Microsoft Technology Licensing, LLC

Other

Micosoft Corporation

Christopher A Meek

David Earl Heckerman

David Maxwell Chickering

Bo Thiesson

Gary William Flake

Joshua T Goodman

Eric J Horvitz

Oliver Hurst-Hiller

Ramez Naam

Anoop Gupta

Darko Kirovski

Robert L Rounthwaite

Carl Myers Kadie

Brian Burdick

Jesper B Lind

William Henry Gates, Iii

John Carlton Platt

Susan Theresa Dumais

Jody D Biggs

Eric David Brill

Asela J Gunawardana

Trenholme J Griffin

Ewa Dominowska

D Maxwell Chickering

Bradly A Brunell

Xuedong D Huang

Raymond Edward Ozzie

Patrice Y Simard

Gonzalo Alberto Ramos

Geoffrey John Hulten

Brett David Brewer

Kyle G Peltonen

David Max Chickering

Wen-tau Yih

Jin A Suh

Shiqian Rachel Ng

Surajit J Chaudhuri

Timothy S Paek

Ying Li

Li Erran Li

John Grant Bennett

Stephen L Lawler

Tarek Najm

Paul S Bradley

Steven J White

Carlos Garcia Jurado Suarez

Rajeev Prasad

Kenneth Alan Moss

Raxit A Kagalwala

John Scarrow

Geoff Hulten

Murali Vajjiravel

Curtis N Von Veh

Andrew J Newman

Sachin Dhawan

Alexander G Gounares

Zicheng Liu

Philip Andrew Chou

Mehmet Iyigun

Amir Netz

Arun Upadhyaya Kishan

Hari R Pulapaka

Michael Richard Fortin

Yun-Cheng Ju

Bongshin Lee

Nebojsa Jojic

Eric Barber Vigesaa

Paul Nathan Bennett

Christopher Bruce Weare

Li-wei He

Allan Folting

Fileno A Alleva

C James MacLennan

Mei-Yuh Hwang

Usama M Fayyad

Mohammad Mahdian

Pamela Bhattacharya

Christian Herwarth Borgs

Ran Gilad-Bachrach

Jennifer Tour Chayes

Anton Mityagin

Ahmad Bilal

John R Bennett

Paul B Koch

Jeffrey Bernhardt

Brendan John Frey

Mikhail Bilenko

Aparna Lakshmiratan

Christopher D Payne

Ojiakonobi Udezue

Oleksandr Polozov

Ashis Kumar Roy

Mikhail Aleksandrovich Royzner

Thierry D'Hers

Amin Saberi

Timothy Seung Yoon Paek

Puyang Xu

Christopher Peter Kleynhans

Ajit Paul Singh

Asela Jeevaka Ranaweera Gunawardana

A document re-finding system generates embeddings for concept clips provided by a user, the concept clips defining a concept for searching for content of interest to the user in a plurality of documents previously seen by the user. The re-finding system determines semantic relationships between the concept and document clips, related to respective document among the plurality of documents, based on the concept embedding and embeddings generated for the document clips. A graphical user interface depicting the semantic relationships is rendered to the user and is operable to enable re-finding a document, among the plurality of documents, having the content of interest to the user.

Document re-finding system

A method and system for predicting an intended time interval for a content segment may include receiving a request for natural language processing (NLP) of the content segment, the content segment including one or more temporal expressions, accessing contextual data associated with each of the one or more temporal expressions, decoding the content segment into a program that describes a temporal logic of the content segment based on the one or more temporal expressions, evaluating the program using the contextual data to predict an intended time interval for the content segment, and providing the intended time interval as an output.

Intelligent interpretation of temporal expressions

In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogeneous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units. To optimize machine training, user input may be received, such that the system may intelligently augment the user's exploratory research task based on the semantic coherence of the content processed from information units and associated user behavior.

Schema augmentation system for exploratory research

A training system is described herein for generating a prediction model that relies on a feature space with reduced dimensionality. The training system performs this task by producing partitions, each of which corresponds to a subset of aspect values (where each aspect value, in turn, may correspond to one or more attribute values). The training system then produces instances of statistical information associated with the partitions. Each instance of statistical information therefore corresponds to feature information that applies to a plurality of aspect values, rather than a single aspect value. The training system then trains the prediction model based on the feature information. Also described herein is a prediction module that uses the prediction model to make predictions in various online contexts.

Predicting behavior using features derived from statistical information

Technologies are described herein for active machine learning. An active machine learning method can include initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation, refining a capacity of a target machine learning model based on the active machine learning, and retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model. Additionally, the target machine learning model is a limited-capacity machine learning model according to the description provided herein.

