US-12619669-B2 - System and method to generate suggestions based on dynamic banner data

US12619669B2US 12619669 B2US12619669 B2US 12619669B2US-12619669-B2

Abstract

An apparatus comprises a memory and a processor communicatively coupled to one another. The memory may be configured to store existing configuration commands instructing execution of one or more operations. The processor may be configured to collect dynamic banner data from one or more interfaces. The dynamic banner data may be representative of multiple existing operations performed by the one or more interfaces. Further, the processor may be configured to generate a plurality of dynamic configuration commands based at least in part upon the dynamic banner data. The dynamic configuration commands may be updates to the existing configuration commands. The processor may be configured to generate multiple suggestions to perform one or more suggested operations based on the dynamic configuration commands, and present the suggestions in a dynamic banner via the one or more interfaces.

Inventors

Jaynish Shashikant Patel

Assignees

Boost SubscriberCo L.L.C.

Dates

Publication Date: 20260505
Application Date: 20230906

Claims (20)

1 . An apparatus, comprising: an interface configured to perform a plurality of operations in a user equipment; a memory communicatively coupled to the interface, comprising: a plurality of existing configuration commands instructing execution of the plurality of operations in the user equipment; and a processor communicatively coupled to the memory and configured to: collect context data from the interface, wherein: the context data is representative of information surrounding the user equipment while the plurality of operations is performed by the interface; and the information surrounding the interface is collected by one or more additional interfaces of the user equipment; train, during a first stage, a machine learning algorithm using historical data associated with the user equipment to account for one or more situations and conditions changing the context data; transform, using the trained machine algorithm, the context data into structured data sets and subsequently stored as files or tables; generate, using the trained machine learning algorithm, a plurality of insights for the context data; generate, using the trained machine algorithm, a plurality of artificial intelligence commands based at least in part upon the plurality of existing configuration commands, the artificial intelligence commands being parameters configured to be combined with the plurality of existing configuration commands to create a plurality of dynamic configuration commands; generate, using the trained machine learning algorithm, the plurality of dynamic configuration commands based at least in part upon the plurality of insights for the context data and the plurality of artificial intelligence commands; modify, using the trained machine learning algorithm, the plurality of existing configuration commands to comprise the plurality of dynamic configuration commands, wherein, after updating the plurality of existing configuration commands to comprise the plurality of dynamic configuration commands, the user equipment is configured to perform an additional plurality of operations; perform, using the interface, the additional plurality of operations in the user equipment in accordance with a modified version of the plurality of existing configuration commands; and train, during a second stage, the machine learning algorithm using the historical data and the modified version of the plurality of existing configuration commands.
2 . The apparatus of claim 1 , wherein in conjunction with collecting the context data, the processor is further configured to: analyze a plurality of geolocations associated with a user comprising a first location and a second location, wherein: the first location is a current location associated with the user; and the second location is a previous location associated with the user; and determine updates to the plurality of existing configuration commands based at least in part upon historic data associated with the plurality of geolocations.
3 . The apparatus of claim 2 , wherein in conjunction with generating the plurality of dynamic configuration commands, the processor is further configured to: determine action data based at least in part upon the plurality of existing configuration commands; in accordance with a plurality of prioritization policies, determine a priority order for a plurality of suggestions; and present the plurality of suggestions in a dynamic banner in the interface based at least in part upon the priority order.
4 . The apparatus of claim 3 , wherein the plurality of suggestions are presented in the priority order and at least one static suggestion preselected to be included in the dynamic banner.
5 . The apparatus of claim 3 , wherein: the interface is a display; and the plurality of suggestions is presented in the dynamic banner via the display.
6 . The apparatus of claim 5 , wherein the dynamic banner is presented as an expandable list on a side of the display.
7 . The apparatus of claim 1 , wherein the context data from the interface is collected over a predefined time duration.
8 . A method, comprising: collecting context data from an interface in a user equipment, wherein: the context data is representative of information surrounding the user equipment while a plurality of operations is performed by the interface; and the information surrounding the interface is collected by one or more additional interfaces of the user equipment; training, during a first stage, a machine learning algorithm using historical data associated with the user equipment to account for one or more situations and conditions changing the context data; transforming, using the trained machine algorithm, the context data into structured data sets and subsequently stored as files or tables; generating, using the trained machine learning algorithm, a plurality of insights for the context data; generating, using the trained machine algorithm, a plurality of artificial intelligence commands based at least in part upon a plurality of existing configuration commands, the artificial intelligence commands being parameters configured to be combined with the plurality of existing configuration commands to create a plurality of dynamic configuration commands; generating, using the trained machine learning algorithm, the plurality of dynamic configuration commands based at least in part upon the plurality of insights for the context data and the plurality of artificial intelligence commands; modifying, using the trained machine learning algorithm, the plurality of existing configuration commands to comprise the plurality of dynamic configuration commands, wherein, after updating the plurality of existing configuration commands to comprise the plurality of dynamic configuration commands, the user equipment is configured to perform an additional plurality of operations; performing, using the interface, the additional plurality of operations in the user equipment in accordance with a modified version of the plurality of existing configuration commands; and training, during a second stage, the machine learning algorithm using the historical data and the modified version of the plurality of existing configuration commands.
