Drift.com, Inc.

Bitpart Ai, Inc.

Christopher M Ward

Jeffrey D Orkin

Elias Torres

Bernard Ngombi Kiyanda

Maria C Moya

Sarah M Rudzki

Carolina M Caprile

Trevor J Santarra

Jane S Taschman

Paul A Crowley

Natalie Duerr

A method of managing a game platform. Using an authoring tool, a data set for a game narrative is generated. The data set includes transcripts that define tasks of non-player agents, such as Non-Player Character (NPC) agents, in a game session in which a player is operating under the control of a game engine. The data set is transformed into re-composable combinations of tasks structured as a multi-modal hierarchical network of tasks, wherein an individual path in the hierarchical network of tasks represents a plan that is a particular implementation of a task that having multiple ways of being implemented. During the game session, and using a director operating in association with the game engine, behaviors of one or more non-player agents are then orchestrated by receiving a set of observations occurring in the game and, in response, implementing a set of plans in the hierarchical network of tasks.

Multi-agent planning system for controlling non-player agents in a game

A method of authorizing content for use, e.g., in association with a conversational bot. The method begins by configuring a conversational bot using a machine learning model trained to classify utterances into topics. Utterances that are not recognized by the machine learning model (e.g., according to some configurable threshold) are then identified. Using a clustering algorithm, one or more of the identified utterances are then processed into a grouping. Information identifying a topic associated with the grouping is then received and, in response, the machine learning model is updated to include the topic.

Authoring content for a conversational bot

A method to select a response in a multi-turn conversation between a user and a conversational bot. The conversation is composed of a set of events, wherein an event is a linear sequence of observations that are user speech or physical actions. Queries are processed against a set of conversations that are organized as a set of inter-related data tables, with events and observations stored in distinct tables. As the multi-turn conversation proceeds, a data model comprising an observation history, together with a hierarchy of events determined to represent the conversation up to at least one turn, is persisted. When a new input (speech or physical action) is received, it is classified using a statistical model to generate a result. The result is then mapped to an observation in the data model. Using the mapped observation, a look-up is performed into the data tables to retrieve a possible response.

Selecting a response in a multi-turn interaction between a user and a conversational bot

A technique for semantic search and retrieval that is event-based, wherein is event is composed of a sequence of observations that are user speech or physical actions. Using a first set of conversations, a machine learning model is trained against groupings of utterances therein to generate a speech act classifier. Observation sequences therein are organized into groupings of events and configured for subsequent event recognition. A set of second (unannotated) conversations are then received. The set of second conversations is evaluated using the speech act classifier and information retrieved from the event recognition to generate event-level metadata that comprises, for each utterance or physical action within an event, one or more associated tags. In response to a query, a search is performed against the metadata. Because the metadata is derived from event recognition, the search is performed against events learned from the set of first conversations. One or more conversation fragments that, from an event-based perspective, are semantically-relevant to the query, are returned.

Event-based semantic search and retrieval

A method and computing platform to imitate human conversational response as a context transitions across multiple channels (e.g., chat, messaging, email, voice, third party communication, etc.) where inputs to the system are categorized into identified speech acts and physical acts, and a conversational bot is associated to the channels. In this approach, a data model associated with a multi-turn conversation is provided. The data model comprises an observation history, wherein an observation in the observation history includes an identification of a channel in which the observation originates. As turns are added to the multi-turn conversation, a conversational context across multiple channels is persisted using the data model. Using this approach, an AI-supported conversation started in one channel can move to another conversation channel while maintaining the context of the conversation intact and coherent.

Persisting an AI-supported conversation across multiple channels

A conversational bot system uses a set of conversations that have been annotated to identify speech acts, wherein a speech act is a labeled grouping of utterances. To facilitate processing, a data model associated with a multi-turn conversation is received. The data model comprises an observation history. Upon receipt of query that includes a sequence of at least two or more utterances, an utterance ranking algorithm is applied. The algorithm selectively reorders the utterances in the sequence into a ranked order of importance that reflects a lowest to highest priority of response. In response to applying the utterance ranking algorithm, the data model is then updated to reflect the ranked order. In one embodiment, updating the data model positions the highest priority utterance as a most recent utterance in the observation history. The updated data model is then used to attempt to generate a coherent response to the query.

Growing community of inventors

Somerville, MA, United States of America

Christopher M Ward