Apple inc. (20240107596). Device-Driven Network Connection simplified abstract
Contents
- 1 Device-Driven Network Connection
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Device-Driven Network Connection - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Device-Driven Network Connection
Organization Name
Inventor(s)
Tarik Tabet of Carlsbad CA (US)
Said Medjkouh of San Diego CA (US)
Sreevalsan Vallath of Dublin CA (US)
Sarma V. Vangala of Campbell CA (US)
Device-Driven Network Connection - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240107596 titled 'Device-Driven Network Connection
Simplified Explanation
A communications system described in a patent application involves a user equipment (UE) device that collects contextual information about its own usage, as well as the usage of the network and other UE devices. The UE runs an application that requests wireless data transfer and uses the gathered contextual information to train a learning model. Based on this information, the UE makes a network connectivity decision, taking into account the data sensitivity of the application. This decision is then implemented through conveyed signals.
- User equipment (UE) device gathers contextual information about its own usage, the network, and other UE devices.
- UE runs an application requesting wireless data transfer and trains a learning model based on the contextual information.
- UE makes a network connectivity decision considering the data sensitivity of the application.
- The decision is implemented through conveyed signals.
Potential Applications
The technology described in the patent application could be applied in various industries such as telecommunications, IoT, and mobile computing to optimize network performance and data transfer efficiency.
Problems Solved
This technology solves the problem of inefficient network connectivity decisions by allowing the UE device to make informed decisions based on contextual information and data sensitivity, ultimately improving overall performance.
Benefits
The benefits of this technology include optimized network performance, improved data transfer efficiency, and enhanced user experience through intelligent network connectivity decisions.
Potential Commercial Applications
- "Optimizing Network Connectivity Decisions for Improved Performance in Telecommunications and IoT"
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in network optimization and decision-making processes in the field of telecommunications.
Unanswered Questions
How does the learning model adapt to changing network conditions over time?
The patent application does not provide specific details on how the learning model continuously adapts to evolving network conditions to make accurate connectivity decisions.
What security measures are in place to protect the contextual information gathered by the UE device?
The patent application does not address the security protocols or encryption methods used to safeguard the sensitive contextual information collected by the UE device.
Original Abstract Submitted
a communications system may include a user equipment (ue) device that communicates with a network. the ue may gather first contextual information about usage of the ue device, may receive second contextual information about usage of the network, and may receive third contextual information about usage of additional ue devices. the ue may run an application that requests wireless data transfer. the ue may train a learning model based on the contextual information. the ue may make a network connectivity decision based on the contextual information, the learning model, and a data sensitivity of the application. once the connectivity decision is made, signals may be conveyed to implement the decision. performing the connectivity decision on the ue instead of the network may allow the ue and the network to take full advantage of contextual learning in different use cases to optimize performance of both the ue and the network.
