US-12627569-B2 - Technique for defining features and predicting likelihood of adoption of the same using machine learning models

Abstract

In one aspect, a method of identifying network features includes receiving a first-time definition of a feature, the feature representing a user query for analytics associated with the feature based on data collected on a plurality of devices in one or more networks, generating the analytics associated with the feature, determining, using a trained machine learning model, a likelihood of adoption of at least the feature by one or more users of the plurality of devices, wherein the trained machine learning model receives as input the first-time definition and provides, as output, the likelihood of adoption of at least the feature, and configuring a user interface on a terminal to provide a visualization of at least one of the likelihood of adoption of at least the feature and the analytics associated with the feature.

Inventors

• MOHAMMAD SHAMI
• Vikramjeet Singh Chauhan
• Caroline Dahllof

Assignees

• CISCO TECHNOLOGY, INC.

Dates

Publication Date

20260512

Application Date

20230404

Claims (20)

1. 1 . A method of identifying network features comprising: receiving a first-time definition of a feature, the feature representing a user query for analytics associated with the feature based on data collected on a plurality of devices in one or more networks, wherein the data is stored in a database; generating the analytics associated with the feature; determining, using a trained machine learning model, a likelihood of adoption of at least the feature by one or more users of the plurality of devices, wherein the trained machine learning model receives as input the first-time definition and provides, as output, the likelihood of adoption of at least the feature, wherein the likelihood of adoption of the feature is represented by a numerical value that indicates how likely the one or more users are to use the feature in the future; and configuring a user interface on a terminal to provide real-time status update on running the trained machine learning model for determining the likelihood of adoption of the at least one feature, and to visually display at least one of the likelihood of adoption of at least the feature and the analytics associated with the feature.
2. 2 . The method of claim 1 , wherein the trained machine learning model further receives, as the input, updated information on current utilization of the plurality of devices.
3. 3 . The method of claim 2 , wherein the trained machine learning model further receive as the input, historical data on device utilization by users of the plurality of devices.
4. 4 . The method of claim 3 , wherein determining the likelihood of adoption of at least the feature is based on the first-time definition, the updated information on current utilization of the plurality of devices, and the historical data on device utilization.
5. 5 . The method of claim 1 , wherein the first-time definition is a description of attributes associated the feature.
6. 6 . The method of claim 5 , further comprising: generating the definition based on the description of the attributes.
7. 7 . The method of claim 6 , wherein the definition is generated using a trained machine learning model that receives the description of the attributes as input, and provides the definition as output.
8. 8 . A network controller comprising: one or more memories having computer-readable instructions stored therein; and one or more processors configured to execute the computer-readable instructions to: receive a first-time definition of a feature, the feature representing a user query for analytics associated with the feature based on data collected on a plurality of devices in one or more networks, wherein the data is stored in a database; generate the analytics associated with the feature; determine, using a trained machine learning model, a likelihood of adoption of at least the feature by one or more users of the plurality of devices, wherein the trained machine learning model receives as input the first-time definition and provides, as output, the likelihood of adoption of at least the feature, wherein the likelihood of adoption of the feature is represented by a numerical value that indicates how likely the one or more users are to use the feature in the future; and configure a user interface on a terminal to provide real-time status update on running the trained machine learning model for determining the likelihood of adoption of the at least one feature, and to visually display at least one of the likelihood of adoption of at least the feature and the analytics associated with the feature.
9. 9 . The network controller of claim 8 , wherein the trained machine learning model further receives, as the input, updated information on current utilization of the plurality of devices.
10. 10 . The network controller of claim 9 , wherein the trained machine learning model further receives, as the input, historical data on device utilization by users of the plurality of devices.
11. 11 . The network controller of claim 10 , wherein determining the likelihood of adoption of at least the feature is based on the first-time definition, the updated information on current utilization of the plurality of devices, and the historical data on device utilization.
12. 12 . The network controller of claim 8 , wherein the first-time definition is a description of attributes associated the feature.
13. 13 . The network controller of claim 12 , wherein the one or more processors are further configured to execute the computer-readable instructions to generate the definition based on the description of the attributes.
14. 14 . The network controller of claim 13 , wherein the definition is generated using a trained machine learning model that receives the description of the attributes as input, and provides the definition as output.
15. 15 . One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors of a network controller, cause the network controller to: receive a first-time definition of a feature, the feature representing a user query for analytics associated with the feature based on data collected on a plurality of devices in one or more networks, wherein the data is stored in a database; generate the analytics associated with the feature; determine, using a trained machine learning model, a likelihood of adoption of at least the feature by one or more users of the plurality of devices, wherein the trained machine learning model receives as input the first-time definition and provides, as output, the likelihood of adoption of at least the feature, wherein the likelihood of adoption of the feature is represented by a numerical value that indicates how likely the one or more users are to use the feature in the future; and configure a user interface on a terminal to provide real-time status update on running the trained machine learning model for determining the likelihood of adoption of the at least one feature, and to visually display at least one of the likelihood of adoption of at least the feature and the analytics associated with the feature.
16. 16 . The one or more non-transitory computer-readable media of claim 15 , wherein the trained machine learning model further receives, as the input, updated information on current utilization of the plurality of devices.
17. 17 . The one or more non-transitory computer-readable media of claim 16 , wherein the trained machine learning model further receives, as the input, historical data on device utilization by users of the plurality of devices.
18. 18 . The one or more non-transitory computer-readable media of claim 17 , wherein determining the likelihood of adoption of at least the feature is based on the first-time definition, the updated information on current utilization of the plurality of devices, and the historical data on device utilization.
19. 19 . The one or more non-transitory computer-readable media of claim 15 , wherein the first-time definition is a description of attributes associated the feature.
20. 20 . The one or more non-transitory computer-readable media of claim 19 , wherein the execution of the computer-readable instructions cause the network controller to generate the definition based on the description of the attributes using a trained machine learning model that receives the description of the attributes as input, and provides the definition as output.

