Oracle International Corporation

Adobe, Inc.

Salesforce Com, Inc.

Rajan Madhavan

Niranjan Shivanand Kumbi

Ajay Awatramani

Omar Rahman

Vaidyanathan Venkatraman

Pawan Nachnani

Madalasa Venkataraman

Navin Kumar Ramineni

Kai Yeung Lau

Paul Deepakraj Retinraj

Dinesh Ghanta

Kenneth R Lenga

Arun Sadayam

Saaed Fattahi

Arindam Ghosh

James F Fowler

Brajendra Kumar Bhujabal

Girish Nautiya

Kanagaraj Ramachandran

Garth B Moulton

Badsah Mukherji

Peng Chong Sien

Sravan Kumar Ananthula

Techniques for generating a dashboard are disclosed. The system may obtain a set of one or more characteristics of a target user. A set of candidate data metrics that are relevant to the target user may be determined by applying a metric selection model to the set of characteristics. The set of candidate data metrics may be presented as a set of recommend data metrics. Input may be received from a user selecting a particular data metric from the set of recommended data metrics. A visualization selection model may be applied to the particular data metric and/or the set of user characteristics to select a visualization type for the particular data metric. A visualization of the particular data metric that accords to the selected visualization type may be generated based on a set of values associated with the particular data set. The visualization may be presented in the user dashboard.

Intelligent generation of visualizations of data metrics

Data can be received that includes information corresponding to a set of users. Privacy protection protocols that apply to the data can be identified. A subset of the data can be identified as being personally identifiable information (PII) data, where the subset includes a set of PII attributes. The PII attributes can be split into categories based on a format of a data field in the PII attributes. The processed PII data can be combined with non-PII data to create processed client data. It can be determined to add noise to part of the processed PII data. An amount of noise can be determined based on the privacy protection protocols. The amount of noise can be added to part of the processed PII data to produce protected data. A machine-learning model can be trained using the protected data.

Systems for design and implementation of privacy preserving AI with privacy regulations within intelligence pipelines

Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.

Machine learning techniques for schema mapping

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a recommended target audience based on determining a predicted attendance utilizing a neural network approach. For example, the disclosed systems can utilize an approximate nearest neighbor algorithm to identify individuals that are within a similarity threshold of invitees for an event. In addition, the disclosed systems can implement an attendance prediction model to determine a probability of an invitee attending the event. The disclosed systems can further determine a predicted attendance for an event based on the individual probabilities. Based on identifying the similar individuals to, and the attendance probabilities for, the invitees, the disclosed systems can generate a recommended target audience to satisfy a target attendance for an event based on a predicted attendance for the event.

Generating a recommended target audience based on determining a predicted attendance utilizing a machine learning approach

Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.

Method to explain factors influencing AI predictions with deep neural networks

To facilitate data transfer between two databases, a transfer machine accesses both databases and finds matching records. The transfer machine determines and stores a match status of a record in one database. The match status indicates whether the record corresponds to at least one of the records in the other database, and if so, which record or records in the other database correspond to the record. If the match status indicates that the record matches a record in the other database, the transfer machine determines which record is current and updates the other record. If the match status indicates that a record has no match in the other database, the transfer machine adds a copy of the record to the other database.

Data transfer between first and second databases

There is provided a method and system to maintain published contact information. The system includes a storage facility to store contact information of a first entity at the system. The system further includes an interface to electronically publish, via a communications network, the contact information of the first entity to a plurality of users of the system and receive, at the system, information relating to validity of the contact information of the first entity. The information is received via the communications network from a first user of the plurality of users. The system further includes a stored value module automatically to provide a reward to the first user responsive to the receipt of the information relating to the validity of the contact information of the first entity.

User-maintained contact information data system

Growing community of inventors

Foster City, CA, United States of America

Rajan Madhavan