A Method And A System For Generating A Contextual Summary Of Multimedia Content

Wipro Limited
Granted App No 201741017535 Patent 514244 Computer Science

Patent information

IP India · live record
Application number
201741017535
Filing date
18 May 2017
Status
Granted
Legal status
Inforce
Publication number
47/2018
Publication type
INA
Invention field
Computer Science
Patent number
514244
Grant date
22 February 2024
Next renewal due
18 May 2027

Abstract

Disclosed subject matter relates to paraphrasing multimedia content including a method and system for generating a contextual summary of multimedia content. A contextual summary generator retrieves the multimedia content comprising scenes from a multimedia content database and generates scene descriptors, describing a scene, for each scene. Further, an emotion factor is identified in each scene based on each scene descriptor, each speech descriptor and each textual descriptor associated with each of the one or more scenes. Upon identifying the emotion factor, a context descriptor indicating context of each scene is generated for each scene based on analysis of each emotion factor and non-speech descriptors. Finally, the scene descriptors, textual descriptors and context descriptors are correlated based on a dynamically configured threshold value to generate the contextual summary of the multimedia content that saves precious time and efforts instead of watching or hearing the entire multimedia content that may be redundant. FIG.2

Applicants

1
India
WIPRO LIMITED Doddakannelli, Sarjapur Road, Bangalore 560035, Karnataka, India.

Inventors

3
India
ADRITA BARARIBlock B-16/249, Kendriya Vihar, VIP Road, Haldirams Bus Stop, Koikhali, Kolkata -700052, West Bengal, India.
India
MANJUNATH RAMACHANDRA80, Sadhana, 2nd main, BSK 3rd stage, Katriguppe East, Bangalore-560085, Karnataka, India.
India
GHULAM MOHIUDDIN KHANF-901, Concorde Manhattans, Electronic City Phase 1, Bangalore -560100, Karnataka, India.

