IP Library Granted Patent US 11,763,580
Granted Patent B2
US 11,763,580 · App. 17/564,270 · Granted Sep 19, 2023

Information processing apparatus, information processing method, and program

Inventors: Makoto Murata (Tokyo, JP); Naoki Shibuya (Tokyo, JP); Junko Takabayashi (Tokyo, JP); Yuuji Takimoto (Kanagawa, JP); Koji Sato (Tokyo, JP)
Assignee: SONY GROUP CORPORATION
G06V20/70G06F1/1686G06F3/002G06F3/011G06F3/0304G06F3/04895G06F16/24575G06F16/435G06F18/256G06F40/00G06Q50/01G06V10/768G06V10/811G06V20/10G06V20/20G06V40/23
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Quick Facts
Patent No.
US 11,763,580
App. No.
17/564,270
Granted
Sep 19, 2023
Kind
B2
Abstract

There is provided an information processing apparatus for automatically generating information representing a context surrounding a user, the information processing apparatus including a recognition processing unit configured to perform, on the basis of user environment information including at least any of location information representing a location where a user is present, image information relating to an environment surrounding a user, and audio information relating to the environment, an analysis process of at least any of the location information, the image information, and the audio information included in the user environment information, at a predetermined time interval, and to recognize a context surrounding the user, using the acquired result of analysis relating to the user environment; and a context candidate information generating unit configured to generate context candidate information representing a candidate of the context surrounding the user, the context candidate information including, at least, information representing the context surrounding the user and information representing the user's emotion in the context, using the result of context recognition performed by the recognition processing unit.

Claims (37)

1. A non-transitory, computer-readable medium storing instructions that, when executed by a processor on an information processing apparatus, control the information processing apparatus to implement a method comprising:

receiving image information read from image data storage;

performing image recognition processes to recognize scene, object, and/or activity in the image information based on a respective discriminator applied to the image information depending on amount of characteristic of scene, object, and/or activity respectively of the image information;

outputting a score corresponding to the recognition as a result of performing the image recognition processes; and

generating context candidate information based on the score and on the recognized scene, object, or activity in a hybrid form comprising text data and at least one of audio data or image data,

wherein the context candidate information represents the received image information, and

wherein the discriminators are generated from a plurality of images collected in advance and applied to a neural network.

2. The non-transitory, computer-readable medium according to claim 1 , wherein the received image information is image information captured by a camera and stored in the image data storage.

3. The non-transitory, computer-readable medium according to claim 1 , wherein the object is at least one of a face, a landscape, or a dish.

4. The non-transitory, computer-readable medium according to claim 3 , wherein, if the object is recognized as a face, the method further comprises outputting at least one of a number of faces, coordinates, angles, presence or absence of smile, age, and race.

5. The non-transitory, computer-readable medium according to claim 3 , wherein, if the object is recognized as a face, the method further comprises outputting a name of a person belonging to the face.

6. The non-transitory, computer-readable medium according to claim 1 , wherein the method further comprises transmitting to a social network service an item of information selected from the context candidate information.

7. The non-transitory, computer-readable medium according to claim 1 , wherein the method further comprises generating the context candidate information each time an environment included in the image information changes.

8. The non-transitory, computer-readable medium according to claim 1 , wherein the method further comprises displaying the context candidate information in a context candidate information area.

9. The non-transitory, computer-readable medium according to claim 8 , wherein a plurality of the context candidate information is displayed in the context candidate information area.

10. The non-transitory, computer-readable medium according to claim 9 , wherein the context candidate information includes text representing the context.

11. The non-transitory, computer-readable medium according to claim 1 , wherein the method further comprises generating one or more classifications based on an amount of object characteristics detected in the image information.

12. The non-transitory, computer-readable medium according to claim 11 , wherein the method further comprises:

comparing the amount of object characteristics to a threshold for a particular object; and

generating a classification for the particular object if the amount of object characteristics exceeds the threshold.

13. A method for determining context candidate information from image information in an information processing apparatus comprising a processor, the method comprising:

receiving image information read from image data storage in the information processing apparatus;

performing image recognition processes to recognize scene, object, and/ or activity in the image information based on a respective discriminator applied to the image information depending on amount of characteristic of scene, object, and/or activity respectively of the image information;

outputting a score corresponding to the recognition as a result of performing the image recognition processes; and

generating context candidate information based on the score and on the recognized scene, object, or activity in a hybrid form comprising text data and at least one of audio data or image data,

wherein the context candidate information represents the received image information, and

wherein the discriminators are generated from a plurality of images collected in advance and applied to a neural network.

14. The method according to claim 13 , wherein the method further comprises transmitting to a social network service an item of information selected from the context candidate information.

15. The method according to claim 13 , wherein the method further comprises generating the context candidate information each time an environment included in the image information changes.

16. An information processing apparatus comprising a processor configured to:

receive image information read from image data storage in the information processing apparatus;

perform image recognition processes to recognize scene, object, and/or activity in the image information based on a respective discriminator applied to the image information depending on amount of characteristic of scene, object, and/or activity respectively of the image information;

output a score corresponding to the recognition as a result of performing the image recognition processes; and

generate context candidate information based on the score and based on the recognized scene, object, or activity in a hybrid form comprising text data and at least one of audio data or image data,

wherein the context candidate information represents the received image information, and

wherein the discriminators are generated from a plurality of images collected in advance and applied to a neural network.

17. The information processing apparatus according to claim 16 , wherein the circuitry is further configured to transmit to a social network service an item of information selected from the context candidate information.

Assignments (1)
CHANGE OF NAME Recorded Dec 29, 2021
From: SONY CORPORATION
To: SONY GROUP CORPORATION
Reel/Frame 058690/0557 →
Priority Claims (1)
JP 2014-106276 · May 22, 2014 · national
Continuity (4)
Continuation 16676477 · Nov 7, 2019
Continuation 16381017 · Apr 11, 2019
Continuation 15303391
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