Dynamic Demand Calculation Using Captured Object Data
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Solution Overview
Problem
Current methods for managing demand for physical objects in the real world are labor-intensive and lack effective tracking and analysis, particularly in linking user interest to wish lists and tracking products through ownership cycles, leading to inefficient inventory management and marketing strategies.
Innovation Solution
A system and method using mobile devices to capture and analyze spatial, temporal, and social attributes of objects, generating aggregate lists to determine demand, and tracking objects through ownership cycles, enabling dynamic demand calculation and informed decision-making for both consumers and suppliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual wish list maintenance and product tracking is performed, then users can maintain detailed product information and track purchases, but the process becomes labor intensive and prone to errors
Solution Approach 1:
The system enables automatic self-service by having the mobile device capture product information, automatically add items to wish lists, and track purchases without requiring manual user intervention. The system monitors product movements through various stages of ownership cycle automatically, eliminating the need for users to manually update wish lists or track products.
Solution Approach 2:
The patent replaces manual mechanical processes with automated electronic systems. Mobile devices with cameras and sensors automatically capture product information, and the system electronically tracks products through ownership cycles, replacing the manual processes of writing down product details, physically tracking items, and manually updating lists.
2Productivity
If automatic product tracking through ownership cycle is implemented, then demand analysis and inventory management improve, but system complexity increases
Solution Approach 1:
The mobile device serves multiple functions: capturing product images, reading barcodes, identifying products, adding to wish lists, tracking purchases, and monitoring ownership cycle stages. This multi-functionality consolidates what would otherwise require multiple separate systems into a single device, managing complexity while improving productivity.
Solution Approach 2:
The system introduces an intermediary layer between users and products - a mobile device-based tracking system that automatically monitors product movements. This intermediary captures data at various stages of the ownership cycle and aggregates it for demand analysis, simplifying the overall system architecture while enabling efficient demand calculation.
3Measurement precision
If manual price comparison and dealer selection is performed, then users can determine optimal prices and deals, but the process is time consuming and energy intensive
Solution Approach 1:
The system performs preliminary actions by automatically capturing product information and tracking price changes before the user needs to make a purchase decision. The mobile device continuously monitors product data and aggregates it, so when the user is ready to buy, the information is already compiled and ready for analysis, eliminating the need for time-consuming manual research.
Solution Approach 2:
The system provides continuous feedback to users about product prices, availability, and demand trends. By automatically collecting and analyzing data from multiple sources, the system delivers timely information that helps users make informed decisions without requiring them to manually search and compare prices across multiple dealers.
4Measurement precision
If automated wish list updates and aggregate list generation are implemented, then demand determination is accurate, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential information needed for demand analysis from the vast amount of captured data. By focusing on key attributes such as product identification, ownership stage, and basic metadata, the system processes data efficiently without being overwhelmed by unnecessary information, maintaining accuracy while managing data volume.
Solution Approach 2:
The system performs partial data processing by selectively analyzing only the portions of captured data that are relevant for demand determination. Rather than processing every detail of every captured image and metadata field, the system identifies and processes only the critical information needed for aggregate list generation and demand calculation, reducing processing requirements while maintaining accuracy.
Data Source
AI summary
Methods and system for managing demand for an object includes capturing information about the object through a mobile device associated with a user. The mobile device is configured to capture information about the object that include one or more of a spatial, temporal, topical and social attributes of the object. The identity of the object is verified and validated using this metadata captured by the user through the device from the real world object or its proxy. Upon successful verification and validation, the object and its metadata are automatically added to a wish list of the user. An aggregate list is generated using the attributes and metadata of the object from a plurality of users. The aggregate list defines a source of demand for the object. The object is tracked as it progresses through various phases of ownership cycle using dynamic demand calculations based on the information associated with the objects, the users and the aggregate lists.