Sap Se

Sap Ag

Lev Sigal

Anton Ioffe

Yuri Arshavski

Juliy Broyda

Anna Fishbein

Ran Bittmann

Suchitra Sundararaman

Jesper B Lind

Jesper Lind

Iryna Butselan

Alexander Glauberman

Shai Ziv

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives an image of a document, the document comprising a set of text. The program further provides the set of text to a machine learning model configured to determine, based on the set of text, a plurality of probabilities for a plurality of defined types of documents. Based on the plurality of probabilities for the plurality of defined types of documents, the program also determines a type of the document from the plurality of defined types of documents.

Classifying documents based on machine learning

Systems and methods are provided for training a machine learning model to use comments entered by a user submitting an expense to determine a correct expense type. The trained machine learning model is used to predict an expense type by analyzing submitted text comments corresponding to a submitted expense. The expense can be flagged if a mismatch is determined between the expense type of the submitted expense and the predicted expense type, or the submitted expense can be automatically updated to the predicted expense type.

Expense-type audit machine learning modeling system

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.

Anomaly and fraud detection with fake event detection using machine learning

Some embodiments provide a program that queries a database for a subset of a plurality of records in the database. Each record in the plurality of records includes a value for a field. The program further samples the subset of the plurality of records to identify a set of records in the subset of the plurality of records. The program also sorts the set of records based on the value for the field in each record in the set of records. The program further determines a first value for the field of a first record in the sorted set of records and a second value for the field of a second record in the sorted set of records forms a slope that is greater than or equal to a defined slope. The program determines a threshold value for the subset of the plurality of records based on the first record.

Determining threshold values based on sampled data

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving a request to authenticate a document image. The image is preprocessed to prepare the image for line orientation analysis. The preprocessed image is analyzed to determine lines in the preprocessed image. The determined lines are automatically analyzed by performing line orientation test(s) on the determined lines to generate line orientation test result(s) for the preprocessed image. The line orientation test result(s) are evaluated to determine whether the image is authentic. In response to determining that at least one line orientation test result matches a predefined condition corresponding to an unauthentic document, a determination is made that the image is not authentic. In response to determining that none of the line orientation test results match any predefined condition corresponding to an unauthentic document, a determination is made that the image is authentic.

Anomaly and fraud detection with fake event detection using line orientation testing

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program queries a database for a subset of a plurality of records in the database. Each record in the plurality of records includes a value for a first field and a second value for a second field. The program further normalizes the first value of the first field of each record in the subset of the plurality of records. The program also divides the subset of the plurality of records into a plurality of groups of records based on the second values of the second field. The program further generates a function for predicting a type of a particular record based on the value of the field of the particular record.

Predicting types of records based on amount values of records

A preemptive neural network database load balancer configured to observe, learn and predict the resource utilization that given incoming tasks utilize. Allows for efficient execution and use of system resources. Preemptively assigns incoming tasks to particular servers based on predicted CPU, memory, disk and network utilization for the incoming tasks. Direct write-based tasks to a master server and utilizes slave servers to handle read-based tasks. Read-base tasks are analyzed with a neural network to learn and predict the amount of resources that tasks will utilize. Tasks are assigned to a database server based on the predicted utilization of the incoming task and the predicted and observed resource utilization on each database server. The predicted resource utilization may be updated over time as the number of records, lookups, images, PDFs, fields, BLOBs and width of fields in the database change over time.

Predictive database resource utilization and load balancing using neural network model

A neural network resource sizing apparatus for database applications. Through use of multiple database application metrics input into a neural network learning algorithm, recommended resource capacities are generated. Input parameters such as the number of records, lookups, images, PDFs, fields, BLOBs and width of fields for example may be utilized to train a neural network to yield needed resource metrics such as the processing power, memory, disk and/or network capacities required to run the database application. Training for the neural network may involve running tests over all desired cross interactions of input and output parameters beginning for example with a small repository and ending with the maximum complexity of data and schema test. The training data is input into the neural network for the given database application version and utilized to plan resource utilization. A portal or webservice may be utilized to provide an interface to the apparatus.

Growing community of inventors

Karmiel, Israel

Lev Sigal