Systems and methods for both technical and non-technical users to create content for interactive synthetic characters are provided. In some embodiments, a conversation editor may be configured to create a traversable script for an interactive synthetic character by receiving a set of conversation rules from a user. These rules can be used to match certain words or phrases that a user speaks or types, or to monitor for a physical movement of the user or synthetic character. Each conversation rule can include responses to be performed by the interactive synthetic character. The responses can include, for example, producing audible or textual speech for the synthetic character, performing one or more animations, playing one or more sound effects, retrieving data from one or more data sources, and the like. A traversable script can be generated from the set of conversation rules that when executed by the synthetic character allows for the dynamic interactions. In some embodiments, the traversable script can be navigated by a state engine using one or more navigational directives associated with the conversation rules.

The present disclosure describes methods, devices, and non-transitory computer readable storage medium for managing lifecycle of a knowledge base (KB) in chatbot systems. The method includes receiving an update request and updating a current-version KB as a new current-version KB. The method also includes receiving a first test request from a knowledge point (KP) operator and determining whether the new current-version KB passes a first test; and in response to the determination that the new current-version KB passes the first test, submitting the new current-version KB as a new submit-version KB. The method further includes receiving a second test request from a KP manager and determining whether the new submit-version KB passes a second test; and in response to the determination that the new submit-version KB passes the second test, storing an original production-version KB as a pervious-version KB and submitting the new submit-version KB as a new production-version KB.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by dynamically parameterized machine learning frameworks, such as a dynamically parameterized machine learning framework comprising an encoder machine learning model that is configured to generate dynamically generated parameters for a target machine learning model of the dynamically parameterized machine learning framework.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by dynamically parameterized machine learning frameworks, such as a dynamically parameterized machine learning framework comprising an encoder machine learning model that is configured to generate dynamically generated parameters for a target machine learning model of the dynamically parameterized machine learning framework.

The present disclosure provides method and apparatus for providing a response to a user in a question-answering session. A message may be received in the session, the session being between the user and an electronic conversational agent. Context information is extracted from a context database based at least on the message. Fact information and emotion information are obtained from at least one of the message and the context information. The fact information is associated with at least one fact, and the emotion information is associated with emotions towards the at least one fact. A response is generated based at least on the fact information and the emotion information. The response is provided to the user.

A method of providing an intelligent response on an electronic device and an apparatus therefor are provided. The method includes receiving a command from a user of the electronic device, analyzing the command, generating, based on the analyzed command, at least one intelligent response corresponding to the command and an actual response corresponding to the command, the at least one intelligent response including at least one of a sound effect associated with the command, a vibration effect associated with the command, or a visual effect associated with the command, and outputting the at least one intelligent response and the actual response by the electronic device.

The present disclosure discloses a method and an apparatus for driving a digital human, and an electronic device. The method includes obtaining a target action corresponding to a target text; obtaining a reference action to be executed before the digital human executes the target action when the digital human is driven to output speech based on the target text; modifying a target action parameter of the target action according to a reference action parameter of the reference action; and driving the digital human to execute the target action according to a modified target action parameter when driving the digital human to output the speech based on the target text.

In an embodiment, cognitive adjustment of responses from a virtual assistant includes detecting verbal content in an audio signal, determining that the verbal content includes a voice command associated with a task by correlating the verbal content with voice command data stored in memory, determining a state of the user from the audio signal by cognitively comparing aspects of the verbal content with aspects of profile data associated with the user, and retrieving task data associated with the task for preparing a response to the voice command.

In an embodiment, cognitive adjustment of responses from a virtual assistant includes detecting verbal content in an audio signal, determining that the verbal content includes a voice command associated with a task by correlating the verbal content with voice command data stored in memory, determining a state of the user from the audio signal by cognitively comparing aspects of the verbal content with aspects of profile data associated with the user, and retrieving task data associated with the task for preparing a response to the voice command.

A slicer in a material drop ejecting three-dimensional (3D) object printer identifies the positions and local densities for a plurality of infill lines within a perimeter to be formed within a layer of an object to be formed by the printer. The local density of each infill line is filtered and a control law is applied to the filtered local density to identify an error in the local density compared to a target density. This process is performed iteratively until the error is within a predetermined tolerance range about the target local density. The error is used to generate machine ready instructions to operate the 3D object printer to achieve the target density for the infill lines.