US-12621221-B2 - Systems and methods for applying a proxy model of network quality to adjust network hardware or software

US12621221B2US 12621221 B2US12621221 B2US 12621221B2US-12621221-B2

Abstract

A machine learning-based proxy model may generate measurements of quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. Once generated, the proxy model may be applied to observed operational parameters of the on-board network to quantify the user experience for any user in any given instance. User experience measurements (e.g., trends identified therein) may be utilized to identify and implement adjustments to hardware, firmware, software, and/or service procedures associated with implementation of the on-board network. These adjustments may be implemented between transits of the vehicle, or in some cases, during transit of the vehicle to improve the user experience over the duration of use of the vehicle-based communication network.

Inventors

Pearl Grey
Jen Pardi-Cusick
Jeff Osoba

Assignees

GOGO BUSINESS AVIATION LLC

Dates

Publication Date: 20260505
Application Date: 20240403

Claims (20)

1 . A computer-implemented method implemented via one or more processors, the computer-implemented method comprising: obtaining a first observed set of operational parameter values corresponding to use of a vehicle-based communication network of a vehicle by a first user during a first transit of the vehicle; obtaining, based at least in part on the first observed set of operational parameter values, a first output of a trained proxy model, wherein the first output comprises a first measurement of quality of user experience for the first user of the vehicle-based communication network during the first transit of the vehicle; and causing, based at least in part on the first measurement of quality of user experience, one or more adjustments to the vehicle-based communication network, wherein a second measurement of quality of user experience for the first user or one or more second users of the vehicle-based communication network exceeds the first measurement of quality of user experience based at least in part on the one or more adjustments.
2 . The computer-implemented method of claim 1 , wherein the vehicle is an aircraft.
3 . The computer-implemented method of claim 1 , wherein the trained proxy model includes a k-means clustering model.
4 . The computer-implemented method of claim 1 , wherein the trained proxy model includes one or more artificial neural networks.
5 . The computer-implemented method of claim 1 , wherein the one or more processors include one or more processors on-board the vehicle.
6 . The computer-implemented method of claim 1 , wherein the one or more processors include one or more processors in a ground-based network external to the vehicle.
7 . The computer-implemented method of claim 1 , wherein the obtaining of the first output of the trained proxy model is performed during a transit of the vehicle, wherein the first observed set of operational parameter values corresponds to a first portion of the transit of the vehicle, and wherein the causing of the one or more adjustments to the vehicle-based communication network is performed during the transit of the vehicle.
8 . The computer-implemented method of claim 7 , further comprising, subsequent to the causing of the one or more adjustments to the vehicle-based communication network: obtaining a second observed set of operational parameter values corresponding to use of the vehicle-based communication network by the first user during a second portion of the first transit of the vehicle; obtaining, based at least in part on the second observed set of operational parameter values, a second output of the trained proxy model, wherein the second output comprises the second measurement of quality of user experience for the first user of the vehicle-based communication network during the second portion of the first transit of the vehicle; and comparing the first and second measurements of the quality of user experience for the first user to determine an efficacy of the one or more adjustments to the vehicle-based communication network.
9 . The computer-implemented method of claim 1 , further comprising, subsequent to the causing of the one or more adjustments to the vehicle-based communication network: obtaining a further one or more observed sets of operational parameter values corresponding to use of the vehicle-based communication network by the first user or the one or more second users during a second transit of the vehicle; obtaining, based at least in part on the further one or more observed sets of operational parameter values, one or more second outputs of the trained proxy model, wherein the one or more second outputs comprise the second measurement of quality of user experience for the first user or for the one or more second users of the vehicle-based communication network during the second transit of the vehicle; and comparing the first measurement to the further one or more measurements to determine an efficacy of the one or more adjustments to the vehicle-based communication network.
10 . The computer-implemented method of claim 1 , wherein the one or more adjustments comprise a replacement, maintenance, or reconfiguration of one or more hardware components in the vehicle-based communication network.
11 . The computer-implemented method of claim 1 , wherein the one or more adjustments comprise a replacement, update, reversion, or reconfiguration of one or more software elements in the vehicle-based communication network.
12 . The computer-implemented method of claim 1 , wherein the one or more adjustments comprise an adjustment to availability of one or more services, applications, or web sites via the vehicle-based communication network.
13 . One or more non-transitory computer readable media comprising instructions that, when executed via one or more processors, cause one or more computing devices to: obtain a first observed set of operational parameter values corresponding to use of a vehicle-based communication network of a vehicle by a first user during a first transit of the vehicle; obtain, based at least in part on the first observed set of operational parameter values, a first output of a trained proxy model, wherein the first output comprises a first measurement of quality of user experience for the first user of the vehicle-based communication network during the first transit of the vehicle; and cause, based at least in part on the first measurement of quality of user experience, one or more adjustments to the vehicle-based communication network, wherein a second measurement of quality of user experience for the first user or one or more second users of the vehicle-based communication network exceeds the first measurement of quality of user experience based at least in part on the one or more adjustments.
14 . The one or more non-transitory computer readable media of claim 13 , wherein the vehicle is an aircraft.
