Verint Americas Inc.

Next It Corporation

Next It Innovation Labs, LLC

Tanya M Miller

Fred A Brown

Mark Zartler

Charles C Wooters

Megan Brown

Molly Q Brown

Richard Morris

Ian Roy Beaver

Bryan Michael Culley

Scott Buzan

Eli D Snavely

Verlie Thompson

Richard Stuart Morris

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.

Virtual assistant focused user interfaces

Virtual assistants intelligently emulate a representative of a service provider by providing variable responses to user queries received via the virtual assistants. These variable responses may take the context of a user's query into account both when identifying an intent of a user's query and when identifying an appropriate response to the user's query.

Method of using a virtual assistant

Techniques for interacting with a portion of a content item through a virtual assistant are described herein. The techniques may include identifying a portion of a content item that is relevant to user input and causing an action to be performed related to the portion of the content item. The action may include, for example, displaying the portion of the content item on a smart device in a displayable format that is adapted to a display characteristic of the smart device, performing a task for a user that satisfies the user input, and so on.

Interaction with a portion of a content item through a virtual assistant

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.

Apparatus, system, and method for natural language processing

Various embodiments are described which leverage techniques for breaking down critical ideas from an inputted phrase into concepts in order to provide a response that is more relevant to the inputted phrase. In this regard, concepts and/or concept patterns are utilized with information retrieval searching to provide more relevant and concise documents in response to an inputted phrase. In addition, concepts and/or concept patterns are utilized with respect to assessing information (e.g., documents) available in a knowledge base and building appropriate pre-defined responses to an inputted phrase.

Leveraging concepts with information retrieval techniques and knowledge bases

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.

Method and system for generating and correcting classification models

Conversation user interfaces that are configured for virtual assistant interaction may include tasks to be completed that may have repetitious entry of the same or similar information. User preferences may be learned by the system and may be confirmed by the user prior to the learned preference being implemented. Learned preferences may be identified in near real-time on large collections of data for a large population of users. Further, the learned preferences may be based at least in part on previous conversations and actions between the system and the user as well as user-defined occurrence thresholds.

Learning user preferences in a conversational system

Providing variable responses in a virtual-assistant environment

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.

Virtual assistant acquisitions and training

Virtual assistant conversations

User intent may be derived from a previous communication. For example, a text string for user input may be obtained. The text string may include a pronoun. Information from a communication received prior to receipt of the user input may be derived. The information may identify an individual. User intent may be derived from the text string and the information. This may include determining that the pronoun refers to the individual.

Growing community of inventors

Colbert, WA, United States of America

Tanya M Miller