IP Library › Granted Patent US 12,340,629
Granted Patent B2
US 12,340,629 · App. 18/631,778 · Granted Jun 24, 2025

Nonverbal information generation apparatus, nonverbal information generation model learning apparatus, methods, and programs

Inventors: Ryo Ishii (Tokyo, JP); Ryuichiro Higashinaka (Tokyo, JP); Taichi Katayama (Tokyo, JP); Junji Tomita (Tokyo, JP); Nozomi Kobayashi (Tokyo, JP); Kyosuke Nishida (Tokyo, JP)
G06V40/28G06N20/00G06V40/10G10L15/22G10L2015/225
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Quick Facts
Patent No.
US 12,340,629
App. No.
18/631,778
Granted
Jun 24, 2025
Kind
B2
Abstract

A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling an expression unit that expresses behavior that corresponds to the text.

Claims (16)

1. A nonverbal information generation method comprising:

generating time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model,

wherein the time-information-stamped text feature quantities are configured to comprise feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text, and the nonverbal information is information for controlling an expression device that expresses behavior that corresponds to the text.

2. A nonverbal information generation method comprising:

generating time-information-stamped nonverbal information that corresponds to time-information-stamped voice feature quantities on the basis of the time-information-stamped voice feature quantities and a learned nonverbal information generation model,

wherein the time-information-stamped voice feature quantities are configured to comprise feature quantities that have been extracted from voice information, and time information representing times of predetermined units when the voice information is emitted, and the nonverbal information is information for controlling an expression device that expresses behavior that corresponds to the voice information.

3. A nonverbal information generation model learning method comprising:

acquiring text information representing text corresponding to voice of a speaker and time information representing times assigned to predetermined units of the text;

acquiring nonverbal information representing information relating to behavior of a listener of speaking of the speaker corresponding to the text when the speaker performed the speaking, and time information representing times at which the behavior was performed and corresponding to the nonverbal information, and creating time-information-stamped nonverbal information;

extracting time-information-stamped text feature quantities representing feature quantities of the text information from the acquired text information and the time information corresponding to the text information; and

learning a nonverbal information generation model for generating the acquired time-information-stamped nonverbal information on the basis of the extracted time-information-stamped text feature quantities.

4. A nonverbal information generation model learning method comprising:

acquiring voice information corresponding to voice of a speaker and time information representing times of predetermined units when the voice information is emitted;

acquiring nonverbal information representing information relating to behavior of a listener of speaking of the speaker corresponding to the voice when the speaker performed the speaking, and time information representing times at which the behavior was performed and corresponding to the nonverbal information, and creating time-information-stamped nonverbal information;

extracting time-information-stamped voice feature quantities representing feature quantities of the acquired voice information from the voice information and the time information corresponding to the voice information; and

learning a nonverbal information generation model for generating the acquired time-information-stamped nonverbal information on the basis of the time-information-stamped voice feature quantities.

Assignments (2)
CHANGE OF NAME Recorded Oct 3, 2025
From: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
To: NTT, INC.
Reel/Frame 072995/0532 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 19, 2024
From: ISHII, RYO; HIGASHINAKA, RYUICHIRO; KATAYAMA, TAICHI; TOMITA, JUNJI; KOBAYASHI, NOZOMI; NISHIDA, KYOSUKE
To: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
Reel/Frame 067163/0086 →
Priority Claims (5)
JP 2018-026516 · Feb 16, 2018 · national
JP 2018-026517 · Feb 16, 2018 · national
JP 2018-097338 · May 21, 2018 · national
JP 2018-097339 · May 21, 2018 · national
JP 2018-230310 · Dec 7, 2018 · national
Continuity (2)
Division 16969765
Related Publication 20240321011A1 · Sep 26, 2024
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