# US 12,769,787 B1 · Autonomous metadata harmonization in multi-cloud environments

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- Number: US 12,769,787 B1
- Document ID: US12769787B1
- Status: Granted
- Kind: B1
- Application number: 19/333,834
- Filed: 2025-09-19
- Granted: 2026-10-20
- Earliest claimed: 2025-09-19
- Assignees: Dell Products L.P.
- Applicants: Dell Products L.P.
- Inventors: Avinash Kumar; Zijia Wang; Srinath Kappgal
- Web page: https://patentgrep.com/patent/US12769787B1

## CPC

- G06F 16/81: Indexing, e.g. XML tags; Data structures therefor; Storage structures
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Autonomous metadata harmonization in multi-cloud environments (e.g., using a computerized tool), is enabled. For example, a corresponding process can comprise validating, using a local artificial intelligence model that was trained using federated learning applied to a group of zones of a multi-cloud architecture, a metadata attribute in a zone of the group of zones of the multi-cloud architecture, in response to determining a deviation in the metadata attribute, determining a trust score, applicable to the metadata attribute, using a defined probabilistic model, in response to the trust score being determined to satisfy a defined trust threshold, queuing a metadata update applicable to the metadata attribute, using a defined weighted priority process, determining a priority order for the metadata update among a group of metadata updates applicable to the group of zones of the multi-cloud architecture, and based on the priority order, initiating the metadata update.

## Claims

### Claim 1

A system, comprising:
at least one processor; and
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising:
validating, using a local artificial intelligence model that was trained using federated learning applied to a group of zones of a multi-cloud architecture, a metadata attribute in a zone of the group of zones of the multi-cloud architecture;
in response to determining a deviation in the metadata attribute, determining a trust score, applicable to the metadata attribute, using a defined probabilistic model;
in response to the trust score being determined to satisfy a defined trust threshold, queuing a metadata update applicable to the metadata attribute;
using a defined weighted priority process, determining a priority order for the metadata update among a group of metadata updates applicable to the group of zones of the multi-cloud architecture; and
based on the priority order, initiating the metadata update.

### Claim 2 (depends on 1)

The system of claim 1, wherein the validating of the metadata attribute comprises comparing real-time telemetry data, applicable to the metadata attribute, with a corresponding expected value.

### Claim 3 (depends on 1)

The system of claim 1, wherein the metadata attribute comprises at least one of: a central processing unit usage, a memory usage, a service level agreement breach risk, a backup versioning, or a system topology.

### Claim 4 (depends on 1)

The system of claim 1, wherein the defined probabilistic model comprises a Bayesian inference model.

### Claim 5 (depends on 1)

The system of claim 1, wherein the operations further comprise:
in response to determining a deviation in the metadata attribute, correcting local metadata in the zone of the multi-cloud architecture.

### Claim 6 (depends on 1)

The system of claim 1, wherein the defined weighted priority process is based on a latency sensitivity applicable to the metadata attribute or a workload criticality applicable to the metadata attribute.

### Claim 7 (depends on 1)

The system of claim 1, wherein the operations further comprise:
during the metadata update applicable to the group of zones of the multi-cloud architecture, inserting an additional field, applicable to the zone of the multi-cloud architecture, in corresponding metadata.

### Claim 8 (depends on 7)

The system of claim 7, wherein the additional field comprises a version-tagged field.

### Claim 9 (depends on 1)

The system of claim 1, wherein the operations further comprise:
in response to initiating the metadata update applicable to the group of zones of the multi-cloud architecture, updating a global artificial intelligence model trained using the federated learning applied to the group of zones of the multi-cloud architecture.

### Claim 10 (depends on 9)

The system of claim 9, wherein the local artificial intelligence model has been generated based on the global artificial intelligence model.

### Claim 11 (depends on 9)

The system of claim 9, wherein the global artificial intelligence model is updated using federated averaging applied to a group of local artificial intelligence models comprising the local artificial intelligence model.

### Claim 12 (depends on 1)

The system of claim 1, wherein the multi-cloud architecture comprises at least one of a public cloud, a private cloud, a hybrid cloud, or an edge cloud.

### Claim 13

A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor, facilitate performance of operations, comprising:
determining, using a local artificial intelligence model trained using federated learning applied to a group of clouds of a multi-cloud architecture, deviated metadata in a cloud of the group of clouds of the multi-cloud architecture;
in response to determining the deviated metadata, determining a trust score, applicable to the deviated metadata, using a defined probabilistic model;
in response to the trust score being determined to satisfy a defined trust threshold, queuing a first metadata update applicable to the deviated metadata;
using a defined weighted priority process, determining a priority order for a group of metadata updates applicable to the group of clouds of the multi-cloud architecture; and
based on the priority order, initiating a second metadata update applicable to the group of clouds of the multi-cloud architecture.

### Claim 14 (depends on 13)

The non-transitory machine-readable medium of claim 13, wherein the deviated metadata comprises a central processing unit allocation, applicable to the cloud, that deviates from a reported central processing unit allocation.

### Claim 15 (depends on 13)

The non-transitory machine-readable medium of claim 13, wherein the operations further comprise:
generating an output applicable to the priority order for the second metadata update applicable to the group of clouds of the multi-cloud architecture.

### Claim 16 (depends on 15)

The non-transitory machine-readable medium of claim 15, wherein the output comprises a graphical user interface indicative of the priority order for the second metadata update applicable to the group of clouds of the multi-cloud architecture.

### Claim 17

A method, comprising:
validating, by a system comprising at least one processor using a local artificial intelligence model trained using federated learning applied to a group of zones of a multi-cloud architecture, a metadata attribute in a zone of the group of zones of the multi-cloud architecture;
in response to determining a deviation in the metadata attribute, determining, by the system, a trust score, applicable to the metadata attribute, using a defined probabilistic model;
in response to the trust score being determined to satisfy a defined trust threshold, queuing, by the system, a metadata update applicable to the metadata attribute;
using a defined weighted priority process, determining, by the system, a priority order for the metadata update among a group of metadata updates applicable to the group of zones of the multi-cloud architecture; and
based on the priority order, initiating, by the system, the metadata update, applicable to the group of zones of the multi-cloud architecture.

### Claim 18 (depends on 17)

The method of claim 17, wherein the metadata attribute has been determined to be impacted by at least one of a burst input-output event, a backup failure, or metadata drift.

### Claim 19 (depends on 17)

The method of claim 17, wherein the validating the metadata attribute comprises comparing real-time telemetry data, applicable to the metadata attribute, with corresponding expected values.

### Claim 20 (depends on 17)

The method of claim 17, wherein the metadata attribute comprises at least one of: a central processing unit usage, a memory usage, a service level agreement breach risk, a backup versioning, or a system topology.