Dynamic Group Session Data Access Protocol
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Solution Overview
Problem
In online interactive group sessions, participants face challenges in accessing relevant data quickly while ensuring information security, especially with varying access levels among participants, which can lead to inefficiencies and potential security breaches.
Innovation Solution
A system utilizing machine learning algorithms, such as LSTM Neural Networks, to analyze participant inputs, determine intent, and dynamically render enterprise data based on access levels, ensuring secure and relevant information sharing by masking data for appropriate access levels and encrypting files in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If participants search systems for information manually, then information security is maintained through access control, but the time required to access relevant data increases significantly
Solution Approach 1:
The patent replaces manual mechanical searching with an automated intelligent system that uses machine learning algorithms to analyze participant inputs, determine intent, and retrieve relevant data automatically. This substitution eliminates the time-consuming manual search process while maintaining security through automated access level verification.
Solution Approach 2:
The system enables self-service by automatically analyzing participant needs through their inputs, determining what data they require based on their access levels, and presenting relevant information without requiring participants to manually search or request data from others.
2Ease of operation
If the system provides all enterprise data to all participants, then data accessibility is maximized, but information security is compromised due to varying access levels
Solution Approach 1:
The patent applies local quality by customizing the data presentation for each participant based on their specific access level. The system analyzes individual participant credentials and dynamically renders only the portions of data they are authorized to view, ensuring each participant receives appropriately scoped information rather than a uniform dataset.
Solution Approach 2:
The system dynamically adjusts data accessibility in real-time based on participant access levels. Rather than static permission settings, the system continuously evaluates participant credentials against data classification tags and dynamically renders or masks data portions, allowing maximum accessibility within security constraints.
3Reliability
If the system dynamically masks data based on access levels, then information security is improved, but system complexity increases due to real-time analysis requirements
Solution Approach 1:
The system performs preliminary actions by pre-tagging enterprise data with access level classifications before sessions occur. Machine learning models are pre-trained to recognize patterns in participant inputs and predict intent. This preparation work reduces the complexity of real-time decision-making during actual data access operations.
Solution Approach 2:
The patent introduces an intermediary layer consisting of machine learning algorithms that mediate between participant requests and the enterprise data system. This intermediary automatically analyzes inputs, determines intent, checks access levels, and retrieves appropriate data, shielding participants from system complexity while maintaining security protocols.
4Device complexity
If manual data retrieval processes are used, then system simplicity is maintained, but productivity decreases due to time-consuming searches
Solution Approach 1:
The patent replaces simple manual data retrieval mechanics with an intelligent automated system that uses machine learning to analyze participant context, determine intent, and retrieve relevant data instantly. This substitution dramatically improves productivity by eliminating manual searching while the system manages its own complexity through automated processes.
Data Source
AI summary
Systems, methods and apparatus are provided for a Dynamic Group Session Data Access Protocol. The system may monitor participant input in a group interactive session. The system may be trained to monitor and understand the group environment and predict intent of the participant discussion and may predict relevant data. The system may be used by a single participant or by multiple participants. The system may determine the access level of the participants. The system may determine the access level of the data. The system may compare the access level of the participants with the access level of the data. The system may dynamically mask the data if the access level of the participants does not match the access level of the data. The system may create customized views of the data for each participant based on the participant's access level and the access level of the data.