- Number
- 20180068658
- Published
- 2018-03-08
- Filed
- 2017-01-03
- Assignee
- DISNEY ENTERPRISES INC.
- Inventors
- Lehman; Jill Fain et al.
- CPC
- G06F40/35
- Verdict
- Set aside dialog knowledge-acquisition system, generic NLP tooling
- Source
- Google Patents · FreePatentsOnline
Abstract
A dialog knowledge acquisition system includes a hardware processor, a memory, and hardware processor controlled input and output modules. The memory stores a dialog manager configured to instantiate a persistent interactive personality (PIP), and a dialog graph having linked dialog state nodes. The dialog manager receives dialog initiation data, identifies a first state node on the dialog graph corresponding to the dialog initiation data, determines a dialog interaction by the PIP based on the dialog initiation data and the first state node, and renders the dialog interaction. The dialog manager also receives feedback data corresponding to the dialog interaction, identifies a second state node based on the dialog initiation data, the dialog interaction, and the feedback data, and utilizes the dialog initiation data, the first state node, the dialog interaction, the feedback data, and the second state node to train the dialog graph.
Background
BACKGROUND
One of the characteristic features of human interaction is variety of expression. For example, even when two people interact repeatedly in a similar manner, such as greeting one another, many different expressions may be used despite the fact that a simple “hello” would suffice in almost every instance. Instead, human beings in interaction are likely to substitute “good morning”, “good evening”, “hi”, “yo”, or a non-verbal expression, such as a nod, for “hello”, depending on the context and the circumstances surrounding the interaction. In order for a non-human social agent, such as one embodied in an animated character or robot for example, to engage in an extended interaction with a user, it is desirable that the non-human social agent also be capable of varying its form of expression in a seemingly natural way.SUMMARY
There are provided dialog knowledge acquisition systems and methods, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.
Claims
1. A dialog knowledge acquisition system comprising: a hardware processor and a memory; an input module and an output module controlled by the hardware processor; the memory storing a dialog manager configured to instantiate a persistent interactive personality (PIP), and a dialog graph having a plurality of linked dialog state nodes; the hardware processor configured to execute the dialog manager to: receive a dialog initiation data via the input module; identify a first state node on the dialog graph corresponding to the dialog initiation data; determine a dialog interaction by the PIP based on the dialog initiation data and the first state node; render the dialog interaction via the output module; receive a feedback data corresponding to the dialog interaction via the input module; identify a second state node on the dialog graph based on the dialog initiation data, the dialog interaction, and the feedback data; and utilize the dialog initiation data, the first state node, the dialog interaction, the feedback data, and the second state node to train the dialog graph for subsequent dialog interactions by the PIP.
11. A method for use by a dialog knowledge acquisition system including a hardware processor, an input module and an output module controlled by the hardware processor, and a memory storing a dialog manager configured to instantiate a persistent interactive personality (PIP) and a dialog graph having a plurality of linked dialog state nodes, the method comprising: receiving, by the dialog manager executed by the hardware processor, a dialog initiation data via the input module; identifying, by the dialog manager executed by the hardware processor, a first state node on the dialog graph corresponding to the dialog initiation data; determining, by the dialog manager executed by the hardware processor, a dialog interaction by the PIP based on the dialog initiation data and the first state node; rendering, by the dialog manager executed by the hardware processor, the dialog interaction via the output module; receiving, by the dialog manager executed by the hardware processor, a feedback data corresponding to the dialog interaction via the input module; identifying, by the dialog manager executed by the hardware processor, a second state node on the dialog graph based on the dialog initiation data, the dialog interaction, and the feedback data; and utilizing, by the dialog manager executed by the hardware processor, the dialog initiation data, the first state node, the dialog interaction, the feedback data, and the second state node to train the dialog graph for subsequent dialog interactions by the PIP.