Claims

20
1 A method of generating a contextual summary of multimedia content, the method comprising: retrieving, by a contextual summary generator (107), the multimedia content comprising one or more scenes from a multimedia content database (105); generating, by the contextual summary generator (107), one or more scene descriptors, describing a scene, for each of the one or more scenes; identifying, by the contextual summary generator (107), an emotion factor in each of the one or more scenes based on each of the one or more scene descriptors, each of one or more speech descriptors and each of one or more textual descriptors associated with each of the one or more scenes; generating, by the contextual summary generator (107), a context descriptor, indicating context of each of the one or more scenes, for each of the one or more scenes based on analysis of each emotion factor in each of the one or more scenes and non-speech descriptors associated with each of the one or more scenes; and correlating, by the contextual summary generator (107), the one or more scene descriptors, the one or more textual descriptors, and the context descriptors of the one or more scenes based on a dynamically configured threshold value to generate the contextual summary of the multimedia content.
2 The method as claimed in claim 1, wherein the one or more scene descriptors are generated by: determining one or more key frames from each of the one or more scenes using one or more predefined techniques; obtaining one or more non-redundant key frames from the one or more key frames based on distance between each of the one or more key frames; and identifying in each of the one or more non-redundant key frames, one or more objects, one or more actions related to the objects and background where the one or more objects are present to generate one or more scene descriptors related to each of the one or more scenes.
3 The method as claimed in claim 1 further comprises assigning a rank to each of the one or more scenes based on each of the one or more scene descriptors, the context descriptors and the dynamically generated weightage corresponding to each of the one or more scenes.
4 The method as claimed in claim 3, wherein the dynamically generated weightage is computed based on frequency and duration of occurrence of one or more objects in each of the one or more scenes.
5 The method as claimed in claim 1, wherein the one or more scenes selected for correlating is based on a rank.
6 The method as claimed in claim 1, wherein the one or more speech descriptors comprise information related to at least one of pitch, tone or speed of speech audio signals present in each of the one or more scenes.
7 The method as claimed in claim 1, wherein the one or more textual descriptors are generated by converting speech audio signals in each of the one or more scenes into a text format.
8 The method as claimed in claim 1, wherein the contextual summary is in at least one of an audio format, a video format, an image format or a textual format.
9 The method as claimed in claim 1, wherein the context descriptor for each of the one or more scenes describes at least one of a formal context, an informal context, a humorous context, a serious context, a positive context or a negative context.
10 The method as claimed in claim 1, wherein the dynamically configured threshold is based on at least one of time for which the contextual summary of the multimedia content should be generated and information to be present in the contextual summary of the multimedia content, wherein the information indicates the one or more scene descriptors, the one or more speech descriptors, the one or more non-speech descriptors, the one or more textual descriptors and the one or more context descriptors.
11 A contextual summary generator (107) for generating a contextual summary of multimedia content, the contextual summary generator (107) comprising: a processor (109); and a memory (113) communicatively coupled to the processor (109), wherein the memory (113) stores the processor (109)-executable instructions, which, on execution, causes the processor (109) to: retrieve the multimedia content comprising one or more scenes from a multimedia content database (105); generate one or more scene descriptors, describing a scene, for each of the one or more scenes; identify an emotion factor in each of the one or more scenes based on each of the one or more scene descriptors, each of one or more speech descriptors and each of one or more textual descriptors associated with each of the one or more scenes; generate a context descriptor, indicating context of each of the one or more scenes, for each of the one or more scenes based on analysis of each emotion factor in each of the one or more scenes and non-speech descriptors associated with each of the one or more scenes; and correlate the one or more scene descriptors, the one or more textual descriptors, and the context descriptors of the one or more scenes based on a dynamically configured threshold value to generate the contextual summary of the multimedia content.
12 The contextual summary generator (107) as claimed in claim 11, wherein to generate the one or more scene descriptors, the instructions cause the processor (109) to: determine one or more key frames from each of the one or more scenes using one or more predefined techniques; obtain one or more non-redundant key frames from the one or more key frames based on distance between each of the one or more key frames; and identify in each of the one or more non-redundant key frames, one or more objects, one or more actions related to the objects and background where the one or more objects are present to generate one or more scene descriptors related to each of the one or more scenes.
13 The contextual summary generator (107) as claimed in claim 11, wherein the processor (109) is further configured to assign a rank to each of the one or more scenes based on each of the one or more scene descriptors, the context descriptors and the dynamically generated weightage corresponding to each of the one or more scenes.
14 The contextual summary generator (107) as claimed in claim 13, wherein the processor (109) computes the dynamically generated weightage based on frequency and duration of occurrence of one or more objects in each of the one or more scenes.
15 The contextual summary generator (107) as claimed in claim 11, wherein the processor (109) selects the one or more scenes for correlating based on a rank.
16 The contextual summary generator (107) as claimed in claim 11, wherein the one or more speech descriptors comprise information related to at least one of pitch, tone or speed of speech audio signals present in each of the one or more scenes.
17 The contextual summary generator (107) as claimed in claim 11, wherein the processor (109) generates the one or more textual descriptors by converting speech audio signals in each of the one or more scenes into a text format.
18 The contextual summary generator (107) as claimed in claim 11, wherein the contextual summary is in at least one of an audio format, a video format, an image format or a textual format.
19 The contextual summary generator (107) as claimed in claim 11, wherein the context descriptor for each of the one or more scenes describes at least one of a formal context, an informal context, a humorous context, a serious context, a positive context or a negative context.
20 The contextual summary generator (107) as claimed in claim 11, wherein the dynamically configured threshold is based on at least one of time for which the contextual summary of the multimedia content should be generated and information to be present in the contextual summary of the multimedia content, wherein the information indicates the one or more scene descriptors, the one or more speech descriptors, the one or more non-speech descriptors, the one or more textual descriptors and the one or more context descriptors. Dated this 18th day of May 2017 SWETHA S.N OF K & S PARTNERS AGENT FOR THE APPLICANT IN/PA-2123 , Description:TECHNICAL FIELD The present subject matter relates generally to paraphrasing multimedia content, and more particularly, but not exclusively to a method and a system for generating a contextual summary of multimedia content.

