Abstract: A method of tracking execution of activities in a computing environment in which events in an activity are recorded along with an activity identifier uniquely identifying the activity and tying the events to the activity. To track interactions between activities, a correlation identifier may be generated and transferred between the interacting activities as part of the interaction. For each of the activities participating in the interaction, information on an event relating to the interaction is recorded along with the correlation identifier. The correlation identifier thus allows uniquely identifying each interaction which may be used to synchronize streams of events within the activities at points of their interaction. Activities may interact across any boundary, including a network.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The subject disclosure pertains to systems and methods for performing natural language processing in which tokens are mapped to task slots. The system includes a mapper component that generates a lattice representing possible interpretations of the tokens, a decoder component that creates a ranked list of paths traversing the lattice, a scorer component that generates scores used to rank paths and post-processing components that format the paths for use by other software. Each of these components may be independent, such that the component may be modified or replaced without affecting the remaining components. This allows a variety of different mathematical models and algorithms to be tested or deployed without requiring changes to the remainder of the system.

Abstract: A semantic and speech component provides a user interface for interaction with a user or author, and handles interactions with speech subsystems and semantic subsystems, so the user or author is not required to know the idiosyncrasies of each of those subsystems.

Abstract: 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.

Abstract: An adaptive shared infrastructure that can be easily utilized to enable natural interaction between user(s) and machine system(s) is provided. Additionally, the novel innovation can provide interactive techniques that produce accurate intent-to-action mapping based upon a user input. Further, the innovation can provide novel mechanism by which assets (e.g., documents, actions) can be authored. The authoring mechanisms can enable the generation of learning models such that the system can infer a user intent based at least in part upon an analysis of a user input. In response thereto, the system can discover an asset, or group of assets based upon the inference. Moreover, the innovation can provide a natural language interface that learns and/or adapts based upon one or more user input(s), action(s), and/or state(s).

Abstract: A form filler system and method are provided. The system can allow a user to fill out forms quickly using natural language. A user can navigate to a particular web site and type a natural language query into a text input box. Based, at least in part, upon the query, the system can automatically fill fields in a form associated with a web site. The system includes an input component that receives a natural language query from a user (e.g., a text input box). The system further includes a form filler engine that examines form(s) (e.g., web pages) and extracts the name(s) of input field(s) and possible inputs value(s), if any, in those fields. The form filler engine provides a way to extract the field name(s) from a web page and to fill in values of the form.

Abstract: 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).

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.