control with optimized sales velocity
Solution Approach 2:
The system performs preliminary actions by pre-analyzing market conditions, historical sales data, and comparable items before the user creates a listing. This advance preparation provides users with pre-calculated pricing ranges and expected sale timelines, allowing them to set optimal prices from the outset rather than learning through trial and error
3Device complexity
If the platform provides no pricing guidance or object identification tools, then the system remains simple, but users may unwittingly misidentify objects and set incorrect prices
Solution Approach 1:
The patent replaces manual mechanical processes of object identification and pricing with automated computational systems. Image recognition algorithms automatically analyze uploaded photos to identify objects and extract attributes, while machine learning models replace manual pricing research with data-driven recommendations, significantly improving accuracy without requiring users to have expert knowledge
Solution Approach 2:
The system segments the listing process into distinct automated components: image upload, object identification through image recognition, attribute extraction, pricing analysis based on historical data, and recommendation generation. This segmentation allows each component to be optimized independently while maintaining overall system simplicity from the user perspective
4Loss of time
If users experience slow sales and frustration over time, then they may overcompensate with price reductions, but this leads to further frustration and disengagement
Solution Approach 1:
The system performs preliminary analysis of market conditions, comparable sales, and optimal pricing strategies before the item is listed. By providing users with pre-calculated pricing recommendations and expected sale timelines based on historical data, the system prevents slow sales from the outset rather than requiring corrective price reductions later, thereby maintaining user satisfaction and retention
Data Source
AI summary
Disclosed herein are system, computer-program product (non-transitory computer-readable medium), and method embodiments for machine-learning prediction or suggestion based on object identification. A system including at least one processor may be configured to cross-reference an identifier of a selected object with a list of known unique identifiers. The selected object may be selected via received selection. The at least one processor may further retrieve a set of values associated with the identifier of the selected object, upon determining that the list of known unique identifiers includes the identifier of the selected object, and perform machine-learning to derive a predicted-value set based at least in part on the set of values associated with the identifier of the selected object and a category applicable to the selected object. The at least one processor may determine that the predicted-value set satisfies a predetermined confidence condition, and output at least part of the predicted-value set.