WO2023275982A1

WO2023275982A1 - Content transfer device, content transfer system, and content transfer method

Info

Publication number: WO2023275982A1
Application number: PCT/JP2021/024524
Authority: WO
Inventors: 智明龍; 孝幸永井; 貴文甲斐
Original assignee: 三菱電機株式会社
Priority date: 2021-06-29
Filing date: 2021-06-29
Publication date: 2023-01-05
Also published as: JPWO2023275982A1; JP7086316B1

Abstract

The content assignment device (1) according to the present disclosure comprises: a distribution video acquisition unit (13) for acquiring a distribution video from an imaging device (2) for imaging an event; a scenario selection unit (11) for acquiring information that indicates whether a scenario included in the distribution video is suitable for transfer and extracting to-be-transferred content, which is content to be transferred, from acquired content, which is the distribution video or other content acquired at the same time as the distribution video, by using the acquired information; and a transfer subject output unit (17) for outputting to-be-transferred information that includes information for identifying the to-be-transferred content.

Description

CONTENT TRANSFER DEVICE, CONTENT TRANSFER SYSTEM AND CONTENT TRANSFER METHOD

The present disclosure relates to a content transfer device, a content transfer system, and a content transfer method that support transfer of digital content.

In recent years, services that use video footage of events such as sports are diversifying. For example, Patent Literature 1 describes an event viewing support system that extracts scenes corresponding to scenes selected by an operator from a video obtained by shooting an event, and transmits a list of scene images to a user terminal. A system is disclosed. The event watching support system described in Patent Literature 1 receives, from a user terminal, a selection result of a scene image that the user wishes to reproduce, and transmits the scene image corresponding to the selection result to the user terminal. As a result, the user can view the desired scene image for reproduction.

JP-A-2003-76812

In the event watching support system of Patent Document 1, it is possible to reproduce scene images desired by the user, but the user does not have the ownership of the digital content corresponding to the scene images. In other words, the event watching support system of Patent Document 1 does not transfer the digital content to the user. In the future, it is expected that services for transferring digital content corresponding to each scene in an event to users will be provided, but Patent Document 1 does not disclose an appropriate method for transferring digital content to users.

The present disclosure has been made in view of the above, and aims to obtain a content transfer device capable of supporting appropriate transfer of digital content corresponding to scenes in an event to a user.

In order to solve the above-described problems and achieve the object, the content transfer device according to the present disclosure includes a distribution video acquisition unit that acquires distribution video from a shooting device that shoots an event, and a scene included in the distribution video to be transferred. Acquiring information indicating whether it is suitable or not, and using the acquired information, extracts the transfer target content, which is the content to be transferred, from the distribution video or the acquired content, which is other content obtained at the same time as the distribution video. A scene selection unit and a transfer target output unit for outputting transfer target information including identification information of transfer target content are provided.

The content transfer device according to the present disclosure has the effect of being able to support appropriate transfer of digital content corresponding to the scene of the event to the user.

1 is a diagram showing a configuration example of a content transfer system according to a first embodiment; FIG. FIG. 2 is a sequence diagram showing an example of transfer target content determination processing in the content transfer system according to the first embodiment; Flowchart showing an example of a transfer target scene extraction processing procedure in the scene extraction unit of the first embodiment A diagram schematically showing an example of a section in Embodiment 1 FIG. 10 is a flowchart showing another example of the transfer target scene extraction processing procedure in the scene extraction unit of the first embodiment; FIG. FIG. 4 is a diagram schematically showing an example of time selected by the scene extraction unit of Embodiment 1; FIG. 2 is a sequence diagram showing an example of a transfer target content transfer method in the content transfer device according to the first embodiment; FIG. Sequence diagram showing an example of the reassignment method of the first embodiment FIG. 1 shows a configuration example of a computer system that realizes the content transfer device according to the first embodiment; FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 1 of Embodiment 1; FIG. 10 is a diagram showing a configuration example of a content transfer system according to a second embodiment; FIG. 11 is a diagram showing a configuration example of a scene selection unit according to Embodiment 2; Schematic diagram showing an example of a neural network FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 1 of Embodiment 2; FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 2 of Embodiment 2; FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 3 of Embodiment 2; FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 4 of Embodiment 2; FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 5 of Embodiment 2;

The content transfer device, content transfer system, and content transfer method according to the embodiments will be described in detail below with reference to the drawings.

