The Bank of New York Mellon Corporation

Google Inc.

Other

Apigee Corporation

Noodle.ai

Kumar Srivastava

Praveen Kumar Singh

Deepinder Singh Dhingra

Sarfaraj Ahmad

Arijit Saha

Sourabh Chourasia

Ganesh Moorthy

Ted Gaubert

An AI platform to enable one or more users to design and create AI enabled applications is provided. The AI platform comprises a data module configured to condition data received from a plurality of data sources to generate a corresponding data pipeline; wherein the data module comprises a plurality of reusable data components. The AI platform further comprises an intelligent processing module configured to process a plurality of datasets received on the data pipeline and generate a corresponding artificial intelligence (AI) pipeline; wherein the intelligent processing module comprises a plurality of reusuable data processing components. The AI platform further includes a communication module configured to enable one or more users to select a set of reusable data components and set of reusuable data processing modules and build a corresponding AI enabled application and an AI configuration module configured to seamlessly integrate the selected set of reusable data components and the set of reusable data processing components using a plurality of standardized interfaces.

Artificial intelligence platform

Disclosed is a method and a system for using techniques to stitch cybersecurity, generate network risks and predictive mitigations. The method includes collecting data from several data sources and labeling events. The method includes creating a profile for each entity observed in the data with the behavior of the profile determined through the analytical analysis of the events in which the entity participates including the transference of labels from events to the entity. One or more profiles of an organization are identified that have changed and the change is processed using specific attack sequence detection to identify one or more risks associated with each profile. The method further includes notifying one or more users associated with the one or more profiles based on the one or more risks.

Method and system to stitch cybersecurity, measure network cyber health, generate business and network risks, enable realtime zero trust verifications, and recommend ordered, predictive risk mitigations

A dataset management system organizes datasets using a data relationship graph that serves as a representation of datasets that are related to one another. For example, the data relationship graph includes nodes that each represent a dataset as well as edges that each connect two nodes. Each edge represents a similarity in characteristics of the similar datasets such as a similarity in the datasets' origin, organizational schema, lineage, or data values. When a user is to be provided recommended datasets, the dataset management system identifies candidate datasets by traversing the nodes and edges of the data relationship graph. Amongst these candidate datasets, the dataset management system evaluates users that have accessed the candidate datasets as well as the context in which the candidate datasets were accessed to identify recommended datasets for presentation to the user.

Predicting and recommending relevant datasets in complex environments

An online database system receives a tenant dataset from a tenant and automatically generates a mapping that can be used to convert the tenant dataset from a tenant schema to a database schema used by the online database system. To generate the mapping, the online database system analyzes individual entries of the tenant dataset received from the tenant and determines a likelihood of usage. The mapping maintains entries in the tenant dataset that are likely to be used as individual entries in the converted dataset and compresses entries that are less likely to be used into fewer entries to satisfy the database schema. The online database system converts the tenant dataset from the tenant schema to the database schema using the mapping.

Processing datasets of varying schemas from tenants

A dataset management system uses a fa&#xe7;ade API to automate access to data stores maintained by data tenants. The system receives a configuration request from a data tenant and determines whether the configuration request corresponds to an existing data tenant. If an existing data tenant is identified, the system updates the fa&#xe7;ade API associated with the existing data tenant based on the configuration request. When a client request for data associated with the data tenant is received, the fa&#xe7;ade API formats the direct query to include the updated parameters and executes the direct query to retrieve data from the data stores.

Automatic API fa&#xe7;ade generation in dynamic multi-tenant environments

A proxy API system intercepts and enriches API messages such as an API request or API response. The proxy API system enriches an API message by incorporating additional semantic meaning into the API message. More specifically, the proxy API system extracts features from a message and applies the features to a trained model. The trained model identifies annotations that are relevant for labeling the API message. Additionally, the proxy API system identifies reference data corresponding to the API message data and annotations to provide additional data related to content of the API message, such as additional detail about a data item in the message such as additional fields related to that item. The proxy API system enriches the API request or API response with the annotations and reference data.

Enhancing API messages

Embodiments of enhancing API servicing schemes are disclosed. A set of proxy code associated with servicing the API requests of an application is analyzed during development time to determine if any portion can be modified to improve the service scheme, prior to executing the set of proxy code in a runtime. After the set of proxy code is deployed in a runtime environment, performance data associated with the current service scheme is collected and periodically used to determine whether the service scheme for the API requests of the application is to be updated.

Enhancing API service schemes

A dataset management system organizes datasets and tracks the changes to the dataset to adaptively respond to user queries. For a dataset, the dataset management system tracks the evolving schema of the dataset over time as new data values and/or updates to existing data values are incorporated into the dataset. When a query is received, the dataset management system accesses the schema of a dataset to understand how the dataset has changed over time. Given the changing schema of the dataset, the dataset management system can respond by providing recommendations as to suggested queries that can return improved results. As another option, the dataset management system can execute a query and return results that satisfy the query to the client device that provided the query.

Organizing datasets for adaptive responses to queries

A code repository receives and analyzes changed code to determine whether and how to automatically instrument the code. The code repository identifies locations within the code, such as locations next to external function calls, to insert instrumentation markers. The code repository determines the information to be collected at the location. Redundant information or information that can be derived from other collected information need not be collected. When the instrumented code is executed, the execution of instrumentation markers causes the collection of information. The subsets of information can be used to troubleshoot the code.

Automatic instrumentation of code

A method is provided for detecting irregularities of one or more application programmer interface (API) entities. The method includes receiving a request for data of one or more API entities. The method further includes monitoring said data from at least one server and detecting irregularities in the data. The method also includes displaying information pertaining to the irregularities to a user.

Growing community of inventors

Mountain View, CA, United States of America

Kumar Srivastava