Sap Se

Fuming Wu

Tu Truong

Sudhir Bhojwani

Quan Zhang

Sandeep Chakravarty

Sudha Lakshman

Richa Namballa

Lin Dong

Yue Li

Han Li

Julio C Navas

Omar Rahman

Hanxiang Chen

Nazanin Zaker Habibabadi

Ajain Kuzhimattathil

Provided is a method and system for normalizing catalog item data to create higher quality search results. In one example, the method may include receiving a record comprising an unstructured description of an object, identifying a type of the object from among a plurality of object types and identifying a predefined attribute of the identified type of object, extracting a value from the unstructured description corresponding to the predefined attribute and modifying the extracted value to generate a normalized attribute value, and storing a structured record of the object in a structured format comprising a plurality of values of a plurality of attributes of the object from the unstructured description including the normalized attribute value for the predefined attribute of the object.

Normalization of unstructured catalog data

In an example, a machine learning algorithm is used to train an entity risk evaluation model to output an entity risk score based on transaction data in a computer network. Entity risk scores for various entities may be stored in a database, and retrieved and displayed upon user interaction with one or more reports involving corresponding entities.

Machine learning models for evaluating entities in a high-volume computer network

Growing community of inventors

Palo Alto, CA, United States of America

Fuming Wu