Embodiment 1.
FIG. 1 is a diagram illustrating a configuration example of a content transfer system according to a first embodiment; A content transfer system 100 of this embodiment includes a content transfer device 1 and a photographing device 2 . A photographing device 2 photographs an event. Examples of events include, but are not limited to, sports such as baseball, soccer, volleyball, basketball, martial arts, boat racing, horse racing, and bicycle racing.

The images captured by the imaging device 2 are distributed to the user terminals 4-1 to 4-N as distribution images. N is an integer of 2 or more. The user terminals 4-1 to 4-N are terminals that can be operated by users, and may be smart phones, game machines for computer games, televisions, and the like. User terminals 4-1 to 4-N will be referred to as user terminals 4 hereinafter when they are not distinguished from each other. Although not shown in FIG. 1, the user terminals 4 other than the user terminal 4-1 also have the same configuration as the user terminal 4-1. The distribution video is also distributed to the user terminals 4 other than the user terminal 4-1. Note that the user terminals 4-1 to 4-N may be included in the content transfer system 100. FIG.

In FIG. 1, the distribution video is distributed from the imaging device 2 to the user terminal 4, but the distribution video is not limited to this, and is distributed from the imaging device 2 to the user terminal 4 via one or more other devices. may

The content transfer device 1 acquires the distribution video from the imaging device 2, and extracts the transfer target content, which is the content to be transferred, from the distribution video based on the user's vote. The content to be transferred is digital content, and for example, a portion corresponding to a scene of interest is extracted from a distributed video. A scene of interest is, for example, a scene called a climax scene, a highlight scene, or the like, but is not limited to these. The details of the attention scene will be described later. The content transfer device 1 transmits information indicating transfer target content to the user terminal 4 . A user who requests the transfer of the transfer target content, that is, the user who wants to purchase the transfer target content operates the user terminal 4 to transmit transfer request information indicating a request to the user terminal 4 to purchase the transfer target content. Send to device 1. The content transfer device 1 uses the transfer request information received from the user terminal 4 to determine a transfer target user for each transfer target content. The transfer of the transfer target content to the user may be performed by auction, or may be performed by selling at a predetermined sales amount. In addition, the content transfer device 1 requests the ownership certificate issuing system 3 to issue an ownership certificate corresponding to the content to be transferred, acquires the certificate information indicating the ownership certificate from the ownership certificate issuing system 3, and obtains the certificate information and the transfer target. content to be transferred with proof of ownership (hereinafter referred to as content with proof of ownership), and the content with proof of ownership is transmitted to the user terminal 4 corresponding to the user to be transferred.

As a result, the content transfer system 100 of the present embodiment can support appropriate transfer of digital content corresponding to the scene in the event to the user. In addition, in the content transfer system 100 of the present embodiment, content to be transferred is determined by user voting. In comparison, there is an effect that the user's intention is more likely to be reflected in the selection of transfer target content.

Next, the ownership proof of this embodiment will be explained. Until now, it has been difficult to prove the ownership of content because digital content is easy to duplicate. It has become possible to prove the ownership of digital content. The content transfer device 1 of the present embodiment uses such an unforgeable appraisal and ownership certificate as ownership proof to transfer the content with ownership proof to the user. It should be noted that the user who has purchased the content with proof of ownership can transfer or sell the content with proof of ownership that he or she owns to another person. In this case, the content transfer device 1 requests the ownership certificate issuing system 3 to rewrite the owner of the content with ownership certificate. Hereinafter, the ownership certificate issued by the ownership certificate issuing system 3 is not limited to NFT, but is preferably non-forgeable.

For example, when requesting issuance of an ownership certificate for the transfer target content, the content transfer device 1 notifies the ownership certificate issuing system 3 of the identification information and the electronic data name of the transfer target content. The ownership certificate issuing system 3 issues an ownership certificate for the requested electronic data, and transmits certification information indicating the issued ownership certificate to the content transfer apparatus 1 .

