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 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: Novel isolated peptides which induce analgesia and which inhibit ASIC channels (Acid Sensing Ion Channels), to the polynucleotides encoding these peptides, and also to the pharmaceutical compositions, host cells, and vectors comprising these peptides and the polynucleotides encoding these peptides. In particular, these peptides are isolated from the venom of the snake Dendroaspis polylepis. The present invention also relates to the use of these peptides as a diagnostic tool or as a medicament, particularly as an analgesic, or for identifying analgesic molecules or molecules which inhibit ASIC channels.

Abstract: The present invention relates to novel isolated peptides which induce analgesia and which inhibit ASIC channels (Acid Sensing Ion Channels), to the polynucleotides encoding said peptides, and also to the pharmaceutical compositions, host cells and vectors comprising same. In particular, said peptides are isolated from the venom of the snake Dendroaspis polylepis. The present invention also relates to the use thereof as a diagnostic tool or as medicament, and in particular as an analgesic, or for identifying analgesic molecules or molecules which inhibit ASIC channels.

Abstract: A periodontal examination data recording and recall method and apparatus. The periodontal examination method includes obtaining an automated periodontal examination data recording and recall apparatus having at least one pre-programmed mode of operation whereby periodontal examination data is entered in a predetermined sequence, performing the periodontal examination and collecting the periodontal examination data, entering the periodontal examination data into the periodontal examination apparatus, the periodontal examination data obtained through the periodontal examination performed in a predetermined sequence, and storing the periodontal examination data for future use, including review and display. The periodontal examination apparatus includes a keypad input device, an LCD, a main controller, an attachment device for securing the apparatus to the examiner's arm or other portion of the armamentarium, and is provided with at least one automatic input sequence.