Transparent Container for Non-Standardized Data Collection
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Solution Overview
Problem
Current technologies lack a framework for efficiently collecting non-standardized data from mobile devices for training Artificial Intelligence and Machine Learning models, as much of the data collected is proprietary and not standardized, making it difficult to access and utilize effectively.
Innovation Solution
A system is developed that determines a non-standardized data collection policy, receives and processes non-standardized data requests, and collects data from User Equipment (UE) devices in a transparent container, allowing for the transfer of non-standardized data to external servers for AI/ML model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected in proprietary non-standardized formats from different device manufacturers and operating systems, then the volume and diversity of available data increases, but the data becomes difficult to access and utilize effectively for AI/ML training
Solution Approach 1:
The patent introduces a standardized data collection framework that acts as an intermediary layer between diverse device data sources and AI/ML training systems. This framework includes standardized data elements, collection policies, and protocols that mediate the interaction between proprietary device formats and universal AI training requirements, enabling effective utilization of data from multiple manufacturers and operating systems without requiring customization for each source.
2Ease of operation
If standardized data collection protocols are implemented across all devices, then data accessibility and usability improve, but the ability to capture device-specific proprietary data formats and features is reduced
Solution Approach 1:
The patent segments data collection into two distinct layers: standardized data elements that ensure universal accessibility and compatibility, and optional proprietary extensions that preserve device-specific capabilities. The standardized layer includes common data types and structures that can be collected from all devices, while the proprietary layer allows individual manufacturers to include additional device-specific data formats and features, thereby achieving both accessibility and adaptability simultaneously.
3Reliability
If comprehensive data collection policies are established to enable AI/ML training, then the utility and accuracy of trained models improve, but privacy concerns and data security risks increase
Solution Approach 1:
The patent changes the parameters of data collection by introducing granular control mechanisms including data collection policies that specify what data can be collected, from which devices, and under what conditions. The system includes configurable parameters for data element selection, geographic restrictions, time-based controls, and device-specific permissions, allowing comprehensive data collection for AI/ML training while maintaining privacy and security through policy-based restrictions and user consent management.
Data Source
AI summary
Method, apparatuses, and computer program products provide means for establishing a mechanism through which a network can configure user equipment to determine how to process an indication of region of the user equipment location when establishing a network connection. An example method includes: receiving, from a visited network, a first indication of a region of an apparatus location, where the apparatus includes a User Equipment (UE); and determining, based on a second indication in the apparatus, whether the apparatus shall ignore the first indication for network selection, where the second indication is received from a home network of the apparatus via a container transparent to the visited network. The region of the apparatus location may include a country of the apparatus location. The container may include a Steering of Roaming transparent container. The container may be delivered via a rejection message.