Next, a configuration example of each device according to this embodiment will be described. The content transfer device 1 includes a scene selection unit 11, a transfer adjustment unit 12, a delivery video acquisition unit 13, and a delivery video storage unit 14, as shown in FIG. The scene selection unit 11 includes a voting information acquisition unit 15 and a scene extraction unit 16 . The transfer adjustment unit 12 includes a transfer object output unit 17, a transfer request acquisition unit 18, a transfer condition determination unit 19, a content transfer unit with ownership certificate 20, a sale request acquisition unit 21, a content storage unit 22, an issuance request unit 23, and a certificate An acquisition unit 24 is provided.

The distribution image acquisition unit 13 acquires the distribution image by receiving the distribution image from the imaging device 2 and stores the acquired distribution image in the distribution image storage unit 14 . The distribution image storage unit 14 stores distribution images.

The scene selection unit 11 acquires voting information, which is an example of information indicating whether or not a scene included in the distribution video is suitable for transfer, and uses the acquired voting information to select transfer target content, which is content to be transferred. is extracted from the distributed video. Voting information is information indicating a scene focused on by a user, that is, information indicating a position in a distributed video focused by a user viewing the distributed video. Details of voting information will be described later.

The voting information acquisition unit 15 of the scene selection unit 11 acquires voting information by receiving the voting information from the user terminal 4 and outputs the acquired voting information to the scene extraction unit 16 . The scene extraction unit 16 uses the voting information received from the voting information acquisition unit 15 to determine the time slot corresponding to the transfer target scene in the distributed video. The scene extraction unit 16 extracts a portion corresponding to the determined time period from the distribution video stored in the distribution video storage unit 14 as transfer target content, and assigns the transfer target content identification information to the transfer target content. , the content to be transferred is stored in the content storage unit 22 of the transfer adjustment unit 12 together with the identification information.

The content storage unit 22 stores the content to be transferred together with the identification information. The transfer target output unit 17 outputs transfer target information including identification information of the transfer target content. Specifically, the transfer target output unit 17 uses the identification information and the transfer target content stored in the content storage unit 22 to generate transfer target information indicating a list of transfer target content, and generates the transfer target content. Send the information to the user terminal 4 . The transfer target information includes identification information of the transfer target content and information indicating details of the transfer target content corresponding to the identification information. The information indicating the content of the transfer target content may be the transfer target content itself, a part of the transfer target content extracted, or the transfer target content whose resolution has been changed. Alternatively, the information indicating the contents of the content to be transferred may be "A's home run scene in the top of the 5th inning on M/D, Y year", "B player's goal scene in the first half of M/D, Y year", and the like. It may also be a text describing the details of the transfer target content. In addition, when the transfer is performed by auction, the transfer target information may include the initial price of the auction, the prompt decision price, and the like. The initial amount and the prompt payment amount may be transmitted from the content transfer device 1 to the user terminal 4 separately from the transfer target information. Also, each transfer target content may be transferred to a plurality of persons. The transfer target information may include the transfer target number of persons for each transfer target content. Further, when each transfer target content is sold at a predetermined sales price, the transfer target information includes the price of each transfer target content.

The transfer request acquisition unit 18 acquires transfer request information by receiving the transfer request information from the user terminal 4 and outputs the acquired transfer request information to the transfer condition determination unit 19 . The transfer request information includes the identification information of the user who requests the purchase of the transfer target content, that is, the transfer request, and the identification information of the transfer target content that the user wants to purchase. User identification information is determined, for example, at the time of user registration for using the content transfer system 100 . When the transfer is made by auction, the transfer request information includes the bid price. An example in which the auction is an open auction (English auction) will be described below, but the auction method is not limited to this. If another auction method is used, the transfer adjustment unit 12 may determine the transferee according to the other auction method.

Based on the transfer request information received from the transfer request acquisition unit 18, the transfer condition determination unit 19 determines the transferee, who is the user to whom the transfer target content is transferred. Specifically, when the transfer is performed by auction, the transfer condition determination unit 19 determines, for each content to be transferred, the highest bid amount among the bids presented by each user, that is, the transfer request information received from the user terminal 4. out of the bid prices extracted from , is output to the transfer target output unit 17 together with the identification information of the transfer target content. The transfer target output unit 17 transmits the maximum amount received from the transfer condition determination unit 19 to the user terminal 4 . For example, the transfer target output unit 17 adds the latest maximum amount to the transfer request information and transmits it to the user terminal 4 . Alternatively, the transfer target output unit 17 may transmit the transfer request information to the user terminal 4 by publishing the transfer request information on a website. In this case, the transfer request information published on the website may include The maximum bid amount for each transfer target content is included, and the maximum bid amount in the transfer request information is updated with the maximum amount received from the transfer condition determination unit 19 .