Description

TECHNICAL FIELD The present disclosure generally relates to the field of computer networking, and more particularly to analytical tools that provide insight into utilization of network devices and solutions and enable prediction of adoption of various hardware and software features in the network. BACKGROUND Enterprise networks and application data centers are typically managed by independent teams, with very little information sharing between the two teams. Data analysts are often asked to define a new feature from existing data and then prepare reports and visualizations to demonstrate the significance (or lack thereof) of the newly defined feature. These projects can be very time consuming and require a lot of work by data analysts to run queries to collect all the data sets needed to compare against the newly defined feature. BRIEF DESCRIPTION OF THE FIGURES To provide a more complete understanding of the present disclosure and features and advantages thereof, reference is made to the following description, taken in conjunction with the accompanying drawings, in which: FIG. 1 illustrates an example of a physical topology of an enterprise network according to some aspects of the present disclosure; FIG. 2 illustrates an example of a logical architecture for an enterprise network according to some aspects of the present disclosure; FIG. 3A is an example screenshot of a user interface for defining a feature according to some aspects of the present disclosure; FIG. 3B illustrates another example screenshot of a user interface for defining a feature according to some aspects of the present disclosure; FIG. 4 illustrates an example neural network that can be utilized for feature definition and analysis according to some aspects of the present disclosure; FIG. 5 provides an example output of a trained machine learning algorithm indicative of feature analysis according to some aspects of the present disclosure; FIG. 6 provides another example output of a trained machine learning algorithm indicative of feature analysis according to some aspects of the present disclosure; FIG. 7 illustrates an example method of defining features and predicting likelihood of adoption of features according to some aspects of the present disclosure; and FIG. 8 illustrates an example of a bus computing system, according to some aspects of the present disclosure. DETAILED DESCRIPTION Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments. Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification. Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise d