AI Powered Auto-curation

This article describes the AI Powered Auto-curation feature that improves software inventory results. Using AI Powered Auto-curation, you'll be able to increase the total amount of Normalization software in your Inventory repositories. This is done using AI that integrates with the inventory consolidation process. By using AI, your organization will benefit from significant numbers of normalized software and a reduced manual effort needed to normalize software.

Inventory Insights and AI Powered Auto-curation

The Inventory Insights app captures data from different sources, then consolidates, normalizes, and stores it in Inventory repositories. It is also used to view, export, and manage associations.

Inventory Insights uses connectors to capture software, hardware, and other data from sources such as 1E, Configuration Manager, Big Fix, and vCenter where it is consolidated, de-duplicated, and normalized. Normalization uses software and hardware entries in Catalog.

An empty repository called Default Inventory is provided out-of-the-box. You can optionally create additional inventory repositories to partition data by source, time period, or another purpose. To populate a repository, you must use one or more inventory connectors, and either manually sync the data or schedule a sync. For more detail, refer to Repositories page - 1E Server and Schedules page in Settings.

In old versions normalization of software product names was handled by 1E Catalog. This used rules that map source Programs and Features data into an accurate standardized form of Vendor, Title, Version, and Edition (VTVE) fields. These normalized records could then be referred to with the same signature. Although the rules in the 1E Catalog cover the most common software, there is always new software that needs to be manually mapped.

AI Powered Auto-curation normalizes software product names that are not in the 1E Catalog. It first extracts the VTVE from varied raw Programs and Features data by removing excess data and extracting the relevant strings. The AI then standardizes the VTVE fields to a single, accurate form of representation. It also produces a numeric value called the confidence score, this score:

• Represents how confident the AI is in its prediction

• Is set to a high threshold through testing and validation across a large data set

• Can be calibrated according to the variety of software data at a customer site to optimize the normalization rate

• Is controlled by 1E through collaboration with customers

AI Powered Auto-curation uses model files that govern its behavior and, when the feature is enabled, the versions of the files are compared with the versions in the 1E Cloud, and downloaded whenever a new version is made available.