Modeling characters that interact with users as part of a character-as-a-service implementation

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to generate a character response during an interaction with a user by performing the steps of:

• generating recognition data from user input data that is generated by one or more sensors associated with a user device of a user;
• evaluating the recognition data to identify a user intent of the user and an assessment domain associated with the user intent;
• selecting, from a plurality of inference algorithms based, at least in part, on the user intent and the assessment domain, a first set of at least two inference algorithms;
• applying the user intent and the assessment domain to each inference algorithm in the first set of inference algorithms to generate a plurality of inferences;
• consolidating the plurality of inferences into a consolidated inference by weighing the plurality of inferences based on a plurality of confidence factors;
• applying a second set of inference algorithms from the plurality of inference algorithms to the consolidated inference to generate a context-specific inference, wherein the first set of inference algorithms and the second set of inference algorithms are mutually exclusive;
• computing, based on the user input data, the context-specific inference, and data representing knowledge associated with a character, the character response to the user input data;
• receiving, based on a plurality of interactions with a plurality of users, structured data that includes (i) a set of named entities and (ii) a set of relationships associated with the set of named entities;
• generating training data that includes the structured data, the context-specific inference, the character response, and one or more additional interactions between the user and the character;
• identifying, for an item of data included in the training data, one or more inference algorithms included in the plurality of inference algorithms and associated with (i) a data type included in the item of data or (ii) a type of inference operation performed on the item of data;
• training, in an offline training mode and using at least the item of data, at least one of the one or more identified inference algorithms included in the plurality of inference algorithms to generate at least one updated inference algorithm;
• and
• generating, by the at least one updated inference algorithm, an additional character response to additional user input data associated with the user, wherein the additional character response is generated based on at least one updated inference outputted by the at least one updated inference algorithm and at least one updated confidence factor associated with the at least one updated inference.

A computer-implemented method for generating a character response during an interaction with a user, the method comprising:

• generating, by a processor, recognition data from user input data that is generated by one or more sensors associated with a user device of a user;
• evaluating, by the processor, the recognition data to identify a user intent of the user and an assessment domain associated with the user intent;
• selecting at least two of a plurality of inference algorithms based, at least in part, on the user intent and the assessment domain;
• applying the user intent and the assessment domain to each of the two or more selected inference algorithms to generate a plurality of inferences;
• consolidating the plurality of inferences into a consolidated inference by weighing the plurality of inferences based on a plurality of confidence factors;
• determining one or more expression capabilities of the user device, wherein the one or more expression capabilities comprise at least one of a graphical processing power, a facial expression, a speech output, a movement, or a gesture;
• applying, based on the one or more expression capabilities of the user device, at least one of the plurality of inference algorithms to the consolidated inference to generate a context-specific inference;
• computing, based on the user input data, the context-specific inference, and data representing knowledge associated with a character, the character response to the user input data;
• configuring the user device to output the character response to the user based on the one or more expression capabilities of the user device;
• receiving, based on a plurality of interactions with a plurality of users, structured data that includes (i) a set of named entities and (ii) a set of relationships associated with the set of named entities;
• training, in an offline training mode, at least one inference algorithm included in the plurality of inference algorithms based on training data that includes the structured data, the user input data, the character response, and one or more additional interactions between the user and the character to generate at least one updated inference algorithm, wherein the at least one inference algorithm is identified for training on a data item included in the training data, based at least on a type of inference operation performed by the at least one inference algorithm;
• generating, by the at least one updated inference algorithm, an additional character response to additional user input data associated with the user, wherein the additional character response is generated based on at least one updated inference outputted by the at least one updated inference algorithm and at least one updated confidence factor associated with the at least one updated inference;
• and
• configuring an additional user device to output the additional character response based on one or more additional expression capabilities of the additional user device, wherein an expression of the additional character response by the additional user device differs from an expression of the character response by the user device.