# US 2025/0147992 A1 · OptiSenseGPT: Context-Aware Anomaly Detection with Natural Language Alerts and ActionableRecommendations for Distributed Fiber Optic Sensing Applications

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- Number: US 2025/0147992 A1
- Document ID: US20250147992A1
- Status: Published application
- Kind: A1
- Application number: 18/935,495
- Filed: 2024-11-02
- Published: 2025-05-08
- Earliest claimed: 2023-11-03
- Assignees: NEC Laboratories America, Inc.
- Applicants: NEC Laboratories America, Inc.
- Inventors: Yangmin DING; Ting WANG
- Web page: https://patentgrep.com/patent/US20250147992A1

## CPC

- G06F 16/3329: Natural language query formulation
- G06F 16/345: {Summarisation for human users}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Disclosed are integrated systems and methods providing intelligent anomaly detection for DFOS systems and applications, the systems and methods utilizing a natural language processing model, such as ChatGPT, to generate real-time alerts with actionable recommendations and potential consequences based on detected anomalies. Our innovative solution—OptiSenseGPT—solves problems left uncured by traditional methods by delivering easily understandable alerts in natural language, enabling timely response by relevant personnel. Our integrated OptiSenseGPT systems and methods disclosed provide context-aware recommendations and consequences, enhancing decision-making and improving overall performance and safety of a monitored infrastructure or environment. Our OptiSenseGPT systems and methods advantageously provide integration of natural language processing; context-aware recommendations; presentation of potential consequences; adaptability and customization; and seamless integration.

## Claims

### Claim 1

An intelligent anomaly detection system for distributed fiber optic sensing (DFOS) applications comprising:
the distributed fiber optic sensing system; and
a natural language processing model configured to generate real-time alerts with actionable recommendations and potential consequences based on anomalies detected in data produced by the DFOS.

### Claim 2 (depends on 1)

The system of claim 1 wherein the actionable recommendations are context aware.

### Claim 3 (depends on 2)

The system of claim 2 wherein the natural language processing model provides the actionable recommendations based on specific DFOS application and domain.

### Claim 4 (depends on 3)

The system of claim 3 wherein the natural language processing model communicates the potential consequences of detected anomalies, raises awareness in operators of potential risks while encouraging timely intervention by the operators.

### Claim 5 (depends on 4)

The system of claim 4 comprising external sensors and data sources including internet-of-things sensors and sources.

### Claim 6 (depends on 5)

The system of claim 5 wherein the actionable recommendations provided by the natural language processing model are adjustable in terms of verbosity, such that a level of detail provided in the actionable recommendations range from concise summaries to comprehensive explanations.

### Claim 7 (depends on 6)

The system of claim 6 wherein the actionable recommendations provided by the natural language processing model are modified by tone to suit different audiences or situations including formal tone for management-level notifications and casual tone for field personnel.

### Claim 8 (depends on 7)

The system of claim 7 wherein the natural language processing model is configured to provide the actionable recommendations in multiple languages.

### Claim 9 (depends on 8)

The system of claim 8 configured to incorporate multimodal data including images, audio, and video to provide additional context or information with the actionable recommendations.

### Claim 10 (depends on 9)

The system of claim 9 configured to interactively communicate between users such that users may request more information, provide feedback, or take actions directly from the natural language processing model generated actionable recommendations.