15 . The one or more non-transitory computer readable media of claim 13 , wherein the trained proxy model includes a k-means clustering model.
16 . The one or more non-transitory computer readable media of claim 13 , wherein the trained proxy model includes one or more artificial neural networks.
17 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more processors include one or more processors on-board the vehicle.
18 . The one or more non-transitory computer readable media of claim 13 , wherein the one or more processors include one or more processors in a ground-based network external to the vehicle.
19 . The one or more non-transitory computer readable media of claim 13 , wherein the obtaining of the first output of the trained proxy model is performed during a transit of the vehicle, wherein the first observed set of operational parameter values corresponds to a first portion of the transit of the vehicle, and wherein the causing of the one or more adjustments to the vehicle-based communication network is performed during the transit of the vehicle.
20 . The one or more non-transitory computer readable media of claim 19 , wherein the instructions, when executed via the one or more processors, further cause the one or more computing devices to: obtain a second observed set of operational parameter values corresponding to use of the vehicle-based communication network by the first user during a second portion of the first transit of the vehicle; obtain, based at least in part on the second observed set of operational parameter values, a second output of the trained proxy model, wherein the second output comprises the second measurement of quality of user experience for the first user of the vehicle-based communication network during the second portion of the first transit of the vehicle; and compare the first and second measurements of the quality of user experience for the first user to determine an efficacy of the one or more adjustments to the vehicle-based communication network.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to, and the benefit of the filing date of, U.S. Provisional Patent Application No. 63/493,983, filed Apr. 3, 2023 and entitled “Systems and Methods for Applying a Proxy Model of Network Quality to Adjust Network Hardware or Software,” the entirety of the disclosure of which is incorporated by reference herein. FIELD The present disclosure relates to communication systems, and more particularly, to techniques for determining network quality experienced by user devices over a duration of time in a communication network and implementing adjustments to a communication/system network based upon the determined quality. BACKGROUND Vehicles, including but not limited to aircraft, may establish one or more satellite-based and/or terrestrial communication links to receive information to, and/or transmit information from the vehicle. A vehicle-based communication system is typically enabled via various communication components aboard the vehicle, including for example one or more aircraft-mounted antennas and further components which may be implemented, for example, as a Line Replaceable Unit (LRU) on-board the vehicle. Operation of the vehicle-based communication system enables an on-board communication network which may, for example, allow devices of passengers to receive live media content (e.g., web browsing, sporting events, live news) at the passenger electronic devices, or enable live bidirectional communications to and from the passenger devices (e.g., internet browsing, cellular calling, etc.) using the on-board communication network. Additionally or alternatively, such communications links may enable the vehicle to communicate with the ground to support the necessary operations of vehicle instruments and/or crew (e.g., aircraft navigation systems or crew communications). A fundamental goal for any vehicle-based communication system is to operate to the satisfaction of users of the system, i.e., to provide a satisfactory on-board network experience from the perspective of the users over a duration of use of the network (e.g., over a flight in an aircraft). Various technological factors may affect the provision of a satisfactory network experience by a communication system provider. These factors may include, for example, the availability and/or performance of components of the communication system by which the network is provided, and/or the resilience of software/hardware/firmware elements to mitigate or account for issues encountered in real-time. Other technological limitations of the communication system may also play a role in user experience, such limitations not always being under the control of the provider of the on-board system. Such limitations may include for example limited bandwidth of an air-to-ground (ATG) or satellite link (and/or of the greater communication network relied upon by the on-board network, e.g., for cell towers in a greater ATG network), or latency associated with components in the greater communication network. Moreover, regardless of the level of technological resources available, and even when the system provider operates an on-board network in a manner that the system provider believes to provide a best possible experience to users (e.g., by considering tradeoffs of application/feature availability, bandwidth, latency, etc., such that the network performs “well” from the perspective of the system provider), the service parameters considered the system provider may not match the service parameters that a given user(s) most strongly correlates to their own assessment of user experience. In consideration of these factors and limitations, vehicle communication system providers have sought models for evaluate the satisfaction of users with the network experience provided to the users (or “user experience”) during any given transit, or across multiple transits. Models established by vehicle communication system providers to evaluate user experience in typically rely upon direct, explicit feedback from the users, e.g., to rate their own experience and/or identify particular factors observed to affect their own experience. To that end, system providers may provide users with surveys, e.g., delivered to respective devices of users during or immediately upon the conclusion of use of the on-board network. The present disclosure identifies, though, that feedback rate for user surveys is often low, or nonexistent for certain types of transit routes and service. Moreover, survey-based feedback, even when received, may not particularly identify the factors positively and/or negatively affecting the user experience. Accordingly, it may be difficult to configure and/or adapt the on-board network to more appropriately suit the needs and preferences of the users therein. SUMMARY The present disclosure describes systems and methods for generating and applying a proxy model to evaluate user experience in a vehicle-based com