Microsoft Technology Licensing, LLC

At+t Corp.

Lucent Technologies Inc.

Other

Micorsoft Corpration

Yi-Min Wang

Chad E Verbowski

Yennun Huang

Shuo Chen

Chao Liu

Jiahe Helen Wang

Ming Ma

Douglas Reed Beck

Pi-Yu Chung

Daniel Ron Simon

Zheng Zhang

Chun Liang Yuan

Wilfred G Russell

Roussi A Roussev

Qixiang Sun

Chandra M Kintala

Wilf G Russell

Anish K Arora

Om P Damani

Wei-Ying Ma

Galen Clyde Hunt

Albert Gordon Greenberg

Ming Zhang

Ji-Rong Wen

John David Dunagan

Lili Qiu

Li-wei He

Emre Mehmet Kiciman

Kiem-Phong Vo

Chun-Kai Wang

Xiaoxin Yin

Vijay K Garg

Zhichun Li

Wesley K Fuchs

Paramvir Bahl

Zheng Chen

Ryen William White

Venkata N Padmanabhan

Alessandro Forin

Ahmed Hassan Awadallah

Aleksey Sinyagin

Jinliang Fan

Douglas L Beck

Bradford Daniels

Wenzhao Tan

Jun Xu

Chao Zhou

Aaron Roy Johnson

Ralf Sasse

Scott Field

Kuansan Wang

Susan Theresa Dumais

Li Erran Li

Yun Lin

Per-Ake Larson

Jacob Rubin Lorch

Mihai Costea

Weizhu Chen

Patrick Pantel

Alexey A Polyakov

Pengcheng He

Hung-Chih Yang

Juhan Lee

Helen Jiahe Wang

Glenn Stephen Fowler

David Andrew Ross

Matthew Brigham Hall

Demetrios Achlioptas

Jiayuan Huang

Mingyu Wang

Jianwen Zhang

Chih-Mei Lin

Junyan Chen

Gautam Das

Fang Liu

Junhua Wang

Brad Daniels

Yu Zhao

Yuting Sun

Xiaogang Liu

Katherine Winant Osborne

Changhong Yuan

Vijay Nair

Vincent R Orgovan

Yang Ye

Gretchen L Loihle

Ryan Frederick Stewart

Fan Guo

Samuel King

John D Dungan

David C Ladd

Shichao Hu

Binh Dou Vo

Alessandro Florin

Jiantao Sun

Roger P Wattenhofer

Yuangang Tu

Yi-Chin Tu

Alexander Borisovitch Romanovsky

Chao Zhaou

A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Interactive dialog training and communication system using artificial intelligence

Systems and devices to perform knowledge-driven dynamic conversations and select content within automated agents such as chatbots and virtual assistants are disclosed. In an example, operations to facilitate a knowledge-based conversation session with a human user using an automated agent include: receiving a conversational input regarding a support issue; analyzing the conversational input to determine an intent and applicable entity properties associated with the intent; performing a multi-turn conversation to identify a solution using the intent and the applicable entity properties, by exchanging iterative questions and answers between the automated agent and the user to dynamically recalculate applicability of the solution to the support issue; and outputting information associated with the identified solution. In further examples, the operations include a dynamic application of a solution policy and a diagnosis policy in the multi-turn conversation, to determine whether to deliver a solution or ask diagnosis questions.

Knowledge-driven dialog support conversation system

Technologies described herein relate to receiving data from a social networking application, wherein the data is generated by a first individual, and executing an entity extraction algorithm to extract at least one entity from the received data. A hyperlink is displayed on a computing device utilized by a second individual, wherein the hyperlink is based at least in part upon the at least one entity, and the hyperlink is configured to direct the first individual to a search engine and is further configured to cause the search engine to execute a search that pertains to the at least one entity.

Generating activities based upon social data

Various technologies pertaining to exploratory suggestions are described herein. A computer-implemented graph is constructed, where the graph includes nodes that are representative of aspects and edges that are representative of associations between aspects. An aspect is representative of a sub-topic of a topic or a sub-task of a task. The computer-implemented graph is learned based upon content of search logs, and is used to output exploratory suggestions, where a user is exploring a topic or attempting to complete a multi-step task.

Constructing a graph that facilitates provision of exploratory suggestions

A system is described herein that includes a preference deriver component that receives a predefined preference rule that indicates a hierarchy pertaining to entities belonging to a domain, wherein each of the entities has attributes and values for such attributes corresponding thereto, and wherein the preference deriver component outputs preferences between various subsets of entities based at least in part upon the preference rule. The system also includes a learning component that learns a computer-implemented ranker component that is configured to rank the entities belonging to the domain, wherein the learning component learns the computer-implemented ranker based at least in part upon the preferences between the various subsets of the entities output by the preference deriver component.

Learning a ranker to rank entities with automatically derived domain-specific preferences

Technologies pertaining to automatically identifying sets of query attribute values that are highly correlative with user dissatisfaction with a search engine are described. Dissatisfied queries are automatically identified through analysis of search logs, wherein a dissatisfied query is a query submitted to a search engine by a user, wherein the user was dissatisfied with search results provided by the search engine responsive to receipt of the query. Sets of query attribute values that are highly correlated with dissatisfied queries, and thus user dissatisfaction, are automatically identified based at least in part upon the identifying of the dissatisfied queries. Subsequent to identifying a set of query attribute values, a segment-specific ranker is learned that is configured to rank search results responsive to receipt of a query with the set of query attribute values, wherein the segment-specific ranker outperforms a general purpose ranker for queries having the set of query attribute values.

