Microsoft Technology Licensing, LLC

Ananthatejas Raghavan

Vipindeep Vangala

Dheepak Ramaswamy

Deepinder S Gill

Nithin Kumar Mara

Snehdip Karandikar

Sergey M Yekhanin

Di Li

Muthukrishnan Paramasivam

Sreeram Nivarthi

Leanna Holly Robb

Janardhan Dattatreya Kulkarni

Aleksey Ashikhmin

Sivakanth Gopi

Sravanth Venkata Madhu Kurumaddali

Jagadeesh Virupaksha Huliyar

Anil Kumar Revuru

Anjaneya Malpani

Sriradha Selvaraj

Xinyun Sun

Bingran Luo

Systems and methods for contextual memory capture and recall are provided. The contextual memory capture and recall systems and methods help a user create, store, and recall memory information associated with an identified activity. The contextual memory capture and recall systems and methods are capable of identifying user activities where a memory inquiry may be desirable, creating a memory inquiry with a recommended memory action based on the activity, providing the memory inquiry to the user, and automatically linking an accepted memory action, along with any received memory information for the memory action, with the identified activity.

Systems and methods for contextual memory capture and recall

A system and method for applying noise to data is described. The system accesses a metric value of a metric of each user from a group of users of an application. The metric indicates a measure of an operation of the application by a corresponding user. The system generates noise values and defines a distribution of the noise values to the group of users. The system modifies the metric value of the metric of each user with a corresponding noise value from the noise values based on the distribution.

Noise generation for differential privacy

A system and method for determining enterprise metrics of an enterprise application is described. The system receives a root cause definition that identifies enterprise user metrics and predefined parameters for the enterprise user metrics. The enterprise user metrics identify operation metrics of the enterprise application by users of the enterprise. The system stores the root cause definition in a library of root causes definitions. The system receives a selection of a plan that identifies an operation attribute of the enterprise application. The system identifies a root cause from the library of root causes definitions based on the plan. The system generates a recommendation based on the identified root cause.

Root cause discovery system

Automated travel diary creation is provided. Various implicit and explicit signals are collected and shared with a trip content generation system, which operates to identify visits made by a user and details about trip associated visits, to generate sentences describing the visits based on a learned personal writing style of the user, and to order the generated sentences and form coherent paragraphs in combination with related images to generate an automatically curated, enriched, and personalized travel diary. The travel diary is displayed to the user in a graphical user interface. The user is enabled to edit the travel diary and to share the travel diary with other users.

Automated travel diary generation

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Telangandi, India

Ananthatejas Raghavan