Active machine learning

Technologies for an application reputation service to assist users with minimizing their computerized machines' exposure to and infection from malware, including an application reputation service that contains the reputations for elements (e.g., applications) that are known to be non-malicious as well as those known to be malicious. In one example, when a user attempts to install or execute a new application, the service is queried by the user's machine with a set of identities for the element. The service determines the reputation of the application by referencing a knowledge base of known reputations and returns an indication (e.g., an overall rating, or a flag) of how safe that application would be to install and run on the user's computer.

Application reputation service

Systems and methods of pre-launching applications in a computer system, said applications being likely to be activated by a user from a terminated and/or suspended process state, are disclosed. The pre-launching of an application may be based on the assessed probability of the application being activated&#x2014;as well as the level of availability of system resources to affect such pre-launching. Applications may be pre-launched based on these and other conditions/considerations, designed to improve the user's experience of a quick launch of applications in the background. Several prediction models are presented to provide a good estimate of the likelihood of an application being activated by a user. Such prediction models may comprise an adaptive predictor (based on past application usage situations) and/or a switch rate predictor (based on historic data of an application being switched and, possibly, having a decay rate applied to such switch rate measure).

Predictive pre-launch for applications

Architecture that monitors interaction data (e.g., search queries, query results and click-through rates), and provides users with links to other users that fall into similar categories with respect to the foregoing monitored activities (e.g., providing links to individuals and groups that share common interests and/or profiles). A search engine can be interactively coupled with one or more social networks, and that maps individuals and/or groups within respective social networks to subsets of categories associated with searches. A database stores mapped information which can be continuously updated and reorganized as links within the system mapping become stronger or weaker. The architecture can comprise a social network system that includes a database for mapping search-related information to an entity of a social network, and a search component for processing a search query for search results and returning a link to an entity of a social network based on the search query.

Search engine that identifies and uses social networks in communications, retrieval, and electronic commerce

Systems and methods allow an on-line game to extract information relevant to a specific need of a game platform or service platform. The specific need relates to management and use of digital content, and is addressed by designing and playing an on-line collaborative game. The rules of the game intend to solve a specific task dictated by the specific need. Players' responses to the game generate a wealth of information related to a specific task objective, such as ranking, sorting, and evaluating a set of digital content items. To compel participation in a game, players can be rewarded with monetary value rewards. As a game illustration, an image selection game (ISG) that exploits human contextual inference is described in detail. The information extracted from ISG is a list of key-image associations, relevant for the task of image sorting and ranking.

Information collection during game play

Described is a technology for measuring the similarity between two objects (e.g., documents), via a framework that learns the term-weighting function from training data, e.g., labeled pairs of objects, to develop a learned model. A learning procedure tunes the model parameters by minimizing a defined loss function of the similarity score. Also described is using the learning procedure and learned model to detect near duplicate documents.

Learning element weighting for similarity measures

Data management in social networks

A method of generating a decision graph from event stream data, wherein the event stream data includes a plurality of events, and each event includes an associated time stamp, includes generating decision nodes for the graph, wherein the decision nodes each comprise a question having a temporal element. The method includes generating leaf nodes for the graph, wherein the leaf nodes each comprise a rate parameter, and iteratively splitting and merging nodes in the graph in order to maximize a measure of purity of outcomes in resulting nodes.

Using event stream data to create a decision graph representing a race participant where leaf nodes comprise rates defining a speed of the race participant in a race simulation

Two methods for measuring keyword-document relevance are described. The methods receive a keyword and a document as input and output a probability value for the keyword. The first method is a similarity-based approach which uses techniques for measuring similarity between two short-text segments to measure relevance between the keyword and the document. The second method is a regression-based approach based on an assumption that if an out-of-document phrase (the keyword) is semantically similar to an in-document phrase, then relevance scores of the in and out-of document phrases should be close to each other.

Consistent phrase relevance measures

A human interactive puzzle (HIP) authorization architecture where keyed and animated puzzles are executed by HIP players which are distinct and obfuscated to the point where breaking a single player is a relatively costly operation. A key is created in response to a request for a service, a HIP player is created based on the key, and a small installation executable is created that expands during installation to produce a computationally expensive data structure on the client relative to verification of the solution at the server. Thus, copying of the player or relay of the puzzle to a third system requires more time than allowed to receive the solution at the server.

Keyed human interactive proof players

Growing community of inventors

Kirkland, WA, United States of America

Christopher A Meek