IP Library Granted Patent US 12,598,111
Granted Patent B2
US 12,598,111 · App. 18/676,653 · Granted Apr 7, 2026

Enabling intent-based network management with generative ai and digital twins

Inventors: Xuan Tuyen Tran (Bridgewater, NJ); Ajay Rajkumar (Morristown, NJ); Yuxuan Jiang (Piscataway, NJ)
Assignee: AT&T Intellectual Property I, L.P.
H04L41/16H04L41/145H04W28/0925
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Quick Facts
Patent No.
US 12,598,111
App. No.
18/676,653
Granted
Apr 7, 2026
Kind
B2
Abstract

Aspects of the subject disclosure may include, for example, an intent-based network (IBN) management system that effects changes in a communication network based on high-level intents. High-level intents are translated into operator-level intents by a large language model (LLM). Conflicts between operator-level intents are resolved, and the operator-level intents are mapped to intent functions. The intent functions are then mapped to policies that may effect changes in the network in accordance with the high-level intents. Other embodiments are disclosed.

Claims (37)

1 . A device, comprising:

a processing system including a processor; and

a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:

receiving network operator-level intents from a large language model that translates high level intents into the network operator-level intents, wherein the network operator-level intents are specific to network operators and comprise a first operator-level intent associated with a first network operator having a first number of radio access network (RAN) nodes and a second operator-level intent associated with a second network operator having a second number of RAN nodes;

interpreting the network operator-level intents, extracting intent functions, and mapping the network operator-level intents into the intent functions, wherein the intent functions comprise a function to subscribe to measurements, a function to update cell configurations, and a function to apply control policies in the RAN;

performing a function call to the mapped intent functions;

performing two-level conflict resolutions comprising performing network operator-level intent conflict resolution on the network operator-level intents and performing application level intent conflict resolution, wherein the network operator-level intent conflict resolution is performed by identifying a network operator-level conflict configured to have overlapping requirements or mutually exclusive configuration between the first operator-level intent and the second operator-level intent, and wherein the application level intent conflict resolution is performed regarding an application level conflict that affect a same key performance indicator (KPI); and

applying the intent functions to modify RAN policies to effect RAN changes in accordance with the high level intents.

2 . The device of claim 1 , wherein the operations further comprise application-level conflict resolution of the intent functions.

3 . The device of claim 1 , wherein the mapping the network operator-level intents into the intent functions comprises accessing the intent functions via application programming interface (API) calls.

4 . The device of claim 1 , wherein the performing network operator-level intent conflict resolution is in accordance with requirements provided by a policy auditor.

5 . The device of claim 1 , wherein the intent functions comprise updating cell configurations.

6 . The device of claim 1 , wherein the applying the intent functions comprises applying the intent functions to a digital twin of a physical RAN.

7 . The device of claim 6 , wherein the operations further comprise applying the intent functions to the physical RAN.

8 . The device of claim 1 , wherein the high level intents specify an expected increase in network users.

9 . The device of claim 8 , wherein the network operator-level intents specify an increased bandwidth requirement to support the expected increase in network users.

10 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:

receiving network operator-level intents from a large language model that translates high level intents into the network operator-level intents, wherein the network operator-level intents are specific to network operators and comprise a first operator-level intent associated with a first network operator having a first number of radio access network (RAN) nodes and a second operator-level intent associated with a second network operator having a second number of RAN nodes;

interpreting the network operator-level intents, extracting intent functions, and mapping the network operator-level intents into the intent functions, wherein the intent functions comprise a function to subscribe to measurements, a function to update cell configurations, and a function to apply control policies in the RAN;

performing a function call to the mapped intent functions;

performing two-level conflict resolutions comprising performing network operator-level intent conflict resolution on the network operator-level intents and performing application level intent conflict resolution, wherein the network operator-level intent conflict resolution is performed by identifying a network operator-level conflict configured to have overlapping requirements or mutually exclusive configuration between the first operator-level intent and the second operator-level intent, and wherein the application level intent conflict resolution is performed regarding an application level conflict that affect a same key performance indicator (KPI); and

applying the intent functions to modify RAN policies to effect RAN changes in accordance with the high level intents.

11 . The non-transitory machine-readable medium of claim 10 , wherein the operations further comprise application-level conflict resolution of the intent functions.

12 . The non-transitory machine-readable medium of claim 10 , wherein the mapping the network operator-level intents into the intent functions comprises accessing the intent functions via application programming interface (API) calls.

13 . The non-transitory machine-readable medium of claim 10 , wherein the performing network operator-level intent conflict resolution is in accordance with requirements provided by a policy auditor.

14 . The non-transitory machine-readable medium of claim 10 , wherein the intent functions comprise updating cell configurations.

