Microsoft Technology Licensing, LLC

Dhruv Joshi

Dolly Sobhani

Suneetha Dhulipalla

Brian Eugene Kihneman

Chaitanya Gogineni

Brian Allan Mueller

Ji Li

Matthew Joseph Koscumb

Fumiaki Yagi

David William Brown

John Christian Leone

Sumithra Bhakthavatsalam

Siddharth Dahiya

Heron Arzaquel Anzures Reyes

Vijay Mohan

Sameera Satyavan Desai

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

Methods, systems, and computer storage media for providing log files using logging system operations in a logging system. The logging system operations support memory mapping log files and asynchronously managing file operations. The logging system operations support selectively mapping segments of log files during write operations. The logging operations also support performing file operations (e.g., closing, opening, deleting and serializing files) advantageously as background processes. Selective memory mapping specifically includes incrementally mapping new segments of a log file up to a predetermined log file size limit. The logging operations support processing spare files using spare file memory mapping. A spare file replaces an existing log file to continue writing logging data using the spare file. Based on the memory mapping, additional logging system operations of the logging system can be performed including persisting the log file data even when the application crashes, minimizing of forced flushes, and asynchronous file management.

Growing community of inventors

Bellevue, WA, United States of America

Dhruv Joshi