Microsoft Technology Licensing, LLC

Concurix Corporation

Ying Li

Alexander G Gounares

Charles D Garrett

Li Erran Li

Michael D Noakes

Tarek Najm

Russell Steven Krajec

Lee Wang

Zheng Chen

Benyu Zhang

Hua-Jun Zeng

Wei-Ying Ma

Brian Burdick

Xianfang Wang

Dou Shen

Teresa Mah

Xing Xie

Ying Shan

Gerard Gjonej

Shuzhen Nong

David Earl Heckerman

Christopher A Meek

David Maxwell Chickering

Nir Nice

Zhaohui Tang

Rajeev Prasad

Uri Barash

Yin Li

Raxit A Kagalwala

Abhinai Srivastava

Murali Vajjiravel

Sachin Dhawan

Fengping Zeng

Eric Picard

Jian Wang

William Henry Gates, Iii

Zhengyou Zhang

Jian Qiao Sun

Ming Zhou

Yi Zhang

Joshua T Goodman

Xiaoou Tang

Ji-Rong Wen

Ying-Qing Xu

Ethan Nelson Ray

Jingyi Xu

Melissa Wood Dunn

Michael Joseph Toutonghi

Chuang Wang

Arungunram Chandrasekaran Surendran

Xu Bin Liu

Cynthia Dwork

Omar H Shahine

Weiwei Zhang

Zaiqing Nie

Ewa Dominowska

Chin-Yew Lin

Jie Tong

Flavia Silvana Moser

Weibin Zhu

Michael Goldbach

Ke Tang

Cheng Niu

Wende E Copfer

Kevin Paul Kornelson

Saleel Sathe

Qiaolin Mao

Mingyu Wang

Rong Jian Guan

Xin Jin

Boaz Feldbaum

Zhifeng Yang

Darrell J Cannon

Hai Huang

Thomas A Leung

Hongbin Gao

Heng Zhang

Joshua J Forman

Aly Valli

Chun Yu Wong

Paul R Weber

Sefy Ophir

Valeri Liborski

Vu Anh Ha

Amit Shaked

Eric Don Van Valkenburg

Friedman Arie

Yue Zhou

Sahil Thaker

Xiaofeng Dai

Honghua Dai

Lingzhi Zhao

Kristin D Bromm

Denise D Hui

Matthew S Carr

Honghua (Kathy) Dai

Pascale Queva

Michael Hurt

Hendricus D J Hoek

Alexadner G Gounares

Systems, methods, and computer-readable media for dynamically generating text associated with an advertisement are provided. Core text associated with an advertisement is received from an advertiser, as is at least one attribute relevant to the advertiser and/or a user. Based upon the received attribute(s), it is determined whether customization of the core text is desired. If customization is desired, the core text is modified and presented in association with the advertisement. If customization is not desired, the core text is presented in association with the advertisement. In one embodiment, target advertisement placement information may also be utilized to determine whether customization of the core text is desired.

Dynamic generation of advertisement text

Various embodiments pertain to techniques for measuring a user's privacy level as a user interacts with various web services. In various embodiments, entities with which the user interacts are detected and sensitive information shared by the user is logged to determine what a given entity knows about the user. In some embodiments, sensitive information that is shared by a user can be processed using a predictive algorithm to ascertain a user's level of privacy. When a user's identity is predicted by the algorithm, a user can be alerted to the loss of anonymity. In various embodiments, user-defined areas of anonymity can be used to measure a user's definition of privacy. In some embodiments, alerts can also be provided to the user when a new, previously undisclosed, piece of information is shared by the user.

Monitoring and managing user privacy levels

Periodicity similarity between two different tracer objectives may be used to identify additional input parameters to sample. The tracer objectives may be individual portions of a large tracer operation, and each of the tracer objectives may have separate set of input objects for which data may be collected. After collecting data for a tracer objective, other tracer objectives with similar periodicities may be identified. The input objects from the other tracer objectives may be added to a tracer objective and the tracer objective may be executed to determine a statistical significance of the newly added objective. An iterative process may traverse multiple input objects until exhausting possible input objects and a statistically significant set of input objects are identified.

Periodicity optimization in an automated tracing system

A tracing management system may use cost analyzes and performance budgets to dispatch tracing objectives to instrumented systems that may collect trace data while running an application. The tracing management system may analyze individual tracing workloads for processing, storage, and network performance costs, and select workloads to deploy based on a resource budget that may be set for a particular device. In some cases, complementary tracing objectives may be selected that maximize consumption of resources within an allocated budget. The budgets may allocate certain resources for tracing, which may be a mechanism to limit any adverse effects from tracing when running an application.

Deploying trace objectives using cost analyses

An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.

Relationships derived from trace data

Optimization analysis using similar frequencies

Memoizable functions may be identified by analyzing a function's side effects. The side effects may be evaluated using a white list, black list, or other definition. The side effects may also be classified into conditions which may or may not permit memoization. Side effects that may have de minimus or trivial effects may be ignored in some cases where the accuracy of a function may not be significantly affected when the function may be memoized.

Purity analysis using white list/black list analysis

Increasing performance at runtime from trace data

An execution environment may have a monitoring, analysis, and feedback loop that may configure and tune the execution environment for currently executing workloads. A monitoring or instrumentation system may collect operational and performance data from hardware and software components within the system. A modeling system may create an operational model of the execution environment, then may determine different sets of parameters for the execution environment. A feedback loop may change various operational characteristics of the execution environment. The monitoring, analysis, and feedback loop may optimize the performance of a computer system for various metrics, including throughput, performance, energy conservation, or other metrics based on the applications that are currently executing. The performance model of the execution environment may be persisted and applied to new applications to optimize the performance of applications that have not been executed on the system.

Experiment manager for manycore systems

A tracing system may divide trace objectives across multiple instances of an application, then deploy the objectives to be traced. The results of the various objectives may be aggregated into a detailed tracing representation of the application. The trace objectives may define specific functions, processes, memory objects, events, input parameters, or other subsets of tracing data that may be collected. The objectives may be deployed on separate instances of an application that may be running on different devices. In some cases, the objectives may be deployed at different time intervals. The trace objectives may be lightweight, relatively non-intrusive tracing workloads that, when results are aggregated, may provide a holistic view of an application's performance.

Application tracing by distributed objectives

A client-based ad agent dynamically determines whether an advertisement campaign should bid on an impression for an end user and/or sets the bid price of the advertisement campaign for the impression. When an opportunity for an impression on a web page is identified, the ad agent accesses user data associated with an end user. The ad agent analyzes the user data to identify the relevance and/or value of serving an impression to the end user to the advertisement campaign. Based on the analysis, the ad agent controls whether the advertisement campaign bids on the impression for the end user and/or sets the bid price of the advertisement campaign for the impression.

User centric real-time advertisement bidding

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Bellevue, WA, United States of America

Ying Li