US 12,136,098 B2
Adaptively enhancing procurement data
Micky G. Keck, Cincinnati, OH (US); Jeffrey T. Crowder, Morrow, OH (US); Sundaresan R. Kadayam, Cincinnati, OH (US); John M. Kitson, Cincinnati, OH (US); and Mark W. Reed, Fort Thomas, KY (US)
Assigned to Coupa Software Incorporated, San Mateo, CA (US)
Filed by Coupa Software Incorporated, San Mateo, CA (US)
Filed on Jul. 16, 2021, as Appl. No. 17/377,817.
Application 17/377,817 is a continuation of application No. 16/408,380, filed on May 9, 2019, abandoned.
Claims priority of provisional application 62/670,470, filed on May 11, 2018.
Prior Publication US 2021/0342920 A1, Nov. 4, 2021
Int. Cl. G06Q 30/0201 (2023.01); G06F 16/2457 (2019.01); G06F 16/9535 (2019.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0201 (2013.01) [G06F 16/24578 (2019.01); G06F 16/9535 (2019.01); G06Q 30/0633 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
obtaining, via upload to a computer system or from live data sources, a plurality of unstructured purchase data having as mandatory fields only line item title, date, and spend amount, the plurality of unstructured purchase data being related to purchases of a plurality of items in a plurality of commodity groups and offered by a plurality of suppliers;
for each purchase line item represented in the plurality of unstructured purchase data, the computer system executing: determining a plurality of field-level data quality scores for a plurality of data fields in each purchase line item; weighting the plurality of field-level data quality scores; determining a row-level quality score based on a sum of the weighted field-level data quality scores; based on the row-level quality score, enriching the each purchase line item in the plurality of unstructured purchase data based on product details and attributes obtained from an item master database, to form an enriched purchase dataset;
processing the enriched purchase dataset using a hierarchical classifier using additional semantic data elements to output a series of natural spend clusters corresponding to product categories represented in the enriched purchase dataset;
displaying on a graphical user interface of a computer display device each of the natural spend clusters in a treemap visualization in which each of the natural spend clusters is a first rectangle corresponding to a product category and a plurality of second rectangles corresponding to subcategories of the product category, each of the second rectangles having a size corresponding to an aggregated spend amount of individual purchases of a corresponding subcategory or a number of items purchased;
in response to a first user input via a control device to select a particular cluster in the treemap visualization and to drag the particular cluster to another cluster, combining the particular cluster and another cluster, and automatically morphing the treemap visualization to reflect the combining.