Identifying dissatisfaction segments in connection with improving search engine performance

A network can be explored to investigate exploitive behavior. For example, network sites may be actively explored by a honey monkey system to detect if they are capable of accomplishing exploits, including browser-based exploits, on a machine. Also, the accomplishment of exploits may be detected by tracing events occurring on a machine after visiting a network site and analyzing the traced events for illicit behavior. Alternatively, site redirections between and among uniform resource locators (URLs) may be explored to discover relationships between sites that are visited.

Honey monkey network exploration

The claimed subject matter provides a system or method for web crawling hidden files. An example method includes loading a web page with a browser agent, and executing any dynamic elements hosted on the web page using the browser agent to insert pre-determined values. A list of form controls may be retrieved from the web page using the browser agent, and the controls may be analyzed using a driver component. Form control values may be sent from the driver component to the browser agent, and an event may be submitted to the web page by the browser agent or scripted content may be run to trigger operations on the web page corresponding to the form control values. A URL may be generated for various form control values using a generalizer.

Interactive web crawler

A crowdsourcing data structuring system and method for capturing unstructured data from the Web and adding structure by placing the data in a document that is accessible by others in a cloud computing environment. Using crowdsourcing, the unstructured data is annotated, amended, and verified to add structure to the unstructured data. An anchor and update module convert the data to a pointer that links the document to the data at an information source and stores the pointer in the document rather than the data itself. The data displayed in the document is updated whenever the information source is updated. A contribution module allows users to add data to the document, a validation module allows users to determine the validity of the data linked to in the document, and an expert ranking module allows users to rank the expert or contributor of the data in the document.

Structuring unstructured web data using crowdsourcing

Embodiments of automated cloud service performance prediction are disclosed. The automated cloud service performance prediction includes extracting one or more dependency relationships for each web object in the webpage. The prediction further includes determining an original performance metric value and original timing information associated with a page loading of a webpage. The prediction also includes simulating a page loading of the webpage based on the adjusted timing information and the dependency relationships to estimate a new performance metric value associated with the simulated page loading of the webpage. The prediction additionally includes comparing the original performance metric value to the new performance metric value to determine whether the adjusted timing information increased or decreased the new performance metric value relative to the original performance metric value.

Web page load time prediction and simulation

An exemplary system for monitoring search spam and protecting against search spam includes a self-monitoring subsystem to uncover spam patterns and a self-protection subsystem to protect against spam by providing spam-related information to strengthen a relevance ranking algorithm. An exemplary architecture for monitoring search spam includes a first component to receive one or more spammer targeted keywords and to search, scan and analyze URLs based at least in part on the one or more spammer targeted keywords, a second component to receive one or more URLs from the first component and to verify one or more of these URLs as a spam URL and a third component to collect spammer targeted keywords associated with one or more spam URLs and to provide one or more of the spammer targeted keywords to the first component.

Search ranger system and double-funnel model for search spam analyses and browser protection

The claimed subject matter provides a system or method for web crawling hidden files. An exemplary method includes loading a web page with a browser agent, and executing any dynamic elements hosted on the web page using the browser agent to insert pre-determined values. A list of form controls may be retrieved from the web page using the browser agent, and the controls may be analyzed using a driver component. Form control values may be sent from the driver component to the browser agent, and an event may be submitted to the web page by the browser agent or scripted content may be run to trigger operations on the web page corresponding to the form control values. A URL may be generated for various form control values using a generalizer.

Methods, systems, and computer-readable media for evaluating the quality of text within online advertisements using output from a language model are provided. The output from the language model may be used by a machine-learning algorithm to generate a quality score for an individual advertisement. The quality score may be used to filter out advertisements with poor text quality or to tax or penalize an advertisement within an online auction. The ad quality scores may also be used to rank or score advertisers that submit the ads. In one embodiment, the advertiser's quality score is combined with an individual ad's quality score to create a final score, which is used to evaluate the advertisement. The advertiser rank/score and ad quality score may be communicated to an advertiser as advertiser feedback.

Ad copy quality detection and scoring

A method described herein includes acts of receiving data from a social networking application, wherein the data is generated by a first individual, and executing an entity extraction algorithm to extract at least one entity from the received data. The method also includes automatically causing a hyperlink to be displayed on a computing device utilized by a second individual, wherein the hyperlink is based at least in part upon the at least one entity, wherein the hyperlink is configured to direct the first individual to a search engine and is further configured to cause the search engine to execute a search that pertains to the at least one entity.

Defeating click-through cloaking includes retrieving a search results page to set a browser variable, inserting a link to a page into the search results page and clicking through to the page using the inserted link. Investigating cloaking includes providing script associated with a suspected spam URL, modifying the script to de-obfuscate the script and executing the modified script to reveal cloaking logic associated with the script.

Search spam analysis and detection

Growing community of inventors

Bellevue, WA, United States of America

Yi-Min Wang