Cost allocation estimation using direct cost vectors and machine learning

US11521274B2 · kind B2 · utility

Assignee

• SAP SE · DE

Inventors

• Steffen Vollmert · Frankfurt, DE
• Luisa Karl · Neuhofen, DE
• Konrad Schenk · Potsdam, DE
• Olga Cherepanova · Malschenberg, DE
• Janet Dorothy Salmon · Dudenhofen, DE
• Ralf Ille · Karlsdorf-Neuthard, DE
• Thomas Zuerker · Brohl, DE

Key dates

• Filing date: Mar 20, 2019
• Grant date: Dec 6, 2022
• Expiry date: Nov 8, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The disclosure generally describes methods, software, and systems for estimating cost allocations, including a method for the following steps. Using a machine learning system, transactions are consolidated into estimated sender-receiver totals for costs transmitted by senders to receivers in an organization. A sender-receiver percentage matrix is determined from the estimated sender-receiver totals of a given sender and for each receiver of the transactions from the given sender. The sender-receiver percentage matrix includes, for each sender, estimated sender-receiver percentages. Current actual costs are determined for each sender to receivers for a given time period. Estimated cost allocations are determined for given time period using the sender-receiver percentage matrix. The estimated cost allocations are determined for each receiver in the organization based on a function of the current actual costs for each sender. A report that includes the estimated cost allocations is provided for presentation to a user.