IP Library › Granted Patent US 12688190
Granted Patent B2
US 12688190 · App. 18/811,367 · Granted Jul 21, 2026

Systems and methods for generating optimized queries for disparate data sources

Inventors: Ashwin Kumar Yedte (Telangana, IN); Diane S. Cannato (Palm City, FL); Soumya Bandyopadhyay (Fairless, PA); Sunil Bhashetty (Hyderabad, IN); Sridhar Viswanathan (Hyderabad, IN)
Assignee: BANK OF AMERICA CORPORATION
G06F16/2453
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Quick Facts
Patent No.
US 12688190
App. No.
18/811,367
Granted
Jul 21, 2026
Kind
B2
Abstract

Systems, computer program products, and methods are described herein for generating optimized queries for disparate data sources. The present invention is configured to identify a query; determine, based on a parsing of the query, at least one attribute and at least one intent of the query; generate a map comprising the query, the intent, and the attribute; apply the map to a cluster of maps, wherein the cluster of maps comprises the intent associated with a secondary attribute different from the attribute, and a secondary query; generate, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of a query with the intent and the attribute, and the secondary intent with the secondary attribute and the secondary query; and determine, based on the heat map, an optimized query for the intent.

Claims (64)

1 . A system for generating optimized queries for disparate data sources, the system comprising:

a memory device with computer-readable program code stored thereon;

at least one processing device operatively coupled to the at least one memory device and at least one communication device, wherein executing the computer-readable code is configured to cause the at least one processing device to:

identify at least one query for at least one data source;

determine, based on a parsing of the at least one query, at least one attribute and at least one intent of the at least one query;

generate a map comprising the at least one query, the at least one intent, and the at least one attribute;

apply the map to a cluster of maps, wherein the cluster of maps comprises the at least one intent associated with at least one secondary attribute different from the at least one attribute, and at least one secondary query;

generate, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of the at least one query with the at least one intent and the at least one attribute, and the at least one secondary intent with the at least one secondary attribute and the at least one secondary query;

determine, based on the heat map, an optimized query for the at least one intent, wherein the optimized query is determined based on the at least one intent comprising an edge related to the optimized query in the heat map, and wherein the optimzied query in the heat map comprises an indication of a lesser computer processing consumption compared to the indication of the computer processing consumption of the at least one query; and

run, in response to determining the optimzied query, the optimized query at the at least one data source.

2 . The system of claim 1 , wherein executing the computer-readable code is further configured to cause the at least one processing device to:

update, by an artificial intelligence (AI) engine, the heat map, wherein the AI engine monitors the computer processing consumption of each query in the heat map.

3 . The system of claim 1 , wherein executing the computer-readable code is further configured to cause the at least one processing device to:

generate, based on the heat map, an intent table comprising a plurality of optimized queries with associated optimized attributes and associated optimized intents;

identify at least one new query;

parse the at least one new query and determine a new intent associated with the at least one new query; and

determine, from the intent table, an optimized query for the at least one new query based on matching the new intent to an optimized intent of the optimized query.

4 . The system of claim 3 , wherein the optimized query comprises a lesser computer processing consumption rate than the new query.

5 . The system of claim 1 , wherein the determination of the optimized query is based on an optimized computer processing consumption in the heat map, wherein the optimized computer processing consumption is based on a lowest computer processing consumption rate.

6 . The system of claim 1 , wherein the cluster of maps further comprises at least one different query, at least one different intent, and at least one different attribute, and wherein executing the computer-readable code is further configured to cause the at least one processing device to:

generate the heat map comprising the cluster of maps, wherein the heat map further comprises the at least one different query with the at least one different intent and the at least one different attribute.

7 . The system of claim 1 , wherein the computer processing consumption comprises a memory resource consumption, a power resource consumption, or a processing component consumption.

8 . The system of claim 1 , wherein the at least one intent comprises the at least one attribute for the at least one query.

9 . The system of claim 1 , wherein executing the computer-readable code is further configured to cause the at least one processing device to:

determine, based on the application of the map to the cluster of maps, a frequency of the at least one intent in the cluster of maps, wherein the frequency of the at least one intent is based on a presence of the at least one intent in the cluster of maps.

10 . The system of claim 1 , wherein executing the computer-readable code is further configured to cause the at least one processing device to:

determine, based on the application of the map to the cluster of maps, a highest frequency of intents in the cluster of maps, wherein the highest frequency of intents is based on a highest presence between the at least one intent and the at least one secondary intent; and

generate, based on the highest frequency of intents, a cache of the highest frequency of intents, wherein the cache comprises at least one intent and the at least one attribute or the at least one secondary intent and the at least one secondary attribute.

