Intelligent generation and management of estimates for application of updates to a computing device
The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updates to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.
1. A computer-implemented method comprising:
identifying an update to be applied to a computing device;
applying a trained artificial intelligence (AI) model that is adapted to generate an update estimate predicting an amount of time that is required to apply the update on the computing device, wherein the update estimate is generated based on a contextual analysis that evaluates:
parameters associated with the update,
device characteristics of the computing device, and
a state of current user activity comprising user activity parameters that indicate applications currently executing on the computing device and a geographical identification of a location of the computing device;
generating a notification that comprises the update estimate; and
causing the notification to be rendered for the computing device.
2. The computer-implemented method of claim 1 , wherein the parameters associated with the update, that are evaluated in the contextual analysis, comprise: a type of the update and a size of the update.
3. The computer-implemented method of claim 1 , wherein the device characteristics, that are evaluated in the contextual analysis, comprise device parameters indicating: a type of hard drive installed in the computing device; an age of the hard drive installed in the computing device; specifications associated with random access memory (RAM) installed in the computing device; and specifications associated with a processor installed in the computing device.
4. The computer-implemented method of claim 1 , wherein the contextual analysis, used to generate the update estimate, further evaluates: historical predictions relating to prior update estimates for one or more computing devices that comprise the computing device.
5. The computer-implemented method of claim 1 , wherein the trained AI model is trained based on: retail device data from a user population of retail computing devices associated with a software data platform; and a corpus of training data comprising feedback on update estimates from prior iterations of the trained AI model.
6. The computer-implemented method of claim 1 , further comprising: generating a data insight providing a content indicating a rationale supporting the update estimate including parameters that were used to generate a prediction indicating the amount of time that is required to apply the update on the computing device; and transmitting the data insight to the computing device for rendering.
7. The computer-implemented method of claim 1 , further comprising: detecting a change to the state of current user activity of the computing device; generating, using the trained AI model, a second update estimate; and transmitting the second update estimate to the computing device.
8. A system comprising:
at least one processor; and
a memory, operatively connected with the at least one processor, storing computer-executable instructions that, when executed by the at least one processor, causes the at least one processor to execute a method that comprises:
identifying an update to be applied to a computing device;
applying a trained artificial intelligence (AI) model that is adapted to generate an update estimate predicting an amount of time that is required to apply the update on the computing device, wherein the update estimate is generated based on a contextual analysis that evaluates:
parameters associated with the update,
device characteristics of the computing device, and
a state of current user activity comprising user activity parameters that indicate applications currently executing on the computing device and a geographical identification of a location of the computing device;
generating a notification that comprises the update estimate; and
causing the notification to be rendered for the computing device.
9. The system of claim 8 , wherein the parameters associated with the update, that are evaluated in the contextual analysis, comprise: a type of the update and a size of the update.
10. The system of claim 8 , wherein the device characteristics, that are evaluated in the contextual analysis, comprise device parameters indicating: a type of hard drive installed in the computing device; an age of the hard drive installed in the computing device; specifications associated with random access memory (RAM) installed in the computing device; and specifications associated with a processor installed in the computing device.
11. The system of claim 8 , wherein the contextual analysis, used to generate the update estimate, further evaluates: historical predictions relating to prior update estimates for one or more computing devices that comprise the computing device.
12. The system of claim 8 , wherein the trained AI model is trained based on: retail device data from a user population of retail computing devices associated with a software data platform; and a corpus of training data comprising feedback on update estimates from prior iterations of the trained AI model.
13. The system of claim 8 , wherein the method, executed by the at least one processor, further comprises: generating a data insight providing a content indicating a rationale supporting the update estimate including parameters that were used to generate a prediction indicating the amount of time that is required to apply the update on the computing device; and transmitting the data insight to the computing device for rendering.
14. The system of claim 8 , wherein the method, executed by the at least one processor, further comprises: detecting a change to the state of current user activity of the computing device; generating, using the trained AI model, a second update estimate; and transmitting the second update estimate to the computing device.
15. A computer-implemented method comprising:
identifying an update to be applied to a computing device;
applying a trained artificial intelligence (AI) model that is adapted to generate an update estimate predicting an amount of time that is required to apply the update on the computing device, wherein the update estimate is generated based on a contextual analysis that evaluates:
parameters associated with the update,
device characteristics of the computing device, and
a state of current user activity comprising user activity parameters that indicate applications currently executing on the computing device and a geographical identification of a location of the computing device;
generating a notification that comprises the update estimate; and
causing the notification to be rendered in a graphical user interface (GUI) executing on the computing device.
16. The computer-implemented method of claim 15 , further comprising: propagating the notification to another computing device registered to a user account associated with a user of the computing device.
17. The computer-implemented method of claim 15 , wherein the parameters associated with the update, that are evaluated in the contextual analysis, comprise: a type of the update and a size of the update.
18. The computer-implemented method of claim 15 , further comprising: generating a data insight providing a content indicating a rationale supporting the update estimate including parameters that were used to generate a prediction indicating the amount of time that is required to apply the update on the computing device; and rendering the data insight in the GUI.
19. The computer-implemented method of claim 15 , wherein the device characteristics, that are evaluated in the contextual analysis, comprise device parameters indicating: a type of hard drive installed in the computing device, an age of the hard drive installed in the computing device, specifications associated with random access memory (RAM) installed in the computing device, and specifications associated with a processor installed in the computing device.
20. The computer-implemented method of claim 15 , wherein the state of current user activity on the computing device, that is evaluated in the contextual analysis, comprises user activity parameters indicating: applications and/or services currently executing on the computing device, and geographical identification of a location of the computing device.