Granted Patent B1

US 12,436,966 · App. 18/893,706 · Granted Oct 7, 2025

Inventors: Eric B. Hensley (San Francisco, CA); Mark C. Wolochuk (Portland, OR)

Assignee: Aravo Solutions, Inc.

G06F16/258

Abstract

Claims (61)

1. A method for generating a computing structure based on both unstructured data and structured data using a large language model (LLM), the method comprising:

receiving, using one or more computing device processors, first unstructured data from a first data source;

receiving, using the one or more computing device processors, first structured data from a second data source;

determining, using the one or more computing device processors, a first computing library associated with the first structured data;

receiving, using the one or more computing device processors, second unstructured data from a third data source;

determining, using the one or more computing device processors, a first set of computing prompts associated with the second unstructured data;

receiving, using the one or more computing device processors, second structured data, associated with a first computing format, from a fourth data source;

determining, using the one or more computing device processors, based on the first computing format, a set of computing structures associated with the second structured data;

transmitting, using the one or more computing device processors, at a first time, the first unstructured data to an LLM;

transmitting, using the one or more computing device processors, at a second time or the first time, the first computing library associated with the first structured data to the LLM;

transmitting, using the one or more computing device processors, at a third time, the second time, or the first time, the first set of computing prompts associated with the second unstructured data to the LLM;

transmitting, using the one or more computing device processors, at a fourth time, the third time, the second time, or the first time, the set of computing structures associated with the second structured data to the LLM;

receiving, using the one or more computing device processors, third structured data, associated with a second computing format, from the LLM, wherein the third structured data comprises or is based on the first set of computing prompts associated with the second unstructured data, a set of responses associated with the first set of computing prompts associated with the second unstructured data, and a computing structure, wherein the computing structure is not comprised in the set of computing structures associated with the second structured data, and wherein the computing structure comprises or is based on the first unstructured data, the first computing library associated with the first structured data, the first set of computing prompts associated with the second unstructured data, and the set of computing structures associated with the second structured data; and

transmitting, using the one or more computing device processors, the third structured data to a first system.

2. The method of claim 1 , wherein the first computing format comprises JavaScript Object Notation (JSON) format.

3. The method of claim 1 , wherein the second computing format comprises JSON format.

4. The method of claim 1 , wherein the first computing library associated with the first structured data comprises at least one of: a second set of computing prompts, a set of attributes, a set of entities, a set of workflow task types, or a set of configured objects.

5. The method of claim 1 , further comprising initiating generating, using the one or more computing device processors, a second computing library using the LLM.

6. The method of claim 5 , wherein the computing structure further comprises or is based on the second computing library.

7. The method of claim 1 , wherein the one or more computing device processors are comprised in one or more computing systems, wherein the one or more computing systems are located in one or more locations.

8. A system for generating a computing structure based on both unstructured data and structured data using a large language model (LLM), the system comprising:

one or more computing system processors; and

memory storing instructions that, when executed by the one or more computing system processors, cause the system to:

receive first unstructured data from a first data source;

receive first structured data from a second data source;

determine a computing library associated with the first structured data;

receive second unstructured data from a third data source;

determine a set of computing prompts associated with the second unstructured data;

receive second structured data, associated with a first computing format, from a fourth data source;

determine, based on the first computing format, a set of computing structures associated with the second structured data;

transmit, at a first time, the first unstructured data to an LLM;

transmit, at a second time or the first time, the computing library associated with the first structured data to the LLM;

transmit, at a third time, the second time, or the first time, the set of computing prompts associated with the second unstructured data to the LLM;

transmit, at a fourth time, the third time, the second time, or the first time, the set of computing structures associated with the second structured data to the LLM;

receive third structured data, associated with a second computing format, from the LLM, wherein the third structured data comprises or is based on the set of computing prompts associated with the second unstructured data, a set of responses associated with the set of computing prompts associated with the second unstructured data, and a computing structure, wherein the computing structure is not comprised in the set of computing structures associated with the second structured data, and wherein the computing structure is based on the first unstructured data, the computing library associated with the first structured data, the set of computing prompts associated with the second unstructured data, and the set of computing structures associated with the second structured data; and

transmit the third structured data to a first system.

9. The system of claim 8 , wherein at least one of the first unstructured data or the second unstructured data comprises raw information or information without a predetermined structure or format.

10. The system of claim 8 , wherein the LLM is hosted on a third-party server.

11. The system of claim 8 , wherein the LLM is hosted on a local server.

12. The system of claim 8 , wherein one or more of the instructions execute in a first stage and a second stage, such that fifth structured data associated with the second stage comprises or is based on fourth structured data associated with the first stage.

13. The system of claim 8 , wherein the system comprises or is comprised in one or more computing systems associated with one or more locations.

14. A method for generating a computing structure based on both unstructured data and structured data using a large language model (LLM), the method comprising:

receiving, using one or more computing device processors, first unstructured data from a first data source;

receiving, using the one or more computing device processors, first structured data from a second data source;

determining, using the one or more computing device processors, a computing library associated with the first structured data;

receiving, using the one or more computing device processors, second unstructured data from a third data source;

determining, using the one or more computing device processors, a set of computing prompts associated with the second unstructured data;

receiving, using the one or more computing device processors, second structured data, associated with a first computing format, from a fourth data source;

determining, using the one or more computing device processors, based on the first computing format, a set of computing structures associated with the second structured data;

transmitting, using the one or more computing device processors, at a first time, the first unstructured data to an LLM;

transmitting, using the one or more computing device processors, at a second time or the first time, the computing library associated with the first structured data to the LLM;

transmitting, using the one or more computing device processors, at a third time, the second time, or the first time, the set of computing prompts associated with the second unstructured data to the LLM;

transmitting, using the one or more computing device processors, at a fourth time, the third time, the second time, or the first time, the set of computing structures associated with the second structured data to the LLM;

receiving, using the one or more computing device processors, third structured data, associated with a second computing format, from the LLM, wherein the third structured data comprises or is based on the set of computing prompts associated with the second unstructured data, a set of responses associated with the set of computing prompts associated with the second unstructured data, and a computing structure, wherein the computing structure is based on at least one of: the first unstructured data, the computing library associated with the first structured data, the set of computing prompts associated with the second unstructured data, or the set of computing structures associated with the second structured data; and

transmitting, using the one or more computing device processors, the third structured data to a first system.

15. The method of claim 14 , wherein at least one of the first unstructured data or the second unstructured data comprises at least one of: text, an image, a figure, a table, audio, a video, a graph, or a diagram.

16. The method of claim 14 , wherein the first unstructured data comprises at least one of: documentation of at least one system, or documentation of at least one process.

17. The method of claim 14 , wherein the computing structure comprises a system configuration.

18. The method of claim 14 , wherein the set of computing structures associated with the second structured data comprises at least one example system configuration.

19. The method of claim 14 , wherein the set of computing prompts associated with the second unstructured data comprises at least one of: at least one requirement associated with a system configuration, or at least one capability associated with the system configuration.

20. The method of claim 14 , wherein the one or more computing device processors are comprised in one or more computing systems, wherein the one or more computing systems are located in one or more locations.

Assignments (2)

References Cited (48)