Information-processing apparatus, method, system, computer-readable medium and method for automatically recording or recommending content

• US 9,552,413 B2
• Filed: 02/09/2015
• Issued: 01/24/2017
• Est. Priority Date: 12/12/2002
• Status: Expired due to Fees

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Yamamoto, Noriyuki, Miyazaki, Mitsuhiro, Saito, Mari, Koike, Hiroyuki
• Primary Examiner(s): Beausoliel, Jr., Robert
• Assistant Examiner(s): Pham, Michael
• Application Number:
  US14/617,531
• Publication Number:
  US 20150178379A1
• Time in Patent Office: 715 Days
• Field of Search: 707/758, 725/39
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: H04L 65/60 Network streaming of media packets
• CPC Class Codes: H04N 21/235 Processing of additional data, e.g. scrambling of additional data or processing content descriptors
• CPC Class Codes: H04N 21/2353 specifically adapted to content descriptors, e.g. coding, compressing or processing of metadata
• CPC Class Codes: H04N 21/26283 for associating distribution time parameters to content, e.g. to generate electronic program guide data
• CPC Class Codes: H04N 21/4325 by playing back content from the storage medium reproduction of recorded television signals H04N5/76; reproduction of recorded television signals H04N9/79
• CPC Class Codes: H04N 21/435 Processing of additional data, e.g. decrypting of additional data, reconstructing software from modules extracted from the transport stream
• CPC Class Codes: H04N 21/44213 Monitoring of end-user related data arrangements for monitoring the users' behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/4661 Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73

Abstract

A program vector can represent attributes of a program to be generated. EPG data can be received, and metadata necessary for generation of a program vector PP can be extracted from the EPG data. A morphological analysis can be carried out on contents and title included in the metadata to disassemble the contents and the title into words. Items included in the metadata can be subjected to a vector creation process to generate the program vector PP. An effect vector can be extracted on the basis of a genre of a program associated with the metadata. The extracted effect vector can be associated with the generated program vector PP and the processing can be ended. This can be applied to a distribution server for distributing contents.

Claims

1. An information processing apparatus, comprising:

circuitry configured to generate a content vector based on meta data of content, the content vector including one or more aspects of the content;

a storage that stores the content vector and a preference vector that includes one or more aspects of the content that are preferred by a user, wherein

the preference vector is a weighted preference vector in which the one or more aspects of the preference vector each include plural sub-vectors, each sub-vector including a number and a word,

the number corresponds to a relative preference of the word with respect to other words in the weighted preference vector, and

the word represents an identifier for a corresponding sub-vector of the preference vector; and

the circuitry further configured to calculate a cosine difference between the one or more aspects of the preference vector with corresponding aspects of the content vector,

calculate a degree of similarity between the content vector and the preference vector based on the cosine difference,

generate weighted attribute information by weighting each aspect of the content vector based on the degree of similarity,

assign a larger weight value to an aspect of the one or more aspects of the content vector having a higher degree of similarity, and select recommended content based on the weighted attribute information.

2. The information processing apparatus according to claim 1, wherein

the preference vector includes weighting factors for weighting each aspect of the content vector, and

the circuitry weights each aspect of the content vector according to the weighting factors.

3. The information processing apparatus according to claim 1, wherein the storage stores recommended content information based on the recommended content selected by the circuitry.

4. The information processing apparatus according to claim 1, wherein the circuitry is further configured to generate the preference vector based on a history of the user.

5. The information processing apparatus according to claim 1, wherein the circuitry is further configured to

receive a content preference input from the user;

modify the weighted attribute information in accordance with the content preference input; and

select the recommended content based on the modified weighted content attribute information.

6. The information processing apparatus according to claim 1, wherein

the content vector includes words,

the preference vector includes weighting factors,

the weighting factors being numbers, and

the weighted attribute information includes a corresponding weighting factor from the preference vector for each word from the content vector.

7. The information processing apparatus according to claim 1, wherein the content vector includes a title of the content, and the circuitry is further configured to disassemble the title into separate words.

8. The information processing apparatus according to claim 1, wherein

the one or more aspects of the content include a title, a genre, a time hand, a broadcasting station, a performer, creators and a key word of the content, and

the creators includes a scriptwriter, an author and a producer of the content.

9. An information processing method, comprising:

generating, by circuitry, a content vector based on meta data of content, the content vector including one or more aspects of the content;

storing the content vector and a preference vector that includes one or more aspects of the content that are preferred by a user, wherein

the preference vector is a weighted preference vector in which the one or more aspects of the preference vector each include plural sub-vectors, each sub-vector including a number and a word,

the number corresponds to a relative preference of the word with respect to other words in the weighted preference vector, and

the word represents an identifier for a corresponding sub-vector of the preference vector;

calculate, by the circuitry, a cosine difference between the one or more aspects of the preference vector with corresponding aspects of the content vector;

calculate, by the circuitry, a degree of similarity between the content vector and the preference vector based on the cosine difference;

generating, by the circuitry, weighted attribute information by weighting each aspect of the content vector based on the degree of similarity;

assigning, by the circuitry, a larger weight value to an aspect of the one or more aspects of the content vector having a higher degree of similarity; and

selecting, by the circuitry, recommended content based on the weighted attribute information.

10. The information processing method according to claim 9, wherein

the preference vector includes weighting factors for weighting each aspect of the content vector, and

each aspect of the content vector is weighted according to the weighting factors.

11. The information processing method according to claim 9, further comprising storing recommended content information based on the selected recommended content.

12. The information processing method according to claim 9, further comprising generating, by the circuitry, the preference vector based on a history of the user.

13. The information processing method according to claim 9, further comprising:

receiving a content preference input from the user; and

modifying, by the circuitry, the weighted attribute information in accordance with the content preference input, wherein

the recommended content is selected based on the modified weighted content attribute information.

14. The information processing method according to claim 9, wherein

the content vector includes words, the preference vector includes weighting factors,

the weighting factors being numbers, and

the weighted attribute information includes a corresponding weighting factor from the preference vector for each word from the content vector.

15. The information processing method according to claim 9, wherein

the one or more aspects of the content include a title, a genre, a time band, a broadcasting station, a performer, creators and a key word of the content, and

the creators includes a scriptwriter, an author and a producer of the content.

16. A non-transitory computer readable medium storing computer executable instructions that, when executed by a computer including a processor, cause the computer to:

generate a content vector based on meta data of content, the content vector including one or more aspects of the content;

store the content vector and a preference vector that includes one or more aspects of the content that are preferred by a user, wherein

the preference vector is a weighted preference vector in which the one or more aspects of the preference vector each include plural sub-vectors, each sub-vector including a number and a word,

the number corresponds to a relative preference of the word with respect to other words in the weighted preference vector, and

the word represents an identifier for a corresponding, sub-vector of the preference vector;

calculate a cosine difference between the one or more aspects of the preference vector with corresponding aspects of the content vector;

calculate a degree of similarity between the content vector and the preference vector based on the cosine difference;

generate weighted attribute information by weighting each aspect of the content vector based on the degree of similarity;

assign a larger weight value to an aspect of the one or more aspects of the content attribute information having a higher degree of similarity; and

select recommended content based on the weighted attribute information.

17. The non-transitory computer readable medium storing computer executable instructions according to claim 16, wherein

the one or more aspects of the content include a title, a genre, a time band, a broadcasting station, a performer, creators and a key word of the content, and

the creators includes a scriptwriter, an author and a producer of the content.