11 . A computer program product for generating optimized queries for disparate data sources, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:

identify at least one query;

determine, based on a parsing of the at least one query, at least one attribute and at least one intent of the at least one query;

generate a map comprising the at least one query, the at least one intent, and the at least one attribute;

apply the map to a cluster of maps, wherein the cluster of maps comprises the at least one intent associated with at least one secondary attribute different from the at least one attribute, and at least one secondary query;

generate, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of the at least one query with the at least one intent and the at least one attribute, and the at least one secondary intent with the at least one secondary attribute and the at least one secondary query;

determine, based on the heat map, an optimized query for the at least one intent, wherein the optimized query is determined based on the at least one intent comprising an edge related to the optimized query in the heat map, and wherein the optimzied query in the heat map comprises an indication of a lesser computer processing consumption compared to the indication of the computer processing consumption of the at least one query; and

run, in response to determining the optimzied query, the optimized query at the at least one data source.

12 . The computer program product of claim 11 , wherein the computer program product further comprises non-transitory computer-readable medium comprising code causing the apparatus to:

update, by an artificial intelligence (AI) engine, the heat map, wherein the AI engine monitors the computer processing consumption of each query in the heat map.

13 . The computer program product of claim 11 , wherein the computer program product further comprises non-transitory computer-readable medium comprising code causing the apparatus to:

generate, based on the heat map, an intent table comprising a plurality of optimized queries with associated optimized attributes and associated optimized intents;

identify at least one new query;

parse the at least one new query and determine a new intent associated with the at least one new query; and

determine, from the intent table, an optimized query for the at least one new query based on matching the new intent to an optimized intent of the optimized query.

14 . The computer program product of claim 11 , wherein the determination of the optimized query is based on an optimized computer processing consumption in the heat map, wherein the optimized consumption processing component is based on a lowest consumption processing component rate.

15 . The computer program product of claim 11 , wherein the computer program product further comprises non-transitory computer-readable medium comprising code causing the apparatus to:

determine, based on the application of the map to the cluster of maps, a frequency of the at least one intent in the cluster of maps, wherein the frequency of the at least one intent is based on a presence of the at least one intent in the cluster of maps.

16 . A computer implemented method for generating optimized queries for disparate data sources, the computer implemented method comprising:

identifying at least one query;

determining, based on a parsing of the at least one query, at least one attribute and at least one intent of the at least one query;

generating a map comprising the at least one query, the at least one intent, and the at least one attribute;

applying the map to a cluster of maps, wherein the cluster of maps comprises the at least one intent associated with at least one secondary attribute different from the at least one attribute, and at least one secondary query;

generating, based on the cluster of maps, a heat map of the cluster of maps, wherein the heat map comprises an indication of a computer processing consumption of the at least one query with the at least one intent and the at least one attribute, and the at least one secondary intent with the at least one secondary attribute and the at least one secondary query;

determining, based on the heat map, an optimized query for the at least one intent, wherein the optimized query is determined based on the at least one intent comprising an edge related to the optimized query in the heat map, and wherein the optimzied query in the heat map comprises an indication of a lesser computer processing consumption compared to the indication of the computer processing consumption of the at least one query; and

run, in response to determining the optimzied query, the optimized query at the at least one data source.

17 . The computer implemented method of claim 16 , further comprising

updating, by an artificial intelligence (AI) engine, the heat map, wherein the AI engine monitors the computer processing consumption of each query in the heat map.

18 . The computer implemented method of claim 16 , further comprising:

generating, based on the heat map, an intent table comprising a plurality of optimized queries with associated optimized attributes and associated optimized intents;

identifying at least one new query;

parsing the at least one new query and determine a new intent associated with the at least one new query; and

determining, from the intent table, an optimized query for the at least one new query based on matching the new intent to an optimized intent of the optimized query.

19 . The computer implemented method of claim 16 , wherein the determination of the optimized query is based on an optimized computer processing consumption in the heat map, wherein the optimized consumption processing component is based on a lowest computer processing consumption rate.

20 . The computer implemented method of claim 16 , further comprising:

determining, based on the application of the map to the cluster of maps, a frequency of the at least one intent in the cluster of maps, wherein the frequency of the at least one intent is based on a presence of the at least one intent in the cluster of maps.