An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Traditional interactive applications are limited to using only the input that users explicitly provide. As users move away from traditional desktop computing environments and move towards mobile and ubiquitous computing environments, there is a greater need for applications to leverage from implicit information, or context. These types of environments are rich in context, with users and devices moving around and computational services becoming available or disappearing over time. This information is usually not available to applications but can be useful in adapting the way in which it performs its services and in changing the available services. Applications that use context are known as context-aware applications. 
This research in context-aware computing has focused on the development of a software architecture to support the building of context-aware applications. While developers have been able to build context-aware applications, they have been limited to using a small variety of sensors that provide only simple context such as identity and location. This dissertation presents a set of requirements and component abstractions for a conceptual supporting framework. The framework along with an identified design process makes it easier to acquire and deliver context to applications, and in turn, build more complex context-aware applications. 
In addition, an implementation of the framework called the Context Toolkit is discussed, along with a number of context-aware applications that have been built with it. The applications illustrate how the toolkit is used in practice and allows an exploration of the design space of context-aware computing. This dissertation also shows how the Context Toolkit has been used as a research testbed, supporting the investigation of difficult problems in context-aware computing such as the building of high-level programming abstractions, dealing with ambiguous or inaccurate context data and controlling access to personal context.

/pdf/providing-architectural-support-for-building-context-aware-1y6c5sg04e.pdf

Providing architectural support for building context-aware applications

A fundamental objective of human–computer interaction research is to make systems more usable, more useful, and to provide users with experiences fitting their specific background knowledge and objectives. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the “right” thing at the “right” time in the “right” way. Designers of collaborative human–computer systems face the formidable task of writing software for millions of users (at design time) while making it work as if it were designed for each individual user (only known at use time). User modeling research has attempted to address these issues. In this article, I will first review the objectives, progress, and unfulfilled hopes that have occurred over the last ten years, and illustrate them with some interesting computational environments and their underlying conceptual frameworks. A special emphasis is given to high-functionality applications and the impact of user modeling to make them more usable, useful, and learnable. Finally, an assessment of the current state of the art followed by some future challenges is given.

/pdf/user-modeling-in-human-computer-interaction-24ivw1rcou.pdf

User Modeling in Human–Computer Interaction

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

Systems and methods by which voice/data communications may occur in multiple modes/protocols are disclosed. In particular, systems and methods are provided for multiple native mode/protocol voice and data transmissions and receptions with a computing system having a multi-bus structure, including, for example, a TDM bus and a packet bus, and multi-protocol framing engines. Such systems preferably include subsystem functions such as PBX, voice mail and other telephony functions, LAN hub and data router. In preferred embodiments, a TDM bus and a packet bus are intelligently bridged and managed, thereby enabling such multiple mode/protocol voice and data transmissions to be intelligently managed and controlled with a single, integrated system. A computer or other processor includes a local area network controller, which provides routing and hub(s) for one or more packet networks. The computer also is coupled to a buffer/framer, which serves to frame/deframe data to/from the computer from TDM bus. The buffer/framer includes a plurality of framer/deframer engines, supporting, for example, ATM and HDLC framing/deframing. The buffer/framer is coupled to the TDM bus by way of a switch/multiplexer, which includes the capability to intelligently map data traffic between the buffer/framer and the TDM bus to various slots of the TDM frames. Preferably, a DSP pool is coupled to buffer/framer in a manner to provide various signal processing and telecommunications support, such as dial tone generation, DTMF detection and the like. The TDM bus is coupled to a various line/station cards, serving to interface the TDM bus with telephone, facsimiles and other telecommunication devices, and also with a various digital and/or analog WAN network services.

Systems and methods for multiple mode voice and data communications using intelligently bridged TDM and packet buses and methods for performing telephony and data functions using the same

A system and method causes a computer to detect and perform actions on structures identified in computer data. The system provides an analyzer server, an application program interface, a user interface and an action processor. The analyzer server receives from an application running concurrently data having recognizable structures, uses a pattern analysis unit, such as a parser or fast string search function, to detect structures in the data, and links relevant actions to the detected structures. The application program interface communicates with the application running concurrently, and transmits relevant information to the user interface. Thus, the user interface can present and enable selection of the detected structures, and upon selection of a detected structure, present the linked candidate actions. Upon selection of an action, the action processor performs the action on the detected structure.

