# US 2025/0165480 A1 · Systems and methods for retrieval augmented generation

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- Number: US 2025/0165480 A1
- Document ID: US20250165480A1
- Status: Published application
- Kind: A1
- Application number: 18/954,236
- Filed: 2024-11-20
- Published: 2025-05-22
- Earliest claimed: 2023-11-20
- Assignees: Elsevier, Inc.
- Applicants: Elsevier Inc.
- Inventors: Adrian Raudaschl; Erik Schwartz; Henry Cleland; Kehan Zhang
- Web page: https://patentgrep.com/patent/US20250165480A1

## CPC

- G06F 16/24578: {using ranking}
- G06F 16/243: {Natural language query formulation}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Systems and methods herein provide a processor; and a non-transitory, processor readable storage medium communicatively coupled to the processor. The non-transitory, processor readable storage medium may include one or more instructions stored thereon that, when executed, cause the processor to: input one or more queries into a large language model; generate, based on the one or more queries, a plurality of natural language queries, wherein each of the plurality of natural language queries are distinct queries and associated with the one or more queries; perform vector searches for the one or more queries and plurality of natural language queries; compile the plurality of natural language queries into a search result based on the vector searches; and generate a summary based on the search result.

## Claims

### Claim 1

A system, comprising:
a processor; and
a non-transitory, processor readable storage medium communicatively coupled to the processor, the non-transitory, processor readable storage medium comprising one or more instructions stored thereon that, when executed, cause the processor to:
input one or more queries into a large language model;
generate, based on the one or more queries, a plurality of natural language queries, wherein each of the plurality of natural language queries are distinct queries and associated with the one or more queries;
perform vector searches for the one or more queries and plurality of natural language queries;
compile the plurality of natural language queries into a search result based on the vector searches; and
generate a summary based on the search result.

### Claim 2 (depends on 1)

The system of claim 1, wherein the one or more instructions further cause the processor to instruct the large language model to operate as an interactive artificial intelligent chat assistant.

### Claim 3 (depends on 1)

The system of claim 1, wherein the one or more instructions further cause the processor to:
retrieve rankings via one or more respective retrieval systems; and
re-rank each of the one or more retrieved rankings.

### Claim 4 (depends on 3)

The system of claim 3, wherein the one or more instructions further cause the processor to fuse each of the one or more re-ranked retrieved rankings.

### Claim 5 (depends on 4)

The system of claim 4, wherein the one or more instructions further cause the processor to sort the one or more fused rankings by sum to generate a unified ranking.

### Claim 6 (depends on 1)

The system of claim 1, wherein the one or more instructions further cause the processor to calculate a new score for each document based on a respective rank in one or more lists.

### Claim 7 (depends on 6)

The system of claim 6, wherein the one or more instructions further cause the processor to:
sort each document with a respective new score to create a re-ranked list; and
output each document in a predetermined order.

### Claim 8

A method, comprising:
inputting one or more queries into a large language model;
generating, based on the one or more queries, a plurality of natural language queries, wherein each of the plurality of natural language queries are distinct queries and associated with the one or more queries;
performing vector searches for the one or more queries and plurality of natural language queries;
compiling the plurality of natural language queries into a search result based on the vector searches; and
generating a summary based on the search result.

### Claim 9 (depends on 8)

The method of claim 8, further comprising instructing the large language model to operate as an interactive artificial intelligent chat assistant.

### Claim 10 (depends on 8)

The method of claim 8, further comprising:
retrieving rankings via one or more respective retrieval systems; and
re-ranking each of the one or more retrieved rankings.

### Claim 11 (depends on 10)

The method of claim 10, further comprising fusing each of the one or more re-ranked retrieved rankings.

### Claim 12 (depends on 11)

The method of claim 11, further comprising sorting the one or more fused rankings by sum to generate a unified ranking.

### Claim 13 (depends on 8)

The method of claim 8, further comprising calculating a new score for each document based on a respective rank in one or more lists.

### Claim 14 (depends on 8)

The method of claim 8, further comprising:
sorting each document with a respective new score to create a re-ranked list; and
outputting each document in a predetermined order.

### Claim 15

A non-transitory, computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform one or more operations comprising:
inputting one or more queries into a large language model;
generating, based on the one or more queries, a plurality of natural language queries, wherein each of the plurality of natural language queries are distinct queries and associated with the one or more queries;
performing vector searches for the one or more queries and plurality of natural language queries;
compiling the plurality of natural language queries into a search result based on the vector searches; and
generating a summary based on the search result.

### Claim 16 (depends on 15)

The non-transitory, computer-readable medium of claim 15, comprising instructions that further cause the at least one processor to instruct the large language model to operate as an interactive artificial intelligent chat assistant.

### Claim 17 (depends on 15)

The non-transitory, computer-readable medium of claim 15, comprising instructions that further cause the at least one processor to:
retrieve rankings via one or more respective retrieval systems; and
re-rank each of the one or more retrieved rankings.

### Claim 18 (depends on 17)

The non-transitory, computer-readable medium of claim 17, comprising instructions that further cause the at least one processor to fuse each of the one or more re-ranked retrieved rankings.

### Claim 19 (depends on 15)

The non-transitory, computer-readable medium of claim 15, comprising instructions that further cause the at least one processor to sort the one or more fused rankings by sum to generate a unified ranking.

### Claim 20 (depends on 15)

The non-transitory, computer-readable medium of claim 15, comprising instructions that further cause the at least one processor to:
calculate a new score for each document based on a respective rank in one or more lists;
sort each document with a respective new score to create a re-ranked list; and
output each document in a predetermined order.