Object Identification ML for Accurate Online Listing Prices
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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 prediction and suggestion based on object identification, which includes object identification algorithms, estimation modules (UID-based, rule-based, and ML-based), and price recommendation algorithms to suggest optimal price ranges and promote listings effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users list items for sale on online platforms without assistance, then they can sell items, but they experience difficulty attracting buyers and setting appropriate prices due to inexperience and unfamiliarity
Solution Approach 1:
The system enables self-service by automatically identifying objects from images using computer vision algorithms and generating pricing recommendations without requiring user expertise. The object identification module autonomously processes uploaded images, extracts visual features, and matches objects against a database to determine category and pricing guidelines, allowing users to successfully sell items despite inexperience.
Solution Approach 2:
The system acts as an intermediary between the user and the marketplace by introducing an object identification module that bridges the gap between the user's uploaded image and the platform's pricing database. This intermediary automatically performs object recognition, category classification, and pricing recommendation, eliminating the need for users to manually research pricing information.
2Productivity
If users misidentify objects for sale, then listings may be created, but sales become slow or fail to close within necessary time frames
Solution Approach 1:
The system replaces manual object identification and classification with computer vision algorithms and machine learning models. The object identification module automatically analyzes image data, extracts visual features, and determines object category without human intervention, significantly improving identification accuracy and preventing misclassification that would slow down sales.
Solution Approach 2:
The system performs preliminary object identification and category classification before the listing is published. By pre-processing the image data and determining the correct category upfront, the system ensures accurate pricing recommendations and proper categorization from the start, preventing sales delays that would result from misidentification.
3Reliability
If users set unreasonable price expectations without guidance, then listings are created, but users become frustrated and may disengage from the platform
Solution Approach 1:
The system provides feedback by generating data-driven pricing recommendations based on historical sales data, market conditions, and object category analysis. This feedback loop gives users realistic price expectations before listing items, preventing frustration from unrealistic expectations and reducing the likelihood of disengagement. The system continuously refines pricing recommendations based on actual sales performance.
Solution Approach 2:
The system performs preliminary pricing analysis and generates recommendations before the user creates a listing. By providing upfront pricing guidance based on historical data and market conditions, users can set realistic expectations from the beginning, avoiding the frustration and time loss associated with unrealistic pricing and subsequent price reductions.
4Productivity
If the platform provides no pricing assistance, then users have freedom to set any price, but sales may not close within necessary time frames due to poor pricing decisions
Solution Approach 1:
The system enables self-service pricing by automatically analyzing object images, determining categories, and generating pricing recommendations without requiring complex user research or external tools. The object identification module autonomously processes images and provides actionable pricing guidance, improving sales closure rates while maintaining system accessibility.
Solution Approach 2:
The object identification module serves as an intermediary that connects user-uploaded images to the platform's pricing database and historical sales data. This intermediary automatically performs image analysis, object recognition, and pricing recommendation, simplifying the overall system while improving sales closure rates through data-driven pricing guidance.
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.