15 . The non-transitory machine-readable medium of claim 10 , wherein the applying the intent functions comprises applying the intent functions to a digital twin of a physical RAN.

16 . The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise applying the intent functions to the physical RAN.

17 . A method, comprising:

receiving, by a processing system including a processor, network operator-level intents from a large language model that translates high level intents into the network operator-level intents, wherein the network operator-level intents are specific to network operators and comprise a first operator-level intent associated with a first network operator having a first number of radio access network (RAN) nodes and a second operator-level intent associated with a second network operator having a second number of RAN nodes;

interpreting, by the processing system, the network operator-level intents, extracting intent functions, and mapping, by the processing system, the network operator-level intents into the intent functions, wherein the intent functions comprise a function to subscribe to measurements, a function to update cell configurations, and a function to apply control policies in the RAN;

performing, by the processing system, a function call to the mapped intent functions;

performing, by the processing system, two-level conflict resolutions comprising performing network operator-level intent conflict resolution on the network operator-level intents and performing application level intent conflict resolution, wherein the network operator-level intent conflict resolution is performed by identifying a network operator-level conflict configured to have overlapping requirements or mutually exclusive configuration between the first operator-level intent and the second operator-level intent, and wherein the application level intent conflict resolution is performed regarding an application level conflict that affect a same key performance indicator (KPI); and

applying, by the processing system, the intent functions to modify RAN policies to effect RAN changes in accordance with the high level intents.

18 . The method of claim 17 , wherein the applying the intent functions comprises applying the intent functions to a digital twin of a physical RAN.

19 . The method of claim 18 , wherein the operations further comprise applying, by the processing system, the intent functions to the physical RAN.

20 . The method of claim 18 , wherein the operations further comprise iterating changes to the RAN policies in the digital twin before applying the changes to the physical RAN.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 12, 2024
From: TRAN, XUAN TUYEN; RAJKUMAR, AJAY; JIANG, YUXUAN
To: AT&T INTELLECTUAL PROPERTY I, L.P.
Reel/Frame 067700/0060 →
Continuity (1)
Related Publication 20250373503A1 · Dec 4, 2025
References Cited (38)
US 10200248B1 · Jiang · 2019 [cited by examiner]
US 10439875B2 · Mohanram · 2019 [cited by examiner]
US 10693738B2 · Nagarajan · 2020 [cited by examiner]
US 11086709B1 · Ratkovic · 2021 [cited by examiner]
US 11283691B1 · A · 2022 [cited by examiner]
US 11677789B2 · Rungta · 2023 [cited by examiner]
US 11799737B1 · Ottamalika · 2023 [cited by examiner]
US 11909592B2 · Wang · 2024 [cited by examiner]
US 11909600B2 · Li · 2024 [cited by examiner]
US 11929886B2 · A · 2024 [cited by examiner]
US 12197852B1 · Kushnikov · 2025 [cited by examiner]
US 12212434B2 · Pfister · 2025 [cited by examiner]
US 12259927B1 · Chan · 2025 [cited by examiner]
US 12309043B2 · Xu · 2025 [cited by examiner]
US 12341658B2 · Brown · 2025 [cited by examiner]
US 20160218933A1 · Porras · 2016 [cited by examiner]
US 20180278480A1 · Prasad · 2018 [cited by examiner]
US 20200351167A1 · Sharma · 2020 [cited by examiner]
US 20220045932A1 · Wang · 2022 [cited by examiner]
US 20220311671A1 · Jamkhedkar · 2022 [cited by examiner]
US 20220321408A1 · Mahimkar · 2022 [cited by examiner]
US 20220342649A1 · Cao · 2022 [cited by examiner]
US 20220393953A1 · A · 2022 [cited by examiner]
US 20230254221A1 · A · 2023 [cited by examiner]
US 20240097983A1 · A · 2024 [cited by examiner]
US 20240176709A1 · A · 2024 [cited by examiner]
US 20240177710A1 · Kumar Saha · 2024 [cited by examiner]
US 20240251293A1 · Jeong · 2024 [cited by examiner]
US 20240267794A1 · Shakkottai · 2024 [cited by examiner]
US 20240291716A1 · Kumar · 2024 [cited by examiner]
US 20240364593A1 · Gomes Da Silva · 2024 [cited by examiner]
US 20240381232A1 · Kovács · 2024 [cited by examiner]
US 20240388501A1 · Chen · 2024 [cited by examiner]
US 20250088946A1 · Grida Ben Yahya · 2025 [cited by examiner]
CN 115623499A · 2023 [cited by examiner]
EP 3716532A1 · 2020 [cited by examiner]
EP 4020921A1 · 2022 [cited by examiner]
LLM-Based Policy Generation for Intent-Based Management of Applications (Year: 2023). [cited by examiner]