System and method for performing an action on a structure in computer-generated data

An important function of an agent is to be “on the lookout” for bits of information that are interesting to its user, even if these items appear in the midst of a larger body of unstructured information. But how to tell these agents which patterns are meaningful and what to do with the result? Especially when agents are used to recognize text, they are usually driven by parsers which require input in the form of textual grammar rules. Editing grammars is difficult and error-prone for end users. Grammex l“Grammars by Example”r is the first direct manipulation interface designed to allow non-expert users to define grammars interactively. The user presents concrete examples of text that he or she would like the agent to recognize. Rules are constructed by an iterative process, where Grammex heuristically parses the example, displays a set of hypotheses, and the user critiques the system's suggestions. Actions to take upon recognition are also demonstrated by example.

Training agents to recognize text by example

Publisher Summary An important function of an agent is to be “on the lookout” for bits of information that are interesting to its user, even if these items appear in the midst of a larger body of unstructured information. The question is to decide which of these agents are meaningful and what is to be done with the result. Especially when agents are used to recognize text, parsers that require input in the form of textual grammar rules usually drive them. Editing grammar is difficult and error-prone for end users. Grammex (Grammars by Example) is the first direct manipulation interface designed to allow non-expert users to define grammars interactively. The user presents concrete examples of text that he or she would like the agent to recognize. An iterative process, in which Grammex heuristically parses the example and displays a set of hypotheses, constructs rules and the user critiques the system's suggestions. Actions to take upon recognition are also demonstrated by example.

A system comprises a structure editor, a structure library, an extractor and a user interface, which are stored in computer memory and create structures for recognizing patterns in computer text. The structure editor via the user interface enables a user to input a text string called a model pattern, and uses previously-created structures such as lexical category structures for parsing the model pattern into default definition items. Each default definition item includes attributes specifying the patterns recognizable by the definition item and relationships with other definition items. The structure editor enables and facilitates user modification of the default definition items by generating attribute choices for user selection and allowing a user to generate a new structure if none of the choices is adequate. The resulting definition items form a new definition of a new structure, which is stored in the structure library. The extractor locates a pattern in computer text by satisfying the conditions of a definition of a new structure.

System and method for creating pattern-recognizing computer structures from example text

Parsers are powerful tools for computer understanding of text, whether the language is a natural language or a formal language. To make the computational power of these tools fully available to an end user, a parser should be userextensible. Until now, a user who wished to control a parser was forced to write or edit a grammar, a text file containing rules. Editing grammars is often difftcult and error-prone for end users since the effect of writing specific rules, and interaction between rules, can often be unclear. Grammex [“Grammars by Example”] is the first direct manipulation interface designed to allow ordinary users to define grammars interactively. Instead of writing a grammar in an abstract rule language, the user presents concrete examples of text that he or she would like the parser to recognize. The user describes the text by selecting substrings, and choosing possible interpretations of the text from popup menus of suggestions heuristically computed by Grammex. Grammex compiles grammar rules that can be used as the input to a traditional parser. PARSERS AND GRAMMARS Parsers are beginning to be deployed as an integral part d the text editing facilities available across all computer applications. Examples are Apple Data Detectors [4] and the Intel Selection Recognition Agent [5]. Apple Data Detectors finds phone numbers, email addresses and URLs in free text These facilities allow automatic recognition of simple, commonly occurring text patterns such as e-mail addresses, URLs, or date formats. Typically, the set of patterns recognized by the parser is to be programmed by a highly expert user, a grammar writer skilled in computational linguistics. The end user is merely expected to invoke the parser and use its results. However, no set of patterns supplied by experts can be

Grammex: defining grammars by example

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System and method for performing an action on a structure in computer-generated data

Training agents to recognize text by example

Training agents to recognize text by example

System and method for creating pattern-recognizing computer structures from example text

Grammex: defining grammars by example