NASA · NASA-327 · record R0201

Lessons Learned Bot (LLB)

National Aeronautics and Space Administration · LaRC: Langley Research Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

In near real-time, the Lessons Learned Bot, or LLB, brings lessons learned (LL) documents to users through a Microsoft Excel add-in application locally installed to search for LL content relevant to the text within the selected Excel cell. The application will encompass a corpus of documents, a trained Machine Learning (ML) model, built-in ML tools to train user’s documents, and an easy-to-use user interface to allow for the streamlined discovery of LL content.

Expected benefits

Today, NASA’s LL are online and searchable via keywords. Nevertheless, users often face a challenge to find lessons relevant to their issues. Applying the advancement in Natural Language Processing (NLP) ML algorithm, the LLB can find and rank LL records relevant to text in the user’s selected Excel cells, containing just a few words or entire paragraphs of text. Results are displayed to the user in their existing Excel workflow.

System outputs

The LLB’s installation package comes with a pre-trained NASA LL dataset and a NASA Scientific and Technical Information (STI) dataset, as well as on-demand training tools allowing the user to apply the LLB search algorithm to their own discipline specific datasets. Additionally, we also have an API version of this software that can be called from any application within the Agency firewall.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported