# US 2026/0099338 A1 · Large language model deployment

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- Number: US 2026/0099338 A1
- Document ID: US20260099338A1
- Status: Published application
- Kind: A1
- Application number: 18/905,979
- Filed: 2024-10-03
- Published: 2026-04-09
- Earliest claimed: 2024-10-03
- Applicants: Wells Fargo Bank, N.A.
- Inventors: Krishnakumar Chellappa; Shamsher Dhaka
- Web page: https://patentgrep.com/patent/US20260099338A1

## CPC

- G06F 9/44505: {Configuring for program initiating, e.g. using registry, configuration files}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

An example computer system for deploying one or more large language models, the computer system comprising: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: manage deployment of one or more machine learning models; generate model configuration files, wherein the model configuration files implement the one or more machine learning models in one or more environments and provide a specification library used to configure the one or more machine learning models; determine scores of a performance of the one or more machine learning models in the one or more environments; and store the model configuration files that are used to deploy each corresponding machine learning model.

## Claims

### Claim 1

A computer system for deploying one or more large language models, the computer system comprising:
one or more processors; and
non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to:
manage deployment of one or more machine learning models;
generate model configuration files, wherein the model configuration files implement the one or more machine learning models in one or more environments and provide a specification library used to configure the one or more machine learning models;
determine scores of a performance of the one or more machine learning models in the one or more environments; and
store the model configuration files that are used to deploy each corresponding machine learning model.

### Claim 2 (depends on 1)

The computer system of claim 1, wherein the instructions further cause the computer system to maintain the one or more machine learning model on-premises or in a cloud instance.

### Claim 3 (depends on 2)

The computer system of claim 2, wherein the one or more machine learning models are accessed through a RESTful endpoint.

### Claim 4 (depends on 3)

The computer system of claim 3, wherein an external device accesses the one or more machine learning models through the RESTful endpoint.

### Claim 5 (depends on 1)

The computer system of claim 1, wherein the instructions further cause the computer system to monitor real-time scoring data of the one or more machine learning models.

### Claim 6 (depends on 5)

The computer system of claim 5, wherein the event streaming device stores the scoring data in a database.

### Claim 7 (depends on 6)

The computer system of claim 6, wherein the database is a relational database or a non-relational database.

### Claim 8 (depends on 1)

The computer system of claim 1, wherein the instructions further cause the computer system to update a model configuration file to update the one or more machine learning models.

### Claim 9 (depends on 8)

The computer system of claim 8, wherein updating the one or more machine learning models are completed without updating underlying code of the one or more machine learning models.

### Claim 10 (depends on 9)

The computer system of claim 9, wherein updating the model configuration file is further programmed to generate a new model configuration file based on a machine learning model template.

### Claim 11

A method for deploying one or more machine learning models, the method comprising:
developing a machine learning model for one or more use cases;
operationalizing the machine learning model for the one or more use cases;
deploying the machine learning model to one or more client devices; and
operating the machine learning model on the one or more client devices.

### Claim 12 (depends on 11)

The method of claim 11, further comprising generating one or more model configuration files corresponding to the one or more machine learning models.

### Claim 13 (depends on 12)

The method of claim 12, wherein the machine learning model is developed from the one or more configuration files.

### Claim 14 (depends on 13)

The method of claim 13, wherein each of the one or more configuration files correspond to at least one of the one or more-use cases.

### Claim 15 (depends on 11)

The method of claim 11, further comprising scoring the one or more machine learning models for the one or more use cases.

### Claim 16 (depends on 15)

The method of claim 15, further comprising storing scoring data in a database.

### Claim 17 (depends on 16)

The method of claim 16, wherein the database is a relational database or a non-relational database.

### Claim 18 (depends on 11)

The method of claim 11, further comprising determining an instance type for deploying the machine learning model.

### Claim 19 (depends on 18)

The method of claim 18, wherein the instance type is on-premises or a cloud instance.

### Claim 20 (depends on 18)

The method of claim 18, further comprising accessing the machine learning model through a Representational State Transfer (“RESTful”) API.