Chatbot for defining a machine learning (ML) solution

Patent metadata

Field	Value
Publication number	US-12190254-B2
Application number	US-202318501716-A
Country	US
Kind code	B2
Filing date	Nov 3, 2023
Priority date	Sep 14, 2019
Publication date	Jan 7, 2025
Grant date	Jan 7, 2025

Abstract

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

First claim

What is claimed is:

1. A computer-implemented method comprising:
receiving, from a user device, a first input;
predicting, using a computational technique, a type of desired result based on the first input;
identifying, using metadata associated with each machine-learning-model framework of a set of machine-learning-model frameworks, one or more machine-learning-model frameworks based on the predicted type of desired result, wherein the one or more machine-learning-model frameworks are of the set of machine-learning-model frameworks;
presenting, for each machine-learning-model framework of the one or more machine-learning-model frameworks, a representation of a corresponding machine-learning-model architecture on a display, wherein one or more machine-learning-model architectures are presented;
receiving a second input identifying a selection of a particular machine-learning-model architecture of the one or more machine-learning-model architectures;
receiving a third input identifying a data source for generating a machine learning architecture;
receiving a fourth input identifying one or more constraints for the machine learning architecture;
generating code for a machine learning model based at least in part on the second input identifying the selection of the particular machine-learning-model architecture, the third input identifying the data source, and the fourth input identifying the one or more constraints; and
storing the generated code in a memory.

2. The computer-implemented method of claim 1 , wherein the particular machine-learning-model architecture includes a neural network.

3. The computer-implemented method of claim 1 , wherein the particular machine-learning-model architecture includes a classifier network.

4. The computer-implemented method of claim 1 , further comprising:
analyzing the one or more constraints to generate a second plurality of code for the particular machine-learning-model architecture based at least in part on optimizing the one or more constraints;
generating an optimized solution; and
displaying the optimized solution.

5. The computer-implemented method of claim 1 , further comprising deploying the particular machine-learning-model architecture via an intelligent assistant interface.

6. The computer-implemented method of claim 1 , wherein the first input includes textual input.

7. The computer-implemented method of claim 1 , wherein the one or more constraints includes a constraint pertaining to at least one of resources, location, security, or privacy.

8. A system comprising:
one or more data processors; and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of actions including:
receiving, from a user device, a first input;
predicting, using a computational technique, a type of desired result based on the first input;
identifying, using metadata associated with each machine-learning-model framework of a set of machine-learning-model frameworks, one or more machine-learning-model frameworks based on the predicted type of desired result, wherein the one or more machine-learning-model frameworks are of the set of machine-learning-model frameworks;
presenting, for each machine-learning-model framework of the one or more machine-learning-model frameworks, a representation of a corresponding machine-learning-model architecture on a display, wherein one or more machine-learning-model architectures are presented;
receiving a second input identifying a selection of a particular machine-learning-model architecture of the one or more machine-learning-model architectures;
receiving a third input identifying a data source for generating a machine learning architecture;
receiving a fourth input identifying one or more constraints for the machine learning architecture;
generating code for a machine learning model based at least in part on the second input identifying the selection of the particular machine-learning-model architecture, the third input identifying the data source, and the fourth input identifying the one or more constraints; and
storing the generated code in a memory.

9. The system of claim 8 , wherein the particular machine-learning-model architecture includes a neural network.

10. The system of claim 8 , wherein the particular machine-learning-model architecture includes a classifier network.

11. The system of claim 8 , wherein the set of actions further includes:
analyzing the one or more constraints to generate a second plurality of code for the particular machine-learning-model architecture based at least in part on optimizing the one or more constraints;
generating an optimized solution; and
displaying the optimized solution.

12. The system of claim 8 , wherein the set of actions further includes deploying the particular machine-learning-model architecture via an intelligent assistant interface.

13. The system of claim 8 , wherein the first input includes textual input.

14. The system of claim 8 , wherein the one or more constraints includes a constraint pertaining to at least one of resources, location, security, or privacy.

15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of actions including:
receiving, from a user device, a first input;
predicting, using a computational technique, a type of desired result based on the first input;
identifying, using metadata associated with each machine-learning-model framework of a set of machine-learning-model frameworks, one or more machine-learning-model frameworks based on the predicted type of desired result, wherein the one or more machine-learning-model frameworks are of the set of machine-learning-model frameworks;
presenting, for each machine-learning-model framework of the one or more machine-learning-model frameworks, a representation of a corresponding machine-learning-model architecture on a display, wherein one or more machine-learning-model architectures are presented;
receiving a second input identifying a selection of a particular machine-learning-model architecture of the one or more machine-learning-model architectures;
receiving a third input identifying a data source for generating a machine learning architecture;
receiving a fourth input identifying one or more constraints for the machine learning architecture;
generating code for a machine learning model based at least in part on the second input identifying the selection of the particular machine-learning-model architecture, the third input identifying the data source, and the fourth input identifying the one or more constraints; and
storing the generated code in a memory.

16. The computer-program product of claim 15 , wherein the particular machine-learning-model architecture includes a neural network.

17. The computer-program product of claim 15 , wherein the particular machine-learning-model architecture includes a classifier network.

18. The computer-program product of claim 15 , wherein the set of actions further includes:
analyzing the one or more constraints to generate a second plurality of code for the particular machine-learning-model architecture based at least in part on optimizing the one or more constraints;
generating an optimized solution; and
displaying the optimized solution.

19. The computer-program product of claim 15 , wherein the set o

Assignees

• Oracle Int Corp

Inventors

• Polleri Alberto
• Lopez Sergio
• Bron Marc Michiel
• Golding Dan David
• Ioannides Alexander
• Mestre Maria Del Rosario
• Monteiro Hugo Alexandre Pereira
• Shevelev Oleg Gennadievich
• Suzuki Larissa Cristina Dos Santos Romualdo
• Zhao Xiaoxue
• Rowe Matthew Charles

Classifications

• G06N20/20

  Ensemble learning · CPC title

• H04L51/02

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title

• G06F40/40

  Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N3/105

  Shells for specifying net layout · CPC title