Data Ingestion and Content Explorer (DICE)

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2024-07 (month precision)Source: Jul-24

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Health and Medical

Operational functions

Document processing and extraction, Search and knowledge retrieval Derived by keyword rules; see methodology

Problem the AI is intended to solve

Multiple stakeholders across HFP have the business need to search for content within artifacts and documents uploaded to various systems. For systems like CARA and CARTS, document search capabilities are limited due to the Appian technology stack utilized by these systems. As the Human Food Data Platform continues to grow, it will also need to provide SMEs with the capability to search within the data platform. Offices need a quicker way to search for content within documents and databases to find relevant data across a multitude of use cases including regulatory and compliance reviews, outbreak response investigation, and research tracking and administration. Additionally, multiple HFP offices have business processes requiring extracting structured data from unstructured documents for data analysis, regulatory reviews, and other business intelligence insights which are currently supported through manual operations. DICE will enable SMEs to obtain properly formatted structured data from unstructured data sources.

Expected benefits

Accelerates the time for subject matter experts (SMEs) to find relevant data and content lost within images, hand-written documents, emails, and other artifacts and provides this in in a one-stop-shop user experience. Allows users to search through millions of documents quickly and makes data accessible to everyone in the HFP and not just those who have backend access. Extracting text from these artifacts makes it available for further analysis and natural language processing. Data can be further processed to detect sentiment, entities, key phrases, syntax, and topics. AWS and API based architecture brings a flexible and scalable framework to HFP to facilitate search use cases while enabling a cost-effective solution. Shared infrastructure for unstructured and structured intelligent search capabilities minimizes cost across CFSAN offices who have this same need.

System outputs

The system includes a user interface that allows users to view returned search results, templatize unstructured documents using the intelligent document processing workflow, and view extracted text with confidence scores from unstructured documents.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Trigent Solutions Inc, Digitrix LLC

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

No

Privacy Impact Assessment

Not reported

Authorization to Operate

No

Demographic features

k) None of the above

Training and evaluation data

CARTS system data, CARA system data.

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.

Potential impacts and how they were identified

Not reported