Microsoft Technology Licensing, LLC

Opendesign, Inc.

Sanjeev Katariya

William Ramsey

Qi Yao

Jianfeng Gao

Jun Liu

William P Jones

Magdi Morsi

Wai Ho Au

Ying Sun

Alexandre Bereznyi

Edward K Y Jung

Pyungchul Kim

Steve G Bush

Thomas Charles Butcher

Zijian Zheng

Zhanliang Chen

Alok Sinha

Adwait Ratnaparkhi

Sin Lew

Yang Xu

Fajen Kyne

Jonas Barklund

Bradley Ray Green

Scott Sovine

Nina Sarawgi

Rob Bearman

Sami Khoury

An administrator of an enterprise storage set may be tasked with storing a large number and variety of data sets on a large number and variety of storage components. However, the manual selection of a physical schema by an administrator may be time-consuming, may generate inefficient physical schemata, and may not be easily reevaluated as the data sets and storage set change. Presented herein are techniques for automatically determining a physical schema by comparing the storage factors of each data set (e.g., data size, relationships with other data sets, and usages of the data set by users) with the storage capabilities of the storage components, selecting a suitable storage component, and implementing the storage of the data set on the storage component. An embodiment of these techniques may thereby achieve an automated identification of a physical schema with improved efficiency and flexibility of the physical schema while conserving administrative resources.

Automatically matching data sets with storage components

Systems and methods for dynamically managed data retention are described. The system comprises a tiered framework having a plurality of namespaces. The namespaces are configured by a user to have selected data retention attributes. Data including a manifest may be received by the system, processed, and directed to a namespace based upon the manifest. Data storage partitions may be created automatically in association with a namespace, and the data partitions may be assigned partition attributes. Data in a storage partition may be migrated automatically to another namespace based on the partition attributes. Code necessary for creating storage partitions and migrating data is generated by the data management system.

Data retention component and framework

A method and system for mapping logical identifiers to physical identifiers is provided. In one embodiment, a logical routing system allows each application, or more generally entity (e.g., user of an application), to register its logical identifier to physical identifier mapping when the application starts executing on a computer. To send a message to an application identified by a logical identifier, a client program uses the registered mapping to identify the physical identifier of the computer. If an application later starts executing on a different computer, then the application can register a different mapping.

Application platform

A task framework and a semantic reasoning engine are combined to provide a scalable mechanism for dealing with extremely large numbers of widgets, allowing users to both find a widget and automatically fill-in whatever functionality is available on the widget. Calling applications are employed to obtain task information from each widget. The calling application also receives user queries that can be resolved by a widget. A task reasoning process based on an adaptive semantic reasoning engine utilizes the task information to select a widget best suited to respond to a user's query. The task reasoning process can also be employed to determine 'best-guess' slot filling of the selected widget. The calling application can then invoke the selected widget and, if available, fill appropriate slots with information to facilitate user interaction with the selected widget. Instances can be client- and/or server-side based.

Widget searching utilizing task framework

A task system and method are provided. The system provides an automated approach for task creation, maintenance and/or execution. The system includes a browser that receives search results and at least one task associated with a query from a search engine. The system further includes a browser helper object that binds to the browser at runtime. The browser helper object provides information associated with a user's action with respect to the search results and/or at least one task. The information can be employed as feedback to update model(s) (e.g., query classification model(s) and/or slot-filling model(s)) of a semantic reasoning component that retrieves task based, at least in part, upon user query(ies).

Automatic task creation and execution using browser helper objects

A task-based advertisement system and method are provided. The system employs high-order concepts (e.g., booking a flight, checking stock quotes etc.) embodied in 'task(s)' which can then be bid upon by advertisers. The task(s) employed by the system are based upon a semantic solution to a natural-language query. The system includes a search engine that is capable of serving content in response to user query(ies). The system further includes a task server that can include hardware and/or software to retrieve task(s) in response to user query(ies). The task(s) retrieved by the task server can be presented to advertiser(s) who can bid on the task(s).