Specification

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Claims:We claim: 1. A method of generating a contextual summary of multimedia content, the method comprising: retrieving, by a contextual summary generator (107), the multimedia content comprising one or more scenes from a multimedia content database (105); generating, by the contextual summary generator (107), one or more scene descriptors, describing a scene, for each of the one or more scenes; identifying, by the contextual summary generator (107), an emotion factor in each of the one or more scenes based on each of the one or more scene descriptors, each of one or more speech descriptors and each of one or more textual descriptors associated with each of the one or more scenes; generating, by the contextual summary generator (107), a context descriptor, indicating context of each of the one or more scenes, for each of the one or more scenes based on analysis of each emotion factor in each of the one or more scenes and non-speech descriptors associated with each of the one or more scenes; and correlating, by the contextual summary generator (107), the one or more scene descriptors, the one or more textual descriptors, and the context descriptors of the one or more scenes based on a dynamically configured threshold value to generate the contextual summary of the multimedia content. 2. The method as claimed in claim 1, wherein the one or more scene descriptors are generated by: determining one or more key frames from each of the one or more scenes using one or more predefined techniques; obtaining one or more non-redundant key frames from the one or more key frames based on distance between each of the one or more key frames; and identifying in each of the one or more non-redundant key frames, one or more objects, one or more actions related to the objects and background where the one or more objects are present to generate one or more scene descriptors related to each of the one or more scenes. 3. The method as claimed in claim 1 further comprises assigning a rank to each of the one or more scenes based on each of the one or more scene descriptors, the context descriptors and the dynamically generated weightage corresponding to each of the one or more scenes.

4. The method as claimed in claim 3, wherein the dynamically generated weightage is computed based on frequency and duration of occurrence of one or more objects in each of the one or more scenes.

5. The method as claimed in claim 1, wherein the one or more scenes selected for correlating is based on a rank.

6. The method as claimed in claim 1, wherein the one or more speech descriptors comprise information related to at least one of pitch, tone or speed of speech audio signals present in each of the one or more scenes.

7. The method as claimed in claim 1, wherein the one or more textual descriptors are generated by converting speech audio signals in each of the one or more scenes into a text format.

8. The method as claimed in claim 1, wherein the contextual summary is in at least one of an audio format, a video format, an image format or a textual format.

9. The method as claimed in claim 1, wherein the context descriptor for each of the one or more scenes describes at least one of a formal context, an informal context, a humorous context, a serious context, a positive context or a negative context.

10. The method as claimed in claim 1, wherein the dynamically configured threshold is based on at least one of time for which the contextual summary of the multimedia content should be generated and information to be present in the contextual summary of the multimedia content, wherein the information indicates the one or more scene descriptors, the one or more speech descriptors, the one or more non-speech descriptors, the one or more textual descriptors and the one or more context descriptors.

11. A contextual summary generator (107) for generating a contextual summary of multimedia content, the contextual summary generator (107) comprising: a processor (109); and a memory (113) communicatively coupled to the processor (109), wherein the memory (113) stores the processor (109)-executable instructions, which, on execution, causes the processor (109) to: retrieve the multimedia content comprising one or more scenes from a multimedia content database (105); generate one or more scene descriptors, describing a scene, for each of the one or more scenes; identify an emotion factor in each of the one or more scenes based on each of the one or more scene descriptors, each of one or more speech descriptors and each of one or more textual descriptors associated with each of the one or more scenes; generate a context descriptor, indicating context of each of the one or more scenes, for each of the one or more scenes based on analysis of each emotion factor in each of the one or more scenes and non-speech descriptors associated with each of the one or more scenes; and correlate the one or more scene descriptors, the one or more textual descriptors, and the context descriptors of the one or more scenes based on a dynamically configured threshold value to generate the contextual summary of the multimedia content. 12. The contextual summary generator (107) as claimed in claim 11, wherein to generate the one or more scene descriptors, the instructions cause the processor (109) to: determine one or more key frames from each of the one or more scenes using one or more predefined techniques; obtain one or more non-redundant key frames from the one or more key frames based on distance between each of the one or more key frames; and identify in each of the one or more non-redundant key frames, one or more objects, one or more actions related to the objects and background where the one or more objects are present to generate one or more scene descriptors related to each of the one or more scenes. 13. The contextual summary generator (107) as claimed in claim 11, wherein the processor (109) is further configured to assign a rank to each of the one or more scenes based on each of the one or more scene descriptors, the context descriptors and the dynamically generated weightage corresponding to each of the one or more scenes.

14. The contextual summary generator (107) as claimed in claim 13, wherein the processor (109) computes the dynamically generated weightage based on frequency and duration of occurrence of one or more objects in each of the one or more scenes.

15. The contextual summary generator (107) as claimed in claim 11, wherein the processor (109) selects the one or more scenes for correlating based on a rank.

16. The contextual summary generator (107) as claimed in claim 11, wherein the one or more speech descriptors comprise information related to at least one of pitch, tone or speed of speech audio signals present in each of the one or more scenes.

