Microsoft Technology Licensing, LLC

Dolly Sobhani

Dhruv Joshi

Brian Eugene Kihneman

Peter P Baer

Anand Balachandran

Suneetha Dhulipalla

Eric Leighton Smith

Brian Allan Mueller

Siddharth Dahiya

Heron Arzaquel Anzures Reyes

Yijin Wu

Ji Li

Matthew Joseph Koscumb

Fumiaki Yagi

Chaitanya Gogineni

David William Brown

John Christian Leone

Sumithra Bhakthavatsalam

Kristopher Wayne Langohr

Ankit Kapur

Sameera Satyavan Desai

Medhavin Vadhanasindhu

Ajanta Mahato

Antonio Andre Monteiro Manoel

A system for development of an Artificial Intelligence (AI) model while protecting sensitive user information includes: a confidential computing environment in which original prompts to the AI model written by users are collected; a trained synthetic prompt generator to generate synthetic prompts based on the original prompts, wherein the synthetic prompt generator generates anonymized synthetic prompts without sensitive user information identifiable from the original prompts; and a developer computing environment in which the synthetic prompts are submitted to the AI model under development to generate a dataset that includes the synthetic prompts and corresponding AI model output for analysis to determine updates for the AI model while protecting the sensitive user information of actual users.

Protecting sensitive user information in developing artificial intelligence models

Methods, systems, and computer programs are presented for generating a usage model for predicting user commands in an app. One method includes receiving model information from client devices. The model is obtained at each client device by training a machine-learning program with app usage data. The server generates synthetic data using the models from the client devices. A machine-learning program is trained using the synthetic data to obtain a global model, which receives as input information about recent commands entered on the app and generates an output with a prediction for the next command expected to be received by the app. The information of the global model is transmitted to a first client device, and the app provides at least one command option in the app user interface based on a prediction, generated by the global model, of the next command expected.

App usage models with privacy protection

A method may include detecting an error during execution of a function of an application executing on a system; inputting an identifier of the error into a first client-side telemetry data filter model; based on the inputting, receiving an output value from the first client-side telemetry data filter model; determining that the output value is above an importance threshold; and in response to the determining, transmitting telemetry data associated with the error to a server computing device.

Client-side telemetry data filter model

A system and method for sampling telemetry events are provided. The method includes monitoring, using at least one listening module, execution of instructions by a processor, identifying, using the at least one listening module, one or more unique tags, determining, based on one or more trigger conditions, if the one or more unique tags is associated with at least one of the one or more trigger conditions, wherein the one or more trigger conditions are dynamically modifiable via a remote device, obtaining, based on the determination, a portion of a stack data structure associated with the execution of the instructions by the processor; and performing, based on the determination, at least one action.

Dynamic remote collection of supplemental diagnostic data and triggering of client actions for client software application

A system and method for sampling telemetry events are provided. The method includes receiving, by a cloud-based server, a plurality of telemetry events, related to an application, from a plurality of client devices; generating, by the cloud-based server, a sampling model for collecting a telemetry event based on the plurality of telemetry events, where the sampling model defines under what conditions the telemetry event is to be reported by a client device; generating, by the cloud-based server, an instruction for determining whether or not to report an incoming same telemetry event by the client device based on the sampling model; and providing, by the cloud-based server, the instruction to the client device, to allow the client device to determine whether or not to report the incoming same telemetry event based on the instruction.

Sampling of telemetry events to control event volume cost and address privacy vulnerability

Intelligent collection and analysis of in-app failure data is disclosed herein. Upon an application failure in a client device, the client device may collect failure information uniquely identifying a specific failure and provide the failure information to an analysis system. The analysis system may identify a specific failure that identifies the application and a specific portion of the code in the application, based on the failure information and match an action correlated to the specific failure where the action is uniquely designed to resolve the specific failure in the application. The action may include instructions for the client device used to intelligently lead to a resolution of the specific failure. The analysis system may transmit the action to the client device to perform the action and provide any follow up information to the analysis server. The analysis server may use the information to further analyze the specific failure.

In-app failure intelligent data collection and analysis

Aspects of the present disclosure relate to techniques for mitigating inadvertent user information collection in telemetry data. In examples, user information is used to evaluate telemetry data associated prior to transmission to a server device. If an instance of user information is identified within the telemetry data, a warning indication is generated. The warning indication may be transmitted to the server device either instead of or in combination with the telemetry data. As a result of the warning indication, the software may be modified to resolve the issue that caused the introduction of the user information into the telemetry data, thereby avoiding future instances of inadvertent data collection. In response to the warning indication, the server may be configured to reject similar telemetry data from other devices, thereby avoiding collecting such data from the other devices. The server device may also use the warning indication to remove or otherwise censor previously collected user information from stored telemetry data.

Mitigating inadvertent user information collection in telemetry data

A method may include receiving user instrumentation data and code instrumentation data from at least one user computing device such that the user and code instrumentation data is associated with performance of a user action of an application. The user instrumentation data may include a name identification of the user action and an input type identification of the initiation of the user action. The method may include storing the user and code instrumentation data in a database. The method may include receiving a search query associated with a past user action made in the application. The method may include retrieving, from the database, a set of one or more user actions based on the search query. The method may include presenting a user interface that includes an abstracted representation of the retrieved set of user actions.

Instrumentation of user actions in software applications

Technologies are disclosed that help developers determine server resource usage caused by client application generated events. Embodiments may be employed while an application is under development to balance the benefit of a given event with an estimated cost to process the event once the application is deployed. One aspect of the present disclosure provides a telemetry application that receives application generated events and estimates a resource cost they cause to data collection servers. The resource cost may be based on historic data captured from similar or related events, as well as data about how often the event and related events are raised by the application.

Real time telemetry monitoring tool

A method may be performed to receive and store in a database user instrumentation data and code instrumentation data from a user computing device. The user and code instrumentation data may be associated with performance of a user action of an application. The user instrumentation data may include a name identification of the user action, an input type identification of the initiation of the user action, and context data of the user action as executed. The user instrumentation data may be stored as associated with a user identification. The method may also include receiving a request from a requesting computing device to identify at least one past user action of the user. In response to receiving the request, the method may include querying the database and providing results of the query to the requesting computing device.

Systems, method sand computer program products for facilitating the automatic deletion of received emails after a user-selectable time period has elapsed are disclosed. In various embodiments, email messages contain a header field referred to as an 'Expiration Time' header field that dictates the length of time the email will reside in a designated Inbox prior to its automatic deletion by an email server. The server is able to automatically delete the received email as long as the email is residing in a folder that is synched to the server. A user receiving the email has access to the Expiration Time Header field and can modify its value if desired and allowed by the sender. The user can move the retrieved email to a folder that is not synched to the server, thus preventing the email from being automatically deleted by the server.

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Redmond, WA, United States of America

Dolly Sobhani