Granted Patent B2

US 12,619,669 · App. 18/461,702 · Granted May 5, 2026

Inventor: Jaynish Shashikant Patel (Muskegon, MI)

Assignee: Boost SubscriberCo L.L.C.

G06F16/9535G06F9/451H04W4/02

Abstract

Claims (84)

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.

Assignments (2)

Continuity (1)

References Cited (35)