Abstract: A conversation user interface enables patients to better understand their healthcare by integrating diagnosis, treatment, medication management, and payment, through a system that uses a virtual assistant to engage in conversation with the patient. The conversation user interface conveys a visual representation of a conversation between the virtual assistant and the patient. An identity of the patient, including preferences and medical records, is maintained throughout all interactions so that each aspect of this integrated system has access to the same information. The conversation user interface presents allows the patient to interact with the virtual assistant using natural language commands to receive information and complete task related to his or her healthcare.

Abstract: A conversation user interface enables users to better understand their interactions with computing devices, particularly when speech input is involved. The conversation user interface conveys a visual representation of a conversation between the computing device, or virtual assistant thereon, and a user. The conversation user interface presents a series of dialog representations that show input from a user (verbal or otherwise) and responses from the device or virtual assistant. Associated with one or more of the dialog representations are one or more graphical elements to convey assumptions made to interpret the user input and derive an associated response. The conversation user interface enables the user to see the assumptions upon which the response was based, and to optionally change the assumption(s). Upon change of an assumption, the conversation GUI is refreshed to present a modified dialog representation of a new response derived from the altered set of assumptions.

Abstract: This disclosure describes techniques and architectures for evaluating conversations. In some instances, conversations with users, virtual assistants, and others may be analyzed to identify potential risks within a language model that is employed by the virtual assistants and other entities. The potential risks may be evaluated by administrators, users, systems, and others to identify potential issues with the language model that need to be addressed. This may allow the language model to be improved and enhance user experience with the virtual assistants and others that employ the language model.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: A conversation user interface enables users to better understand their interactions with computing devices, particularly when speech input is involved. The conversation user interface conveys a visual representation of a conversation between the computing device, or virtual assistant thereon, and a user. The conversation user interface presents a series of dialog representations that show input from a user (verbal or otherwise) and responses from the device or virtual assistant. Associated with one or more of the dialog representations are one or more graphical elements to convey assumptions made to interpret the user input and derive an associated response. The conversation user interface enables the user to see the assumptions upon which the response was based, and to optionally change the assumption(s). Upon change of an assumption, the conversation GUI is refreshed to present a modified dialog representation of a new response derived from the altered set of assumptions.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: Techniques and architectures for implementing a team of virtual assistants are described herein. The team may include multiple virtual assistants that are configured with different characteristics, such as different functionality, base language models, levels of training, visual appearances, personalities, and so on. The characteristics of the virtual assistants may be configured by trainers, end-users, and/or a virtual assistant service. The virtual assistants may be presented to end-users in conversation user interfaces to perform different tasks for the users in a conversational manner. The different virtual assistants may adapt to different contexts. The virtual assistants may additionally, or alternatively, interact with each other to carry out tasks for the users, which may be illustrated in conversation user interfaces.

Abstract: Techniques and architectures for implementing a team of virtual assistants are described herein. The team may include multiple virtual assistants that are configured with different characteristics, such as different functionality, base language models, levels of training, visual appearances, personalities, and so on. The characteristics of the virtual assistants may be configured by trainers, end-users, and/or a virtual assistant service. The virtual assistants may be presented to end-users in conversation user interfaces to perform different tasks for the users in a conversational manner. The different virtual assistants may adapt to different contexts. The virtual assistants may additionally, or alternatively, interact with each other to carry out tasks for the users, which may be illustrated in conversation user interfaces.

Abstract: Techniques and architectures for implementing a team of virtual assistants are described herein. The team may include multiple virtual assistants that are configured with different characteristics, such as different functionality, base language models, levels of training, visual appearances, personalities, and so on. The characteristics of the virtual assistants may be configured by trainers, end-users, and/or a virtual assistant service. The virtual assistants may be presented to end-users in conversation user interfaces to perform different tasks for the users in a conversational manner. The different virtual assistants may adapt to different contexts. The virtual assistants may additionally, or alternatively, interact with each other to carry out tasks for the users, which may be illustrated in conversation user interfaces.

Abstract: Techniques and architectures for implementing a team of virtual assistants are described herein. The team may include multiple virtual assistants that are configured with different characteristics, such as different functionality, base language models, levels of training, visual appearances, personalities, and so on. The characteristics of the virtual assistants may be configured by trainers, end-users, and/or a virtual assistant service. The virtual assistants may be presented to end-users in conversation user interfaces to perform different tasks for the users in a conversational manner. The different virtual assistants may adapt to different contexts. The virtual assistants may additionally, or alternatively, interact with each other to carry out tasks for the users, which may be illustrated in conversation user interfaces.

