- Number
- 20240135201
- Published
- 2024-04-25
- Filed
- 2023-01-17
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Butler; Brianna, Kumar; Komath Naveen, Ayala; Alfredo
- CPC
- G06N20/00; G06N5/022; G06N7/01; G06N3/045
- Verdict
- Low Notable software
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Automated performative/show-sequence generation technique.
Abstract
A system includes a computing platform having a hardware processor and a memory storing software code and a machine learning (ML) model trained to predict the next element of a sequence. The software code is executed to receive input data identifying an element of the sequence, determine, using the input data, at least one mood driver(s) of the sequence, and predict, based on input data and the mood driver(s), one or more candidate next element(s) of the sequence using the ML model. The software code further obtains expertise data relating to the sequence, evaluates the candidate next element(s), using the expertise data, the input data, and the mood driver(s), to provide aptness score(s) each corresponding to a respective one candidate next element, and determines, using the aptness score(s) and a respective probability assigned to each of the candidate next element(s) by the ML model, the next element of the sequence.
Background
BACKGROUND
Advances in artificial intelligence (AI) have enabled the generation of a variety of automated performances, such as those by machines or digital characters that perform actions or simulate social interaction. However, conventionally generated AI performances typically project a single synthesized persona that tends to lack a distinctive personality and is unable to credibly express mood.
In contrast to conventional AI generated performances, actions performed by human beings tend to be more nuanced, varied, and dynamic. For example, speech, movement, facial expressions, and postures of a person are typically influenced by the emotional and physical states of that person. That is to say, typical shortcomings of AI generated performances include their lack of inflection by mood or emotional state such as excitement, disappointment, anxiety, and optimism, to name a few. Thus, there is a need in the art for an automated performative sequence generation solution capable of producing emotionally expressive actions and effects for execution in real-time, dynamically, while a performance is ongoing.
Claims
1. A system comprising: a computing platform having a hardware processor and a system memory storing a software code and a machine learning (ML) model trained to predict a next element of a sequence; the hardware processor configured to execute the software code to: receive input data identifying an element of the sequence; determine, using the input data, at least one mood driver of the sequence; predict, based on the input data and the at least one mood driver, one or more candidate next elements of the sequence using the ML model; obtain, from a knowledge base, expertise data relating to the sequence; evaluate the one or more candidate next elements, using the expertise data, the input data, and the at least one mood driver, to provide one or more aptness scores each corresponding to a respective one of the one or more candidate next elements; and determine, using the one or more aptness scores and a respective probability assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence. ||
8. A method for use by a system including a computing platform having a hardware processor and a system memory storing a software code and a machine learning (ML) model trained to predict a next element of a sequence, the method comprising: receiving, by the software code executed by the hardware processor, input data identifying an element of the sequence; determining, by the software code executed by the hardware processor and using the input data, at least one mood driver of the sequence; predicting, based on the input data and the at least one mood driver, one or more candidate next elements of the sequence, by the software code executed by the hardware processor and using the ML model; obtaining, by the software code executed by the hardware processor, from a knowledge base, expertise data relating to the sequence; evaluating, by the software code executed by the hardware processor, the one or more candidate next elements, using the expertise data, the input data, and the at least one mood driver, to provide one or more aptness scores each corresponding to a respective one of the one or more candidate next elements; and determining, by the software code executed by the hardware processor and using the one or more aptness scores and a respective probability assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence. ||
15. A computer-readable non-transitory storage medium having stored thereon a software code, which when executed by a hardware processor, instantiates a method comprising: receiving input data identifying an element of a sequence; determining, using the input data, at least one mood driver of the sequence; predicting, based on the input data and the at least one mood driver, one or more candidate next elements of the sequence using a machine learning (ML) model trained to predict a next element of the sequence; obtaining, from a knowledge base, expertise data relating to the sequence; evaluating the one or more candidate next elements, using the expertise data, the input data, and the at least one mood driver, to provide one or more aptness scores each corresponding to a respective one of the one or more candidate next elements; and determining, using the one or more aptness scores and a respective probability assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence.