Abstract: An Artificial Intelligence (AI)-based data processing system processes current data to determine if the quality of the current data is adequate to be provided to data consumers and if the quality is adequate, the current data is further analyzed to determine if an impacted load including changes to dimension data of the current data or an incremental load including changes to fact data of the current data is to be provided to the data consumers. Depending on the amount of data to be provided to the data consumers, processing units (PUs) may be determined and assigned to carry out the data upload. Various machine learning (ML) models that are used to provide predictions from the current data are analyzed to determine the quality of predictions and if needed, can be automatically retrained by the data processing system.

Abstract: A system for auto classification of products includes an entity recognizer and a model selector. The entity recognizer receives training data including an attribute of a product. The model selector selects a feature from the training data using a first statistical model to provide a first feature and a second statistical model to provide a second feature, and trains a probabilistic classifier using the first and the second features for providing a first and a second classification models respectively. Further, the model selector calculates an accuracy score of the obtained classification models for each distinct category in a preset hierarchy of categories and selects a classification model from the obtained classification models based on the accuracy score. The selected classification model has a highest accuracy score for a corresponding category in the preset hierarchy.

Abstract: An Artificial Intelligence (AI)-based data processing system processes current data to determine if the quality of the current data is adequate to be provided to data consumers and if the quality is adequate, the current data is further analyzed to determine if an impacted load including changes to dimension data of the current data or an incremental load including changes to fact data of the current data is to be provided to the data consumers. Depending on the amount of data to be provided to the data consumers, processing units (PUs) may be determined and assigned to carry out the data upload. Various machine learning (ML) models that are used to provide predictions from the current data are analyzed to determine the quality of predictions and if needed, can be automatically retrained by the data processing system.

Abstract: An Artificial Intelligence (AI) based performance prediction system predicts the performance and behavior of an entity via a complex structure made of iterative and parallel machine learning (ML) model rebuilds with real time data collection. The engine selects a best model at every level and scores the entity to help in predicting the behavior of the entity. Model selection is based on various model selection criteria. The selected model determines a propensity score that indicates a likelihood of the entity migrating from a currently categorized segment to another segment of higher or lower value. Accordingly, messages or alerts with one or more of corrective actions or system enhancements can be transmitted based on the status of the entity via various targeting channels and a post treatment analysis is carried out to find the effect of the corrective actions on the entity.

Abstract: An artificial intelligence based system and method for predicting and preventing illicit behavior is disclosed. The system and method may include obtaining search strings used by multiple users, as well as the clickstream data of such users. The search terms included in the search strings may be preprocessed and analyzed for inclusion of suspicious words, e.g., words related to illicit behavior, provided in a corpus of suspicious words. Information associated with the search strings containing suspicious words may be analyzed to identify users associated with the same search strings. The clickstream of the identified users may be analyzed to determine whether the users are likely to engage in illicit behavior. Preventive measures may be taken to prevent such users from engaging in such illicit behavior.

Abstract: A system for providing real-time optimized product designs may obtain historical data and real-time data related to a product to identify real-time customer preferences and sentiments associated with the product. The system may provide a plurality of real-time potential attribute recommendations comprising attributes likely to be preferred by customers. The system may provide a set of preferred attribute recommendations based on the plurality of real-time potential attribute recommendations ranked highest in an attribute ranking order. The system may provide a cannibalization factor and a similarity index for each of the set of preferred attribute recommendations. The system may provide a real-time demand forecast of the product and may also provide a predicted cannibalization volume and a predicted incremental volume for the product in real-time. The system may determine a set of final attribute recommendations for the product to provide an optimized design for the product in real-time.

Abstract: A system for auto classification of products includes an entity recognizer and a model selector. The entity recognizer receives training data including an attribute of a product. The model selector selects a feature from the training data using a first statistical model to provide a first feature and a second statistical model to provide a second feature, and trains a probabilistic classifier using the first and the second features for providing a first and a second classification models respectively. Further, the model selector calculates an accuracy score of the obtained classification models for each distinct category in a preset hierarchy of categories and selects a classification model from the obtained classification models based on the accuracy score. The selected classification model has a highest accuracy score for a corresponding category in the preset hierarchy.

Abstract: An artificial intelligence based system and method for predicting and preventing illicit behavior is disclosed. The system and method may include obtaining search strings used by multiple users, as well as the clickstream data of such users. The search terms included in the search strings may be preprocessed and analyzed for inclusion of suspicious words, e.g., words related to illicit behavior, provided in a corpus of suspicious words. Information associated with the search strings containing suspicious words may be analyzed to identify users associated with the same search strings. The clickstream of the identified users may be analyzed to determine whether the users are likely to engage in illicit behavior. Preventive measures may be taken to prevent such users from engaging in such illicit behavior.

Abstract: An Artificial Intelligence (AI) based performance prediction system predicts the performance and behavior of an entity via a complex structure made of iterative and parallel machine learning (ML) model rebuilds with real time data collection. The engine selects a best model at every level and scores the entity to help in predicting the behavior of the entity. Model selection is based on various model selection criteria. The selected model determines a propensity score that indicates a likelihood of the entity migrating from a currently categorized segment to another segment of higher or lower value. Accordingly, messages or alerts with one or more of corrective actions or system enhancements can be transmitted based on the status of the entity via various targeting channels and a post treatment analysis is carried out to find the effect of the corrective actions on the entity.