Microsoft Technology Licensing, LLC

Harish Jayanti

Christopher Walter Anderson

Eugene Shinn

Jayaram N M Nanduri

Abhishek Prateek

Ling-Ya Huang

Srinivasa Raghavan Santhanam

Gaurav Bindlish

Saliha Azzam

Cristian M Matesan

Brent Edward Ford

Woo Ram Lee

Danilo Landucci Benzatti

Rajasi Saha

Kfir Ami-ad

Peter John Richards

Keith Robert Kinnan

Jagannatha Raju Dantuluri

Naganandhini Kohareswaran

Xiaoguang Qi

Derrick Yeqiang Quan

Andrew N Dwersteg

Julio Cesar Gutierrez

Abhishek Mondal

Shiyuan Zhao

Jonathan I Reyes Spezzia

Mark D Stumpf

Aayushi Chowdhary

Jesse H Stein

Khushi Hitendra Patel

Fengna Gu

Amber Jerica McRae

William P Dinkuhn

Paul Konasewich

Chad A Zanonie

Michael Y Moon

Sarvesh Surana

Mitali Prakash Shenoy

Conner Brennick

Michael J Krejcik

Einat Hazan

Christopher J Mallery

Magdalena Dakeva

Willis Fung

Payel Hazra

Antoine Haas

Shilpi Sinha

Lu Han

Brian H Burks

A system for creating a target audience group includes receiving a request from a user interface screen, to create a target audience group from among users of an organization associated with a cloud computing environment, where the request including selected values for a plurality of categories of data associated with the users of the organization. Upon receiving the request; the method creates an audience targeting definition for the target audience group that includes the plurality of categories of data and their selected values and stores the audience targeting definition to a data store. Next, one or more data sources of the cloud computing environment are queried to identify users for which values for the categories of data corresponds with the selected values and a list of those users is stored in a different data store. The list of the users is then made available to a plurality of applications of the cloud computing environment for communicating with or presenting an experience to the members of the target audience group.

Unified audience targeting system

Methods, systems, apparatuses, and computer program products are described for providing content recommendations to a requestor. In implementations, user event records associated with media items and content types may be stored, for instance, in an event catalog. A set of user scores may be generated based on a user-content score model and the user event records, where each generated user score represents an affinity between a user and a corresponding content type of the plurality of content types. A set of title scores may also be generated for a media item based on a title-content score model, where each generated title score represents an affinity between the media item and a corresponding content type. In response to a request, the sets of scores may be combined to generate a set of content recommendations to provide to the requestor.

Multi-content recommendation system combining user model, item model and real time signals

Systems and methods for providing enterprise-level application content customization are described herein. A method includes executing an enterprise application (from a suite of enterprise applications that are utilized by the enterprise) on a remote computing system operated by an enterprise administrator. The method includes surfacing a content customization UI on a display device of the remote computing system during execution of the enterprise application. The content customization UI includes UI elements that enable the specification of parameters for providing enterprise-specific customized content during execution of enterprise application(s) from the suite of enterprise applications on remote computing system(s) operated by enterprise user(s). The method also includes receiving, via the content customization UI, user input including the specification of parameters and generating the customized content based on the specified parameters. The method further includes surfacing the customized content on display device(s) of the remote computing system(s) during execution of the enterprise application(s).

Natively-integrated application content customization for enterprises

Systems and methods provide a customer engagement platform experimentation framework. Experiments are created to determine which variation of a particular promotion (i.e., an experiment) is most effective. Initially, users are split into buckets that define whether a particular user is in a control group or a treatment group for a particular experiment. Users are assigned into buckets differently for unrelated experiments and similarly for related experiments. This ensures the users are in either the control group or the treatment group for all related experiments. Key performance indicators (KPIs) of users that experienced the experiment are compared with KPIs of users that did not experience the experiment. An assessment of the experiment can then be provided based on the compared KPIs.

Customer engagement platform experimentation framework

Methods, computer systems, and computer-storage media are provided for increasing the efficiency of a multi-tenant geospatial data index. Efficiency is increased by using a multi-tenant model for storing and serving the data, processing raw geospatial data received from tenants into a runtime-optimized format, and by partitioning tenant geospatial data into a processor memory portion and a file system memory portion. Efficiency is also increased by executing a staged upload of the processor memory portion and the file system memory portion to a subset of host machines in order to check for invalid data before uploading the data to the remaining host machines. Additionally, efficiency is increased by optimizing geospatial search queries using query filters stored in a query filter cache, and executing the query filters initially against the processor memory.

Efficient multi-tenant spatial and relational indexing

Geocoding architecture based on information retrieval. The geocoding information retrieval system operates on locations and geographic entities. The architecture uses parameters in addition to the query string, if available. The parameters provide contextual information that enables filtering and sorting the results. The additional parameters include market, culture, map view, and user location. A geocoding triage process uses the query and context information provided and output of a machine-learning parser to select the data-lookup geocoders for processing the query. A results processing component collects the results from the underlying data-lookup geocoders, merges the results into a single list after normalizing the scores, and then re-sorts the results list.

Geocoding using information retrieval

A full text indexing system is provided for processing content associated with data applications such as encyclopedia and dictionary applications. A build process collects data from various sources, processes the data into constituent parts, including alternative word sets, and stores the constituent parts in structured database tables. A run-time process is used to query the database tables and the results in order to provide effective matches in an efficient manner. Run-time processing is optimized by preprocessing all steps that are query-independent during the build process. A double word table representing all possible word pair combinations for each index entry and an alternative word table are used to further optimize runtime processing.

Content data indexing and result ranking

Content data indexing with content associations

A full text indexing system is provided for processing content associated with data applications such as encyclopedia and dictionary applications. A build process collects data from various sources, processes the data into constituent parts, including alternative word sets, and stores the constituent parts in structured database tables. A run-time process is used to query the database tables and the results in order to provide effective matches in an efficient manner. Run-time processing is optimized by preprocessing all steps that are query-independent during the build process. A double word table representing all possible word pair combinations for each index entry and an alternative word table are used to further optimize run-time processing.

Content data indexing

Prestructuring disparate content data types prior to receiving any access requests to provide fast retrieval, assembly, and delivery of content data. Source content data are obtained from a source data structure that is readily created and editable by an author who does not have programming skills. The source content data are transformed into a predefined output structure that enables efficient access to the content data over a network. Predefined classes and entities of metadata indicate subsets of the content data that are deliverable and indicate relationships between elements of the content data. The content data and controlling metadata are preformatted for display and stored in fields of the output structure. The content data are also cross referenced by a plurality of categories within various areas of interest to create a hierarchical structure of interrelationships that is stored in the output structure, enabling a user to browse through related content data.

Database build for web delivery

A method and system for searching index databases allows a user to search for specific information using high-level key words, questions, or sentences. The system includes three main segments: a searchable content database, a run time search component, and a user interface. The searchable content database includes a full text index and a lookup table. The search component includes a results list database, an exact match search, a natural language processor (NLP), and a full text search. Indexes, prioritized search tokens, and word clusters are combined to create a better search experience. A user's query is processed into prioritized clustered tokens using the NLP, token priority rules, and word clusters.

Method and system for searching index databases

Growing community of inventors

Redmond, WA, United States of America

Harish Jayanti