9 . The method of claim 8 , wherein in conjunction with collecting the context data, the method further comprises: analyzing a plurality of geolocations associated with a user comprising a first location and a second location, wherein: the first location is a current location associated with the user; and the second location is a previous location associated with the user; and determining updates to the plurality of existing configuration commands based at least in part upon historic data associated with the plurality of geolocations.
10 . The method of claim 9 , wherein in conjunction with generating the plurality of dynamic configuration commands, the method further comprises: determining action data based at least in part upon the plurality of existing configuration commands; in accordance with a plurality of prioritization policies, determining a priority order for a plurality of suggestions; and presenting the plurality of suggestions in a dynamic banner in the interface based at least in part upon the priority order.
11 . The method of claim 10 , wherein the plurality of suggestions are presented in the priority order and at least one static suggestion preselected to be included in the dynamic banner.
12 . The method of claim 10 , wherein: the interface is a display; and the plurality of suggestions is presented in the dynamic banner via the display.
13 . The method of claim 12 , wherein the dynamic banner is presented as an expandable list on a side of the display.
14 . The method of claim 8 , wherein the context data from the interface is collected over a predefined time duration.
15 . A non-transitory computer readable medium storing instructions that when executed by a processor cause the processor to: collect context data from an interface in a user equipment, wherein: the context data is representative of information surrounding the user equipment while a plurality of operations is performed by the interface; and the information surrounding the interface is collected by one or more additional interfaces of the user equipment; train, during a first stage, a machine learning algorithm using historical data associated with the user equipment to account for one or more situations and conditions changing the context data; transform, using the trained machine algorithm, the context data into structured data sets and subsequently stored as files or tables; generate, using the trained machine learning algorithm, a plurality of insights for the context data; generate, using the trained machine algorithm, a plurality of artificial intelligence commands based at least in part upon a plurality of existing configuration commands, the artificial intelligence commands being parameters configured to be combined with the plurality of existing configuration commands to create a plurality of dynamic configuration commands; generate, using the trained machine learning algorithm, the plurality of dynamic configuration commands based at least in part upon the plurality of insights for the context data and the plurality of artificial intelligence commands; modify, using the trained machine learning algorithm, the plurality of existing configuration commands to comprise the plurality of dynamic configuration commands, wherein, after updating the plurality of existing configuration commands to comprise the plurality of dynamic configuration commands, the user equipment is configured to perform an additional plurality of operations; perform, using the interface, the additional plurality of operations in the user equipment in accordance with a modified version of the plurality of existing configuration commands; and train, during a second stage, the machine learning algorithm using the historical data and the modified version of the plurality of existing configuration command.
16 . The non-transitory computer readable medium of claim 15 , wherein in conjunction with collecting the context data, the processor is further caused to: analyze a plurality of geolocations associated with a user comprising a first location and a second location, wherein: the first location is a current location associated with the user; and the second location is a previous location associated with the user; and determine updates to the plurality of existing configuration commands based at least in part upon historic data associated with the plurality of geolocations.
17 . The non-transitory computer readable medium of claim 16 , wherein in conjunction with generating the plurality of dynamic configuration commands, the processor is further caused to: determine action data based at least in part upon the plurality of existing configuration commands; in accordance with a plurality of prioritization policies, determine a priority order for a plurality of suggestions; and present the plurality of suggestions in a dynamic banner in the interface based at least in part upon the priority order.
18 . The non-transitory computer readable medium of claim 17 , wherein the plurality of suggestions are presented in the priority order and at least one static suggestion preselected to be included in the dynamic banner.
19 . The non-transitory computer readable medium of claim 17 , wherein: the interface is a display; and the plurality of suggestions is presented in the dynamic banner via the display.
20 . The non-transitory computer readable medium of claim 19 , wherein the dynamic banner is presented as an expandable list on a side of the display.

