Advanced machine learning interfaces
A smart assistant is disclosed that provides for interfaces to capture requirements for a technical assistance request and then execute actions responsive to the technical assistance request. Example embodiments relate to parsing natural language input defining a technical assistance request to determine a series of instructions responsive to the technical assistance request. The smart assistant may also automatically detect a condition and generate a technical assistance request responsive to the condition. One or more driver applications may control or command one or more computing systems to respond to the technical assistance request.
1. A computer-implemented method comprising:
receiving, by a computer system, a technical assistance request from a user, the technical assistance request being a request to detect system outages;
analyzing, by a translator, the technical assistance request to determine an intent of the user, wherein the translator comprises a machine learning model;
generating, by the translator, a sequence of instructions responsive to the intent of the user;
performing, by a driver application, the sequence of instructions, wherein the driver application controls the execution of at least one computer applications, wherein the at least one computer applications are not the translator and not the driver application;
analyzing, by an anomaly detection system, a server log to generate a set of anomaly features, the server log comprising textual events output by a server; and
analyzing, by a machine learning model, the set of anomaly features to predict system outages, the machine learning model trained on examples of past system outages and corresponding server logs at the time of the past system outages.
2. The computer-implemented method of claim 1 further comprising:
receiving the technical assistance request in the form of natural language text;
parsing, by a parser, the technical assistance request into tokens; and
determining the intent of the user from the parsed technical assistance request using a machine learning model.
3. The computer-implemented method of claim 2 , wherein the machine learning model is a neural network.
4. The computer-implemented method of claim 1 further comprising: receiving the technical assistance request in the form of natural language text; parsing, by a parser, the technical assistance request into tokens;
analyzing the parsed technical assistance request with a machine learning model and detecting a need for additional information;
generating an information request;
displaying the information request to the user;
receiving additional information from the user;
parsing, by the parser, the additional information into tokens;
analyzing the parsed additional information with the machine learning model; and
determining the intent of the user from the parsed technical assistance request and the parsed additional information using the machine learning model.
5. The computer-implemented method of claim 1 , wherein the user interface element is a graphical user interface (GUI) request builder, wherein the GUI request builder includes a plurality of visible interface elements for building the technical assistance request.
6. The computer-implemented method of claim 1 , wherein the driver application controls the execution of the at least one computer applications by using application programming interfaces (APIs) of the at least one computer applications.
7. The computer-implemented method of claim 1 , wherein the driver application controls the execution of the at least one computer applications by using a machine learning-based driver, wherein the machine learning-based driver is a machine learning model trained on prior uses of the at least one computer applications.
8. The computer-implemented method of claim 7 , wherein the prior uses include video frames of prior uses of the at least one computer applications.
9. The computer-implemented method of claim 8 , wherein the driver application controls the execution of the at least one computer applications by mimicking human input.
10. The computer-implemented method of claim 1 , wherein the technical assistance request is a request to configure a computer environment, and the driver application configures the computer environment according to the technical assistance request.
11. A computer-implemented method comprising:
monitoring, by a computer system, user actions;
analyzing the user actions using a machine learning model;
determining, by the machine learning model based on the user actions, that the user is in need of technical assistance based on a technical assistance request that is a request to detect system outages;
determining a type of technical assistance needed by the user;
generating, by a translator, a sequence of instructions responsive to the type of technical assistance needed by the user;
performing, by a driver application, the sequence of instructions, wherein the driver application controls the execution of at least one computer applications, wherein the at least one computer applications are not the translator and not the driver application
analyzing, by an anomaly detection system, a server log to generate a set of anomaly features, the server log comprising textual events output by a server; and
analyzing, by a machine learning model, the set of anomaly features to predict system outages,
wherein the machine learning model is trained on examples of past system outages and corresponding server logs at the time of the past system outages.
12. The computer-implemented method of claim 11 , further comprising:
displaying a message to the user to ask if technical assistance is needed, prior to performing the sequence of instructions.
13. The computer-implemented method of claim 12 , further comprising: prompting the user for input about the type of technical assistance needed.
14. The computer-implemented method of claim 11 , wherein the driver application controls the execution of the at least one computer applications by using application programming interfaces (APIs) of the at least one computer applications.
15. The computer-implemented method of claim 11 , wherein the driver application controls the execution of the at least one computer applications by using a machine learning-based driver, wherein the machine learning-based driver is a machine learning model trained on prior uses of the at least one computer applications.
16. The computer-implemented method of claim 15 , wherein the prior uses include video frames of prior uses of the at least one computer applications.
17. The computer-implemented method of claim 16 , wherein the driver application controls the execution of the at least one computer applications by mimicking human input.
18. The computer-implemented method of claim 11 , wherein the technical assistance request is a request to configure a computer environment, and the driver application configures the computer environment according to the technical assistance request.