Software Asset Reconciliation via Financial Data Analysis
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Solution Overview
Problem
Medium and large enterprises face challenges in managing software assets due to unclear deployment pictures, licensing issues, and overlapping software packages, leading to inefficiencies and potential legal or financial risks, as existing automated discovery tools are limited in scope and accuracy, especially for cloud-based software and mobile devices.
Innovation Solution
A system utilizing machine learning to analyze documentation and identify software packages by predicting publishers, titles, and categories from transaction records, and comparing these to managed software through a graphical user interface to highlight unmanaged or redundant packages, enabling better software utilization and licensing management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated discovery tools are used to detect installed software packages, then the scope and accuracy of software detection is improved, but the tools cannot detect all installed software packages especially cloud-based software and mobile devices
Solution Approach 1:
The patent introduces financial data (invoices, receipts, credit card statements) as an intermediary source to complement automated discovery tools. This intermediary data source enables detection of cloud-based software and mobile devices that traditional discovery mechanisms miss, thereby expanding coverage scope while maintaining detection accuracy through cross-validation.
Solution Approach 2:
The system performs multiple functions: it processes financial documents to extract software information, validates findings against automated discovery data, and reconciles licensing status. This multi-functional approach allows a single system to address diverse software deployment scenarios including cloud-based and mobile devices that traditional single-purpose tools cannot handle.
2Loss of information
If enterprises manually track and manage software packages, then complete information about deployed software can be obtained, but a great deal of effort and time is wasted
Solution Approach 1:
The system enables self-service by automatically extracting software information from financial documents using optical character recognition and machine learning algorithms. This automated self-service approach eliminates manual tracking efforts while maintaining complete information about deployed software, including cloud-based and mobile devices.
Solution Approach 2:
The system performs preliminary action by proactively analyzing financial data to identify software purchases before they are recorded in traditional asset management systems. This preliminary detection allows the system to capture software information that would otherwise be missed, reducing the need for subsequent manual investigation.
3Reliability
If traditional software asset management tools are used, then managed software can be tracked, but unmanaged packages and cloud-based software cannot be identified
Solution Approach 1:
The system dynamically adapts its detection methods based on the type of software being searched for. For cloud-based software and mobile devices, it activates financial data analysis mechanisms, while for traditional installed software, it uses automated discovery tools. This dynamic approach maintains high reliability for managed software while expanding coverage to unmanaged and cloud-based packages.
Data Source
AI summary
A database may contain representations of: (i) software packages managed by a software management tool, including publishers, titles, and categories associated with each, and (ii) a plurality of software activities, including descriptions and amounts associated with each. A server device may be configured to obtain classifications of the software activities that predict the publishers, titles, and categories of the software activities from the descriptions. The server device may further compare the software packages to the classifications in order to identify: (i) unmanaged software packages, and (ii) amounts associated with the software packages. The server device may also transmit a representation of a graphical user interface that depicts first and second panes, the first pane listing the publishers with respective total publisher amounts and whether any of the unmanaged software packages are attributable to each of the publishers, and the second pane including a chart depicting the amounts incurred over time.