SYSTEM FOR MINIMIZING REPETITION IN INTELLIGENT VIRTUAL ASSISTANT CONVERSATIONS

    公开(公告)号:US20220382990A1

    公开(公告)日:2022-12-01

    申请号:US17883064

    申请日:2022-08-08

    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.

    System for minimizing repetition in intelligent virtual assistant conversations

    公开(公告)号:US11409961B2

    公开(公告)日:2022-08-09

    申请号:US16598692

    申请日:2019-10-10

    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.

    Learning user preferences in a conversational system

    公开(公告)号:US11295221B2

    公开(公告)日:2022-04-05

    申请号:US16539345

    申请日:2019-08-13

    Abstract: 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.

    SYSTEMS AND METHODS FOR GENERATING RESPONSES FOR AN INTELLIGENT VIRTUAL

    公开(公告)号:US20200320134A1

    公开(公告)日:2020-10-08

    申请号:US16835975

    申请日:2020-03-31

    Inventor: Ian Beaver

    Abstract: Systems and methods are described to automatically generate candidate questions and responses to speed the process of response creation and editing for commercial IVAs and chatbots. Rather than create the questions and responses from scratch for a new IVA, the system uses existing questions and responses from a previous or related IVA to train a model that can generate proposed responses to provided questions. The model, or a different model, can further be trained to generate responses using data taken from company or entity-specific data sources such as websites and knowledge bases. After a set of questions and responses have been generated for an IVA they may be reviewed by one or more human reviewers to ensure they are of a suitable quality. Where no previous or related IVA exists to provide example responses, the model may be trained solely using the company or entity-specific data.

    DYNAMIC INTENT CLASSIFICATION BASED ON ENVIRONMENT VARIABLES

    公开(公告)号:US20200082204A1

    公开(公告)日:2020-03-12

    申请号:US16531350

    申请日:2019-08-05

    Inventor: Ian Beaver

    Abstract: To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

    MACHINE BASED EXPANSION OF CONTRACTIONS IN TEXT IN DIGITAL MEDIA

    公开(公告)号:US20200026753A1

    公开(公告)日:2020-01-23

    申请号:US16513073

    申请日:2019-07-16

    Inventor: Ian Beaver

    Abstract: As described herein, a system for expanding contractions in electronically stored text includes expanding contractions having only on expanded form. For remaining contractions, a grammar check is performed for all possible expanded forms to determine if an expanded form can be selected based on context and grammar rules. If an expanded form is not evident from the first two steps, all possible expanded forms of the remaining contractions are converted to a vector representation along with the original text. A Word Movers Distance (WMD) for each possible expansion is calculated using the vectors for each possible expansion and the original text. An expanded form is chosen without human intervention based on the grammar score alone or the WMD and the grammar score.

    Learning user preferences in a conversational system

    公开(公告)号:US10417567B1

    公开(公告)日:2019-09-17

    申请号:US14181608

    申请日:2014-02-14

    Abstract: 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.

    PARTIAL AUTOMATION OF TEXT CHAT CONVERSATIONS

    公开(公告)号:US20240305715A1

    公开(公告)日:2024-09-12

    申请号:US18669921

    申请日:2024-05-21

    Inventor: Ian Beaver

    Abstract: To allow the human customer service agents to specialize in the instances where human service is preferred, but to scale to the volume of large call centers, systems and methods are provided in which human agents and intelligent virtual assistants (IVAs) co-handle a conversation with a customer. IVAs handle simple or moderate tasks, and human agents are used for those tasks that require or would benefit from human compassion or special handling. Instead of starting the conversation with an IVA and then escalating or passing control of the conversation to a human to complete, the IVAs and human agents work together on a conversation.

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