Granted Patent B2

US 12,619,876 · App. 19/056,627 · Granted May 5, 2026

Inventors: Anshuman Kanwar (Cambridge, MA); Robin Sylvester (San Francisco, CA)

Assignee: Reltio, Inc.

G06N3/08

Abstract

Claims (95)

1 . A system comprising:

one or more processors; and

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

receiving a plurality of different machine learning models;

testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;

selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;

receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;

identifying a plurality of first attributes in a data model;

identifying a plurality of second attributes in the data model;

creating a first string based on the plurality of first attributes in the data model;

creating a second string based on the plurality of second attributes in the data model;

generating a first prompt based on the first string;

generating a second prompt based on the second string;

providing the first prompt to the domain-agnostic large language model;

generating, by the domain-agnostic large language model using the first prompt, a first domain-agnostic large language model result;

clipping the first domain-agnostic large language model result;

providing the second prompt to the domain-agnostic large language model;

generating, by the domain-agnostic large language model using the second prompt, a second domain-agnostic large language model result;

clipping the second domain-agnostic large language model result;

generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second domain-agnostic large language model results;

merging, based on the machine learning final result, the first entity and the second entity.

2 . The system of claim 1 , wherein the plurality of different machine learning models are received from a user.

3 . The system of claim 1 , wherein the instructions, when executed by the one or more processors, cause the system to perform tuning the domain-agnostic large language model based on user feedback received through a graphical user interface.

4 . A method comprising:

receiving a plurality of different machine learning models;

testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;

selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;

receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;

identifying a plurality of first attributes in a data model;

identifying a plurality of second attributes in the data model;

creating a first string based on the plurality of first attributes in the data model;

creating a second string based on the plurality of second attributes in the data model;

generating a first prompt based on the first string;

generating a second prompt based on the second string;

providing the first prompt to the domain-agnostic large language model;

generating, by the domain-agnostic large language model using the first prompt, a first domain-agnostic large language model result;

clipping the first domain-agnostic large language model result;

providing the second prompt to the domain-agnostic large language model;

generating, by the domain-agnostic large language model using the second prompt, a second domain-agnostic large language model result;

clipping the second domain-agnostic large language model result;

generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second domain-agnostic large language model results;

merging, based on the machine learning final result, the first entity and the second entity.

5 . The method of claim 4 , wherein the plurality of different machine learning models are received from a user.

6 . A system comprising:

one or more processors; and

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

receiving a plurality of different machine learning models;

testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;

selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;

swapping the domain-agnostic large language model with an approximator network, wherein the approximator network approximates outputs of the domain-agnostic large language model;

receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;

identifying a plurality of first attributes in a data model;

identifying a plurality of second attributes in the data model;

creating a first string based on the plurality of first attributes in the data model;

creating a second string based on the plurality of second attributes in the data model;

generating a first prompt based on the first string;

generating a second prompt based on the second string;

providing the first prompt to the approximator network;

generating, by the approximator network using the first prompt, a first approximator network result;

clipping the first approximator network result;

providing the second prompt to another approximator network;

generating, by the other approximator network using the second prompt, a second approximator network result;

clipping the second approximator network result;

generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second approximator network results;

merging, based on the machine learning final result, the first entity and the second entity.

7 . The system of claim 6 , wherein the plurality of different machine learning models are received from a user.

8 . The system of claim 6 , wherein the swapping is performed pre-runtime.

9 . The system of claim 6 , wherein the swapping is performed during runtime.

10 . The system of claim 6 , wherein the swapping is performed post-runtime.

11 . The system of claim 6 , wherein the approximator network is smaller than the domain-agnostic large language model.

12 . A method comprising:

receiving a plurality of different machine learning models;

testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;

selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;

swapping the domain-agnostic large language model with an approximator network, wherein the approximator network approximates outputs of the domain-agnostic large language model;

receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;

identifying a plurality of first attributes in a data model;

identifying a plurality of second attributes in the data model;

creating a first string based on the plurality of first attributes in the data model;

creating a second string based on the plurality of second attributes in the data model;

generating a first prompt based on the first string;

generating a second prompt based on the second string;

providing the first prompt to the approximator network;

generating, by the approximator network using the first prompt, a first approximator network result;

clipping the first approximator network result;

providing the second prompt to another approximator network;

generating, by the other approximator network using the second prompt, a second approximator network result;

clipping the second approximator network result;

generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second approximator network results;

merging, based on the machine learning final result, the first entity and the second entity.

13 . The method of claim 12 , wherein the plurality of different machine learning models are received from a user.

14 . The method of claim 12 , wherein the swapping is performed pre-runtime.

15 . The method of claim 12 , wherein the swapping is performed during runtime.

16 . The method of claim 12 , wherein the swapping is performed post-runtime.

17 . The method of claim 12 , wherein the approximator network is smaller than the domain-agnostic large language model.

Assignments (1)

Continuity (3)

References Cited (16)