Description

TECHNICAL FIELD The present disclosure relates generally to predicting operation suggestions in a communication system, and more specifically to a system and method to generate suggestions based on dynamic banner data. BACKGROUND In some wireless communications systems, user devices waste resources trying to identify new operations to perform. These device resources may be power resources, memory resources, and processing resources that a given user device consumes while a user attempts to determine a new operation to perform in the given user device. The device resources are wasted when the given user device lacks any immediate operations that may be useful to a corresponding user. For example, device resources may be wasted by attempting to enter a search query in a search engine and scrolling through services to identify restaurants in a city that are open at a given time just to find out that there are no restaurants open in the city for the given time. In another example, device resources may be wasted in the process of trying to decide a specific service to use among multiple services available. SUMMARY OF THE DISCLOSURE Generating Suggestions Based on Dynamic Notification Data In one or more embodiments, the system and method disclosed herein generate suggestions based on dynamic notification data. In particular, the system and method may be configured to provide real-time suggestions that recommend operations to be performed based at least in part upon context data associated with a user equipment. In one or more embodiments, the system and method described herein are integrated into a practical application to provide real-time contextual suggestions based on information shown in the user device. In some embodiments, the system and method may be configured to contextually analyze images and sounds in real-time and suggest actionable prompts based on the content of the context data and a dynamic user profile associated with the user equipment. In other embodiments, the system and method are configured to provide the suggestions based at least in part upon evaluating inventory stock data of services available to the user equipment. For example, the system and method may be configured to generate a suggestion that comprises retrieving an object from a store. In this regard, the suggestion may comprise operations that guide the user to the store after determining that the store matches preferences associated with the user device and the context data. In addition, the system and method described herein are integrated into a technical advantage of increasing processing speeds in a computer system, because processors associated with the system and method comprise a machine learning algorithm that actively generate insights for the context data. In the machine learning algorithm, the system and method may provide the dynamic configuration commands based on some or all dynamic notification data obtained from the context data. As the machine learning algorithm is trained to account for many of the situations and conditions changing in the context data, multiple dynamic configuration commands are generated to relieve stress conditions in future processing operations. In some embodiments, the system and method may generate real-time prompt suggestions that recommend operations for the user. In this regard, resources may be saved in the user equipment by identifying new relevant operations to perform. The device resources may be power resources, memory resources, and processing resources that the user equipment saves by proactively and automatically determining a new immediate operation to perform. In one or more embodiments, the system and method may be performed by an apparatus, such as a server, communicatively coupled to multiple network components in a core network, one or more base stations in a radio access network, and one or more user equipment. Further, the system may be a wireless communication system, which comprises the apparatus. In addition, the system and method may be performed as part of a process performed by the apparatus communicatively coupled to the network components in the core network. As a non-limiting example, the apparatus may comprise a memory and a processor communicatively coupled to one another. The memory may be configured to store multiple existing configuration commands instructing execution of one or more operations. The processor may be configured to perform multiple existing operations in accordance with the existing configuration commands, and collect dynamic notification data from one or more interfaces configured to perform the existing operations. The dynamic notification data may comprise context data representative of the existing operations performed by the one or more interfaces. Further, the processor is configured to generate multiple dynamic configuration commands based at least in part upon the dynamic notification data. The dynamic configuration commands may co