Microsoft Technology Licensing, LLC

Bryston Mitsuo Nitta

Chen Liang

Adrian Francisco Teran Guajardo

Nelly L Porter

Shayak Lahiri

Anooshiravan Saboori

Muhammad Umar Janjua

Debasish Mukhopadhyay

Vassil Dimitrov Bakalov

Haitao Li

Philip Joseph Hallin

Marc Shepard

Anthony Paul Penta

Xiaohong Su

Yogesh A Mehta

Hung Minh Dang

Kelvin Sjek Yiu

Nimish Aggarwal

Ramasubramanian Shastri

Oana Silvia Nica

Maarten Van Horenbeeck

Divyesh Shah

Chris Day

Aarthi Thangamani

Marc-Andrea Klimaschewski

Wenbo Shen

Vassil D Bakalov

A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.

Secure and private hyper-personalization system and method

The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

Configuring an electronic device using artificial intelligence

Described herein is a system and method for utilizing a virtual secure mode instance to protect an artificial intelligence model from unauthorized access (e.g., inspection, copying) during execution of an application utilizing the AI model (e.g., training and/or inference) on a client device. An encrypted artificial intelligence model is received in a virtual secure mode instance of the client device. The encrypted artificial intelligence model is decrypted in the virtual secure mode instance using a decryption secret. The decrypted artificial intelligence model is stored in the virtual secure mode instance. An application that utilizes the decrypted artificial intelligence model is executed (e.g., training and/or inference) in the virtual secure mode instance.

Protecting artificial intelligence models using virtual secure mode

Systems, methods and computer program products are described herein that enable an intelligent agent to automatically discover an application programming interface (API) exposed by a service, to obtain descriptive metadata associated with the API, and to generate, based on the metadata, natural language processing information and/or function call building information associated with at least one functionality of the service. In addition, the intelligent agent is enabled to use the natural language processing information to determine that a user intends to interact with the service based on at least one user input of the user and to use the function call building information to build a function call for interacting with the service based on the at least one user input of the user, and to place the function call to the service on behalf of the user.

Adding descriptive metadata to application programming interfaces for consumption by an intelligent agent

Examples described herein generally relate to device analytics. Specifically, the present disclosure provides resource deployment at an organization including one or more devices. The present disclosure provides for receiving telemetry data from the one or more devices associated with the organization. The present disclosure further provides for generating a set of deployment rings for the deployment of the software resource at the one or more devices of the organization. Additionally, the present disclosure provides for deploying the software resource within the organization based on the set of deployment rings.

Resource deployment using device analytics

In many information security scenarios, a certificate issued by a certificate authority may be presented to a client in order to assert a trust level of a certificated item, such as a message or a web page. However, due to a decentralized structure and incomplete coordination among certificate authorities, the presence and exploitation of security vulnerabilities to issue untrustworthy certificates may be difficult to determine, particularly for an individual client. Presented herein are techniques for advising clients of the reputations of respective certificate authorities by evaluating the certificates issued by such certificate authorities, such as the number and types of domains certified by the certificate; the number and pattern of certificates issued for the domain; and the certification techniques used to issue the certificates. Such evaluation enables a determination of a certificate authority trust level that may be distributed to the clients in a certificate authority trust set.

Advising clients about certificate authority trust

A population of devices provides telemetry data and receives software changes or updates. Event buckets for respective events are found. Event buckets have counts of event instances, where each event instance is an occurrence of a corresponding event reported as telemetry by a device. Records of the software changes are provided, each change record representing a software change on a corresponding device. The event buckets are analyzed to identify which indicate an anomaly. Based on the change records and the identified event buckets, correlations between the software changes and the identified event buckets are found.

Anomaly analysis for software distribution

A computing device analyzes digital certificates received from various different sites (e.g., accessed via the Internet or other network) in order to automatically detect fraudulent digital certificates. The computing device maintains a record of the digital certificates it receives from these various different sites. A certificate screening service operating remotely from the computing device also accesses these various different sites and maintains a record of the digital certificates that the service receives from these sites. In response to a request to access a target site the computing device receives a current digital certificate from the target site. The computing device determines whether the current digital certificate is genuine or fraudulent based on one or more of previously received digital certificates for the target site, confirmation certificates received from the certificate screening service, and additional characteristics of the digital certificates and/or the target site.

Automatic fraudulent digital certificate detection

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Redmond, WA, United States of America

Bryston Mitsuo Nitta