Systems and methods for improved data processing of communications across computer networks using trifurcated prompts

Patent metadata

Field	Value
Publication number	US-2026099519-A1
Application number	US-202519069204-A
Country	US
Kind code	A1
Filing date	Mar 3, 2025
Priority date	Oct 4, 2024
Publication date	Apr 9, 2026
Grant date	—

Abstract

Systems and methods for improved data processing of communications across computer networks using trifurcated prompts during communication exchanges are described. For example, the system may receive a first inbound communication, wherein the first inbound communication system may determine a first context for the first inbound communication based on the first text string. The system may process the first context in a perturbation model to determining a first perturbed context, wherein the perturbation model determines the first perturbed context by determining a first alternative token for a first token in the first context. The system may determine a first prompt for a first large language model based on the first perturbed context.

First claim

What is claimed is:

1 . A system for improved data processing of communications across computer networks using trifurcated prompts during communication exchanges, the system comprising:
one or more processors; and one or more non-transitory, computer-readable mediums comprising instructions that when executed by the one or more processors cause operations comprising:
receiving, by a first server in a computer network, a first inbound communication from a second server in the computer network, wherein the first inbound communication comprises a first text string relating to a first network task, wherein the first text string comprises a plurality of tokens;
determining a first context for the first inbound communication based on the first text string;
processing the first context in a perturbation model to determine a first perturbed context, wherein the perturbation model determines the first perturbed context by determining a first alternative token for a first token in the first context;
determining a first prompt for a first large language model, wherein the first prompt comprises a first instruction, a first question, and the first perturbed context;
processing the first prompt in the first large language model to generate a first outbound communication; and
determining, based on the first outbound communication, a third server for servicing the first network task.

2 . A method for improved data processing of communications across computer networks using trifurcated prompts during communication exchanges, the method comprising:
receiving a first inbound communication, wherein the first inbound communication comprises a first text string; determining a first context for the first inbound communication based on the first text string; processing the first context in a perturbation model to determine a first perturbed context, wherein the perturbation model determines the first perturbed context by determining a first alternative token for a first token in the first context; determining a first prompt for a first large language model, wherein the first prompt comprises a first instruction, a first question, and the first perturbed context; and processing the first prompt in the first large language model to generate a first outbound communication.

3 . The method of claim 2 , wherein the perturbation model comprises a first model component trained on a known instruction, a known question, and a known context.

4 . The method of claim 2 , wherein the perturbation model comprises a second model component trained on a known instruction, a known question, and a known perturbed context.

5 . The method of claim 2 , wherein the perturbation model comprises a third model component trained on a known instruction and a known question, and wherein the third model component is trained without a known context or a known perturbed context.

6 . The method of claim 2 , wherein the perturbation model comprises a difference metric calculator component that compares, to a ground truth output, a first component output of a first model component, a second component output of a second model component, and a third component output of a third model component.

7 . The method of claim 2 , wherein the perturbation model comprises a confusion collator component that compares respective answer outputs from a first model component, a second model component, and a third model component.

8 . The method of claim 2 , wherein the perturbation model comprises a regression model that compares outputs from a confusion collator and a difference metric calculator.

9 . The method of claim 2 , wherein processing the first context in a perturbation model to determine a first perturbed context further comprises:
receiving a model identifier for the first large language model; and selecting the perturbation model from a plurality of perturbation models based on the model identifier.

10 . The method of claim 2 , wherein determining the first alternative token for the first token in the first context further comprises:
determining respective likelihoods of misidentification by the first large language model for a plurality of tokens; and determining to replace the first token with the first alternative token based on a respective likelihood of the respective likelihoods for the first token.

11 . The method of claim 2 , wherein determining the first alternative token for the first token in the first context further comprises:
determining a synonym for the first token; and determining the first alternative token based on the synonym.

12 . The method of claim 2 , wherein determining the first alternative token for the first token in the first context further comprises:
determining a class for the first token; and randomly selecting the first alternative token from the class.

13 . The method of claim 2 , wherein the first text string comprises a plurality of tokens, and wherein determining the first context for the first inbound communication based on the first text string further comprises:
determining a word phrase based on the plurality of tokens; and processing the plurality of tokens as the word phrase to determine the first context.

14 . The method of claim 2 , wherein determining the first context for the first inbound communication based on the first text string further comprises:
determining a plurality of tokens describing to the first text string; and determining the first context based on the plurality of tokens.

15 . The method of claim 2 , wherein determining the first prompt for the first large language model further comprises:
determining the first instruction based on the first inbound communication; and determining the first question based on the first inbound communication.

16 . The method of claim 2 , wherein processing the first prompt in the first large language model to generate the first outbound communication further comprises:
determining a network task based on the first inbound communication; and determining a network component for servicing the network task based on the first outbound communication.

17 . One or more non-transitory, computer-readable mediums, comprising instructions that, when executed by one or more processors, cause operations comprising:
receiving a first inbound communication, wherein the first inbound communication comprises a first text string; determining a first context for the first inbound communication based on the first text string; processing the first context in a perturbation model to determine a first perturbed context, wherein the perturbation model determines the first perturbed context by determining a first alternative token for a first token in the first context; determining a first prompt for a first large language model based on the first perturbed context; and processing the first prompt in the first large language model to generate a first outbound communication.

18 . The one or more non-transitory, computer-readable mediums of claim 17 , wherein the perturbation model comprises a first model component trained on a known instruction, a known question, and a known context, wherein the perturbation model comprises a second model component trained on the known instruction, the known question, and a known perturbed context, wherein the perturbation model comprises a third model component trained on the known instruction and the known question, and wherein the third model component is trained without the known context or the known perturbed context.

Assignees

• Capital One Services Llc

Inventors

• Zhang Chi
• Datla Vivek
• Shrivastava Aditya
• Samuel Alfy
• Kumar Anoop
• Liu Daben

Classifications

• G06F40/284

  Lexical analysis, e.g. tokenisation or collocates · CPC title

• G06F40/247

  Thesauruses; Synonyms · CPC title

• G06N5/043

  Distributed expert systems; Blackboards · CPC title

• G06F16/33295 Primary

  in dialogue systems · CPC title