Abstract: A legal spend management solution is described herein, a solution that utilizes improved machine learning algorithms to match lines of a legal invoice to lines in a receipt from a set of receipts. The matching uses cosine similarity algorithms and Levenshtein distances to determine whether there is a match between the receipt and the invoice lines. The machine learning results are displayed using a novel set of icons that present the confidence score with a set of three squares below a document icon.

Abstract: A unique user interface for improving legal (and other fields) spend management is described herein. The algorithm may include looping through the lines of an invoice and displaying an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A unique animated graphical user interface for improving the tracking of documents is described herein. The user interface comprises of symbols that move across a line to represent the status of that document. The line may be separated into quadrants. To facilitate this graphical user interface, a non-transitory machine-readable media is programmed to store invoices in a list in a database and count the number of invoices in each quadrant in the list in the database. The first quadrant may include a representation of the number of new documents that are not yet uploaded. The second quadrant may include a representation of the number of uploaded documents that may not be able to process without further analysis. The third quadrant may include a representation of the number of uploaded documents that have been automatically processed by the software and are ready to submit electronically. The fourth quadrant may include a representation of the number of documents submitted electronically.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.

Abstract: An invoice payment acceleration system includes a database stored on a computer readable medium including a plurality of records, each record corresponding to an invoice issued by a vendor for payment from a buyer. A network interface is configured to receive a payment initiation command from a buyer for payment of an invoice to a vendor by a first payment method. A processor is configured to analyze the invoice as to which the payment command is received to determine whether the invoice satisfies one or more predetermined criteria. The processor further is configured, when the processor determines that the invoice satisfies the one or more predetermined criteria, to convert the first payment method to a second payment method, and the second payment method provides for an accelerated payment as compared to the first payment method. The criteria for acceleration may include vendor preferences, and/or terms of a subscription service.