Abstract: Techniques for mapping actions and objects to tasks may include identifying a task to be performed by a virtual assistant for an action and/or object. The task may be identified based on a task map of the virtual assistant. In some examples, the task may be identified based on contextual information of a user, such as a conversation history, content output history, user preferences, and so on. The techniques may also include customizing a task map for a particular context, such as a particular user, industry, platform, device type, and so on. The customization may include assigning an action, object, and/or variable value to a particular task.

Abstract: A fan comprising: (a) an impeller, (b) a motor connected to the impeller so that during operation the motor drives the impeller to move a fluid; (c) an instrumentation assembly that includes one or more components for controlling one or more operations of the fan; (d) an interface in communication with the instrumentation assembly; wherein the interface is configured so that one or more programs, one or more signals, or both can be input into the fan after a final assembly of the fan so that the fan can be configured to perform a desired function at the time of installation into a final product, reconfigured for a different purpose than originally programmed, or both.

Abstract: Various embodiments are described for searching and retrieving documents based on a natural language input. A computer-implemented natural language processor electronically receives a natural language input phrase from an interface device. The natural language processor attributes a concept to the phrase with the natural language processor. The natural language processor searches a database for a set of documents to identify one or more documents associated with the attributed concept to be included in a response to the natural language input phrase. The natural language processor maintains the concepts during an interactive session with the natural language processor. The natural language processor resolves ambiguous input patterns in the natural language input phrase with the natural language processor. The natural language processor includes a processor, a memory and/or storage component, and an input/output device.

Abstract: A virtual assistant may communicate with a user in a conversational manner based on context. For instances, a virtual assistant may be presented to a user to enable a conversation between the virtual assistant and the user. A response to user input that is received during the conversation may be determined based on contextual values related to the conversation or system that implements the virtual assistant.

Abstract: A virtual assistant may communicate with a user in a natural language that simulates a human. The virtual assistant may be associated with a human-configured knowledge base that simulates human responses. In some instances, a parent response may be provided by the virtual assistant and, thereafter, a child response that is associated with the parent response may be provided.

Abstract: Virtual agents may be implemented on a wearable device. The wearable device may include an input device to receive input and a communication component to send the input to a computing device for processing and to receive a response for the input. The wearable device may also include an output device to output the response via the virtual agent as part of a conversation with a user.

Abstract: Various embodiments provide a tool, referred to herein as “Active Lab” that can be used to develop, debug, and maintain knowledge bases. These knowledge bases (KBs) can then engage various applications, technology, and communications protocols for the purpose of task automation, real time alerting, system integration, knowledge acquisition, and various forms of peer influence. In at least some embodiments, a KB is used as a virtual assistant that any real person can interact with using their own natural language. The KB can then respond and react however the user wants: answering questions, activating applications, or responding to actions on a web page.

Abstract: A conversation user interface enables patients to better understand their healthcare by integrating diagnosis, treatment, medication management, and payment, through a system that uses a virtual assistant to engage in conversation with the patient. The conversation user interface conveys a visual representation of a conversation between the virtual assistant and the patient. An identity of the patient, including preferences and medical records, is maintained throughout all interactions so that each aspect of this integrated system has access to the same information. The conversation user interface presents allows the patient to interact with the virtual assistant using natural language commands to receive information and complete task related to his or her healthcare.

Abstract: Conversation user interfaces that are configured for virtual assistant interaction may include contextual interface items that are based on contextual information. The contextual information may relate to a current or previous conversation between a user and a virtual assistant and/or may relate to other types of information, such as a location of a user, an orientation of a device, missing information, and so on. The conversation user interfaces may additionally, or alternatively, control an input mode based on contextual information, such as an inferred input mode of a user or a location of a user. Further, the conversation user interfaces may tag conversation items by saving the conversation items to a tray and/or associating the conversation items with indicators.

Abstract: Ambiguous input of a user received during an interactive session with a virtual agent may be processed. The virtual agent may be presented via a computing device to facilitate the interactive session with the user. The user may provide the ambiguous input, which is processed to determine a response to the input. The virtual agent may provide the response to the user. The virtual agent may also carry out a goal-based dialogue where a goal to be accomplished is identified. The virtual agent may prompt the user for information related to the goal.