Employment of task framework for advertising

Provided is an adaptive semantic reasoning engine that receives a natural language query, which may contain one or more contexts. The query can be broken down into tokens or a set of tokens. A task search can be performed on the token or token set(s) to classify a particular query and/or context and retrieve one or more tasks. The token or token set(s) can be mapped into slots to retrieve one or more task result. A slot filling goodness may be determined that can include scoring each task search result and/or ranking the results in a different order than the order in which the tasks were retrieved. The token or token set(s), retrieved tasks, slot filling goodness, natural language query, context, search result score and/or result ranking can be feedback to the reasoning engine for further processing and/or machine learning.

Adaptive semantic reasoning engine

A computer-implemented method of performing named entity recognition in a client-server environment includes providing a first named entity recognition module operable with a client machine in the client-server environment and a second named entity recognition module operable with a server in the client-server environment. The method also includes performing named entity recognition on the client machine to identify one or more domain dependent named entities in a set of tokens and data assessable to the client machine and performing named entity recognition on the server to identify one or more domain independent named entities in the set of tokens and data assessable to the server. A task is completed using at least information related to the identified named entities from the client machine and the server.

Distributed named entity recognition architecture

A system for dynamically updating user accessible features of a software application on a client computer has a user interface, a local usage data file, and a data mining engine. The user interface is adapted to receive operator inputs. The local usage data file is adapted to store usage information corresponding to the operator inputs. The data mining engine is adapted to process the stored usage information and to generate local adjustments to a user interface of the software application based on the operator inputs. In one embodiment, a server is adapted to receive usage data from a plurality of application instances on a plurality of client computers and to generate global adjustments based on the received usage data. In one embodiment, the system has a merge feature adapted to blend and resolve conflicts between local and global adjustments to generate an interface adjustment for the user interface.

Adaptive systems and methods for making software easy to use via software usage mining

A context modeling architecture that includes a context representation portion, which adapted to represent context as features, is provided. The features are specifiable at runtime of an application including the context representation portion.

Context modeling architecture and framework

The subject invention relates to systems and methods that employ automated learning techniques to database and information retrieval systems in order to facilitate knowledge capabilities for users and systems. In one aspect, an adaptive information retrieval system is provided. The system includes a database component to store structured and unstructured data values. A search component queries the data values from the database, wherein a learning component associated with the search component or the database component is provided to facilitate retrieval of desired information.

High scale adaptive search systems and methods

A 'Natural Language Script Interface' (NLSI), provides an interface and query system for automatically interpreting natural language inputs to select, execute, and/or otherwise present one or more scripts or other code to the user for further user interaction. In other words, the NLSI manages a pool of scripts or code, available from one or more local and/or remote sources, as a function of the user's natural language inputs. The NLSI operates either as a standalone application, or as a component integrated into existing applications. Natural language inputs may be received from a variety of sources, and include, for example, computer-based text or voice input, handwriting or text recognition, or any other human or machine-readable input from one or more local or remote sources. In various embodiments, machine learning techniques are used to improve script selection and processing as a function of observed user interaction with selected scripts.

Natural language interface for driving adaptive scenarios

Word-breaking of a query from a client machine in a client-server environment includes determining whether to use a first word breaking module operable with a client machine in the client-server environment and/or a second word breaking module operable with a server in the client-server environment.

Client-server word-breaking framework

The subject disclosure pertains to systems and methods for performing natural language processing in which natural language input is mapped to a task. The system includes a task interface for defining a task, the associated data and the manner in which the task data is interpreted. Furthermore, the system provides a framework that manages the tasks to facilitate natural language processing. The task interface and framework can be used to provide natural language processing capabilities to third party applications. Additionally, the task framework can learn or be trained based upon feedback received from the third party applications.

Adaptive task framework

Property store information and an aggregation of a plurality of ranking mechanisms, including a learning mechanism, are leveraged to provide performant query results with increased user relevancy. The learning mechanism permits query feedback to be accepted to facilitate in optimizing user relevance. This mechanism can also be incorporated with traditional Information Retrieval (IR) components, each supplying independent ranking to a relevance aggregation function that determines relevancy at a high level. This precludes diminishing the value of query feedback that occurs when the data is fed into traditional IR algorithms. By allowing the query feedback to maintain its proper weighting and utilizing scope and bias capabilities of the property store information, relevance increases in a highly performant manner.

Growing community of inventors

Bellevue, WA, United States of America

Sanjeev Katariya