Crowd-Sourced Data Distribution for Real-Time Shopping
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Solution Overview
Problem
Consumers and service providers face challenges with outdated or static data in online shopping, leading to inefficient shopping experiences due to lack of real-time pricing, availability, and environmental information, which hinders optimal purchasing decisions and user satisfaction.
Innovation Solution
A system that utilizes crowd-sourced data from multiple devices to provide real-time information and recommendations through a preferred communication platform, incorporating natural language processing and machine learning to determine relevant item and environmental data, and incentivizes users to share data through a reward system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service providers use static data stored in databases, then data storage is simple and reliable, but the data becomes outdated and does not reflect real-time changes in inventory, pricing, or other relevant information
Solution Approach 1:
The system transitions from static database storage to dynamic crowd-sourced data collection, where data is continuously updated by multiple user devices in real-time. This allows the system to maintain both reliability through verification mechanisms and freshness through continuous updates from the crowd.
Solution Approach 2:
The system enables users to contribute data themselves through their devices, automatically updating the database without requiring service provider intervention. This self-service approach ensures data remains current while distributing the data collection burden across many users.
2Loss of information
If service providers collect real-time data from multiple sources, then data accuracy and relevance improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system divides data collection into independent segments contributed by individual user devices, each providing localized information. This segmentation allows the system to gather comprehensive data without requiring a centralized complex collection mechanism, as each device independently contributes its observations.
Solution Approach 2:
The system creates a universal data collection framework that works across multiple device types and platforms. By designing a multi-functional system that can accept data from various sources (mobile devices, sensors, user inputs), the complexity is distributed and standardized rather than requiring specialized handling for each source.
3Measurement precision
If consumers perform extensive research to find optimal items, then purchasing decisions improve, but time consumption and shopping efficiency deteriorate
Solution Approach 1:
The system performs preliminary data collection and analysis by the crowd before consumers need to make decisions. Real-time information about pricing, availability, and product details is pre-gathered and made accessible to consumers, eliminating the need for them to conduct extensive research at the point of purchase.
Solution Approach 2:
The system provides continuous feedback to consumers through real-time data delivery, automatically updating them on pricing changes, availability, and optimal purchasing opportunities. This feedback mechanism replaces manual research with automated information delivery, maintaining decision quality while reducing time investment.
Data Source
AI summary
There are provided systems and methods for user specific data distribution of crowd-sourced data. A user may provide data indicative of an item the user may wish to purchase, for example, through browsing history, entry of the item to a transaction, or other information. A community of users associated with the user, which may include the user or other known or nearby users, may provide real-time data collected by their devices of information that may be relevant to purchase of the item. The information may be passively or actively collected by components of devices for their devices, and may be crowd-sourced so that the community of users may elect to release the information and receive rewards by virtue of their data sharing. The information may be processed to determine a recommendation for purchase, which may be output through a commonly used communication platform for the user.