17. The contextual summary generator (107) as claimed in claim 11, wherein the processor (109) generates the one or more textual descriptors by converting speech audio signals in each of the one or more scenes into a text format.

18. The contextual summary generator (107) as claimed in claim 11, wherein the contextual summary is in at least one of an audio format, a video format, an image format or a textual format.

19. The contextual summary generator (107) as claimed in claim 11, wherein the context descriptor for each of the one or more scenes describes at least one of a formal context, an informal context, a humorous context, a serious context, a positive context or a negative context.

20. The contextual summary generator (107) as claimed in claim 11, wherein the dynamically configured threshold is based on at least one of time for which the contextual summary of the multimedia content should be generated and information to be present in the contextual summary of the multimedia content, wherein the information indicates the one or more scene descriptors, the one or more speech descriptors, the one or more non-speech descriptors, the one or more textual descriptors and the one or more context descriptors.

Dated this 18th day of May 2017

SWETHA S.N OF K & S PARTNERS AGENT FOR THE APPLICANT IN/PA-2123 , Description:TECHNICAL FIELD The present subject matter relates generally to paraphrasing multimedia content, and more particularly, but not exclusively to a method and a system for generating a contextual summary of multimedia content.

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Application documents
1 Power of Attorney [18-05-2017(online)].pdf 18 May 2017
2 Form 5 [18-05-2017(online)].pdf 18 May 2017
3 Form 3 [18-05-2017(online)].pdf 18 May 2017
4 Form 18 [18-05-2017(online)].pdf_159.pdf 18 May 2017
5 Form 18 [18-05-2017(online)].pdf 18 May 2017
6 Form 1 [18-05-2017(online)].pdf 18 May 2017
7 Drawing [18-05-2017(online)].pdf 18 May 2017
8 Description(Complete) [18-05-2017(online)].pdf_158.pdf 18 May 2017
9 Description(Complete) [18-05-2017(online)].pdf 18 May 2017
10 REQUEST FOR CERTIFIED COPY [19-05-2017(online)].pdf 19 May 2017
11 Request For Certified Copy-Online.pdf_1.pdf 06 Jun 2017
12 Request For Certified Copy-Online.pdf 06 Jun 2017
13 PROOF OF RIGHT [11-07-2017(online)].pdf 11 Jul 2017
14 Correspondence by Agent_Form 30 and Form 1_13-07-2017.pdf 13 Jul 2017
15 201741017535-Abstract.jpg 17 Jul 2017
16 201741017535-FER.pdf 28 Feb 2020
17 201741017535-FORM 3 [26-08-2020(online)].pdf 26 Aug 2020
18 201741017535-RELEVANT DOCUMENTS [27-08-2020(online)].pdf 27 Aug 2020
19 201741017535-PETITION UNDER RULE 137 [27-08-2020(online)].pdf 27 Aug 2020
20 201741017535-OTHERS [27-08-2020(online)].pdf 27 Aug 2020
21 201741017535-FER_SER_REPLY [27-08-2020(online)].pdf 27 Aug 2020
22 201741017535-DRAWING [27-08-2020(online)].pdf 27 Aug 2020
23 201741017535-CORRESPONDENCE [27-08-2020(online)].pdf 27 Aug 2020
24 201741017535-CLAIMS [27-08-2020(online)].pdf 27 Aug 2020
25 201741017535-ABSTRACT [27-08-2020(online)].pdf 27 Aug 2020
26 201741017535-US(14)-HearingNotice-(HearingDate-09-03-2023).pdf 24 Feb 2023
27 201741017535-POA [02-03-2023(online)].pdf 02 Mar 2023
28 201741017535-FORM 13 [02-03-2023(online)].pdf 02 Mar 2023
29 201741017535-Correspondence to notify the Controller [02-03-2023(online)].pdf 02 Mar 2023
30 201741017535-AMENDED DOCUMENTS [02-03-2023(online)].pdf 02 Mar 2023
31 201741017535-Written submissions and relevant documents [23-03-2023(online)].pdf 23 Mar 2023
32 201741017535-PatentCertificate22-02-2024.pdf 22 Feb 2024
33 201741017535-IntimationOfGrant22-02-2024.pdf 22 Feb 2024
Search strategy
1searchstrategy201741017535_27-02-2020.pdf

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