Group Data Objects for Secure Real-Time Functionality Enablement
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Solution Overview
Problem
Existing digital platforms face challenges in securely integrating user group functionalities across multiple platforms, managing real-time updates, and efficiently processing transactions for group data objects, while ensuring security and reducing computational resources.
Innovation Solution
The implementation of group data objects with sub-accounts and intelligent, real-time updates, utilizing machine learning models to automatically generate and manage user groups, encrypt data, and optimize transactions, thereby reducing the number of transactions and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional user account management is used across multiple platforms, then user group functionalities can be integrated, but security risks and computational overhead increase significantly
Solution Approach 1:
The patent segments user account management by introducing group data objects that represent specific user groups on specific platforms. Each group data object is a discrete unit that can be independently managed, encrypted, and processed. This segmentation allows the system to handle only relevant group information rather than all user account data, reducing computational overhead while maintaining security through targeted data protection.
Solution Approach 2:
The patent introduces group data objects as intermediary entities between user accounts and platform functionalities. These group data objects serve as mediators that encapsulate user group information, enabling secure interaction across platforms without exposing underlying user account details. This intermediary layer reduces computational overhead by providing a standardized interface while maintaining security through controlled data access.
2Reliability
If real-time updates are implemented for group data objects, then user group functionalities remain current, but processing time and computational resources increase
Solution Approach 1:
The patent implements dynamic update mechanisms where group data objects are updated in real-time only when necessary changes occur. The system dynamically determines which group data objects need updating based on user actions, platform events, or data changes. This dynamic approach maintains data currentness while avoiding unnecessary processing of unchanged data, thereby reducing overall processing time and computational resource consumption.
Solution Approach 2:
The patent applies local quality by updating only the specific portions of group data objects that are affected by changes, rather than performing full updates across all group data objects. When a user joins or leaves a group, or when group attributes change, only the relevant group data object or specific fields within it are updated. This localized update strategy maintains data currentness while significantly reducing processing time and computational overhead compared to global updates.
3Loss of information
If multiple transactions are processed for group operations, then all user interactions are recorded, but the number of transactions and computational resources required increase
Solution Approach 1:
The patent merges multiple individual user transactions into consolidated group-level transactions. Instead of processing separate transactions for each user's actions within a group, the system consolidates these into single group data object updates. For example, when multiple users join the same group simultaneously, the system processes a single consolidated update rather than multiple separate transactions. This merging approach maintains complete transaction records for auditability while significantly improving processing efficiency by reducing the total number of transactions required.
Solution Approach 2:
The patent implements universal group data objects that can represent multiple user groups across different platforms with a single data structure. This multi-functional design allows the same group data object to serve various purposes (e.g., representing a user group on both a messaging platform and a payment platform) without requiring separate transaction processing for each platform. This universality maintains comprehensive transaction information while improving productivity by reducing redundant processing across platforms.
Data Source
AI summary
Techniques described herein are directed to generating group data objects and monitoring inputs associated with the group data objects to determine if attribute thresholds are satisfied. When satisfied, functionality may be enabled for the users associated with the group data objects. Additionally, when change to the group data object are detected, user interfaces associated with the group data object may be updated to provide real-time indications of information and functionality associated with the group data object.