Microsoft Technology Licensing, LLC

Nayana Singh Patel

Mehmet Kadri Umay

Imran Siddique

Jyothsna Devi Bijjam

John Marley Gray

Supriya Madhuram

Gregory Philip Cignavitch

Brenda Yachu Lee

Daniel Keith Scholl

Shikha Garg

A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Extensible data platform with database domain extensions

In examples, streaming data is received from a data source (e.g., by an edge device associated with a data platform) and is queued and aggregated. Batch data may similarly be received from a data source. The batch data and the aggregated streaming data may be processed to generate metadata accordingly. The data and metadata may be provided to the data platform, where the metadata may be used to update an index and the data may be stored in association with the index. In some instances, the data may be stored in chunks to facilitate subsequent retrieval of the data. In response to a request for the data, the index may be used to identify relevant data, which may include data associated with batch and/or streaming data formats, thereby enabling the client device to access data having any of a variety of formats from the data platform.

Heterogeneous data platform

A remote server computing system is configured to deploy a cloud-service-managed control plane and a cloud service data plane spanning the remote server computing system, a local edge computing device, and a local on-premises computing device connected in a hybrid cloud environment. Energy-related training data is received including a plurality of energy-related training data pairs. A machine learning function is trained using the plurality of training data pairs to predict a classified label for restricted energy-related data that is not accessible to the remote server computing system. The trained machine learning function is deployed to the one or more of the local edge computing device and the local on-premises computing device via the cloud service data plane. The remote server computing system is further configured to receive, via the cloud service data plane, classified output of the trained machine learning function.

Deploying a function in a hybrid cloud

A computer system comprises a logic system, and, operatively coupled to the logic system, a computer-memory system holding instructions that, when executed by the logic system, cause the computer system to: receive a token-behavior selection corresponding to a real-world asset to be tracked on a virtual ledger; receive a virtual-ledger architecture selection identifying a provider-defined architecture of the virtual ledger; construct a template for registration of a token class on the virtual ledger according to the provider-defined architecture of the virtual ledger, wherein each new token instantiated from the token class exhibits a set of behaviors determined by the token-behavior selection; and provide access to the template to a client computer device.

Reusable template for asset token

Techniques for implementing a ledger-independent token service are provided. According to one set of embodiments, a computer system executing the service can receive, from a user, a request to create a token on a distributed ledger network. The computer system can further provide to the user one or more token templates, where each token template corresponds to a type of physical or digital asset and defines a set of one or more attributes and one or more control functions associated with the type. The computer system can then receive, from the user, a selection of a token template in the one or more token templates and create the token on the distributed ledger network, where the created token includes the set of one or more attributes and one or more control functions defined in the selected token template.

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Mercer Island, WA, United States of America

Nayana Singh Patel