Computational Platform Integrating Data Sharing via Share Codes
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Solution Overview
Problem
Current online transaction processors lack effective data sharing mechanisms across platforms, leading to inefficient use of user data for artificial intelligence training and processing, resulting in a self-contained system where valuable data remains unused and not optimized for AI applications.
Innovation Solution
A computational platform integrates different data sharing platforms using API integrations to track user interactions, generate offers based on machine learning models, and incentivize users to share transaction histories through a share code system, allowing for data tracking and reward mechanisms across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users are not incentivized to share data, then data privacy and security are maintained, but data quantity and quality for AI training remain insufficient
Solution Approach 1:
The patent introduces an intermediary incentive mechanism that mediates between data providers and data consumers. The system uses reward points as an intermediary currency to facilitate data sharing while maintaining privacy controls. Users share data through the platform's controlled interface rather than direct exposure, and the incentive system acts as a buffer that motivates sharing without compromising security.
Solution Approach 2:
The patent implements a feedback loop where users receive immediate rewards and recognition for data sharing activities. The system tracks data contributions, assigns reward points, and provides continuous feedback to users about their sharing impact. This positive feedback mechanism encourages sustained data sharing behavior while the platform maintains oversight to ensure privacy compliance.
2Adaptability or versatility
If data is shared across multiple platforms, then data utility for AI training increases, but system complexity and integration challenges increase
Solution Approach 1:
The patent creates a universal data sharing platform that serves multiple functions: data collection, incentive distribution, privacy management, and AI training facilitation. The system is designed to work with various types of data (transaction histories, social media posts, browsing behavior) and multiple AI applications, making it a multi-functional solution that reduces the need for separate specialized systems.
Solution Approach 2:
The platform acts as an intermediary layer between diverse data sources and AI training systems. Rather than requiring direct integration between each data platform and AI system, the patent's platform standardizes data collection and distribution protocols, simplifying the integration architecture and reducing overall system complexity.
3Measurement precision
If manual data input processes are required, then data accuracy can be verified, but user time and effort requirements increase
Solution Approach 1:
The patent enables self-service data collection where users automatically share their existing data (transaction histories, social media posts, browsing behavior) through the platform's integrated connectors. The system automatically verifies, validates, and processes this data without requiring manual user intervention, thus maintaining accuracy through automated checks while minimizing user time investment to simple authorization actions.
4Reliability
If data remains in self-contained systems, then data security is maintained, but data relevance and currentness for users deteriorate
Solution Approach 1:
The platform serves as a secure intermediary that enables data to remain protected while still being made relevant to users. Data stays within the platform's controlled environment where security protocols are maintained, but the platform processes and distributes this data to create relevant user experiences through AI-generated insights and personalized content, thus maintaining both security and relevance.
Data Source
AI summary
There are provided systems and methods for a computational platform using machine learning for integration data sharing platforms. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items from a merchant. A service provider may provide a data feed to the user via integrated computational platforms that allows the user to post data including information regarding the processed transaction. The post may include a share code that links back to the user and their corresponding transaction. Thereafter, the post may be viewed by other users and the share code may be used by the other users in order to perform similar transaction processing, where these later transactions are linked back to the original user. Tracking of these later transactions may be done through application extensions that allow the computational platforms to track user data over different online interactions.