The transfer condition determination unit 19 determines the transfer conditions using the transfer request information received by the auction closing time. The transfer conditions include the identification information of the content to be transferred, the identification information of the user who is the transferee, and the transfer amount. For example, when one transfer target content is transferred to one person, the transfer condition determination unit 19 determines the highest bid amount included in the transfer request information received by the auction closing time for each transfer target content. Then, the user corresponding to the maximum amount is determined as the person to be transferred, and the maximum amount is set as the transfer amount. The transfer condition determination unit 19 associates the identification information of the transfer target content with the user identification information of the transfer target person and notifies the issuance request unit 23 of them.

When one transfer target content is transferred to a plurality of people through an auction, if the number of transfer target persons is M (M is an integer equal to or greater than 2), the transfer condition determination unit 19 determines the bid amount M The second highest price is notified to the transfer target output unit 17, and the transfer target output unit 17 transmits the M-th highest price to the user terminal 4 instead of the highest bid price. In this case, the transfer condition determination unit 19 selects M users corresponding to the first to M-th bid amounts in descending order among the bid amounts included in the transfer request information received by the auction closing time. and the bid price corresponding to each user is set as the transfer price. Alternatively, instead of notifying the transfer target output unit 17 of the M-th highest price, the transfer condition determination unit 19 notifies the transfer target output unit 17 of information indicating the order of the bid price of each user. , the transfer target output unit 17 may transmit information indicating the number of the price to the corresponding user terminal 4 . In this case, the user can determine the next bid price based on the information indicating what price the bid price is and the information about how many people the bid will be transferred to.

When the content to be transferred is sold at a predetermined sales price, the transfer condition determining unit 19 selects the transferee for each content to be transferred based on the transfer request information received by the purchase deadline. Determine the user. Specifically, for example, the transfer condition determining unit 19 determines whether or not the number of users requesting transfer of each transfer target content is equal to or less than the number of transfer target users. When the content to be transferred is sold at a predetermined sales price, the transfer price may not be included in the transfer conditions determined by the transfer condition determination unit 19 because the sales price is set. If the number of users requesting the transfer is equal to or less than the number of transferees, the transfer condition determination unit 19 determines all the users requesting the purchase as transferees. Further, when the number of users requesting the transfer exceeds the number of transferees, the transfer condition determining unit 19 determines the users who are the transferees by a predetermined method. The predetermined method may be a lottery method, a method of determining the user who transmitted the transfer request information earliest as the transferee, or any other method. The transfer condition determination unit 19 associates the identification information of the transfer target content with the identification information of the transfer target user and notifies the issuance request unit 23 of them.

The issue requesting unit 23 uses the user identification information, which is the identification information of the user corresponding to the transfer target person, to request the issuance of the ownership certificate of the corresponding transfer target content. Specifically, the issue requesting unit 23 uses the identification information of the content to be transferred notified from the transfer condition determining unit 19 and the user identification information of the transferee to generate the issue request information for requesting the issuance of the ownership certificate. and transmits the generated issuance request information to the possession certificate issuing system 3 .

The certificate acquisition unit 24 acquires certification information indicating that the owner of the transfer target content is the transfer target person determined by the transfer condition determination unit 19 . Specifically, the certificate acquisition unit 24 acquires the certification information by receiving the certification information from the ownership certification issuing system 3 and outputs the acquired certification information to the ownership certification attached content transfer unit 20 .

The ownership-certified content transfer unit 20 generates ownership-certified content using the certification information and the corresponding transfer target content, and distributes the ownership-certified content to the transfer target user who is the user to whom the ownership-certified content is transferred. to the user terminal 4 of the Specifically, the ownership-certified content transfer unit 20 reads the transfer target content corresponding to the certification information from the content storage unit 22, generates the ownership-certified content using the read-out transfer target content and the certification information, The generated content with proof of possession is transmitted to the corresponding user terminal 4 . Alternatively, the content transfer device 1 may hold the certificate information indicating the ownership certificate in, for example, the content storage unit 22 without sending it to the user, and send only the corresponding content (transfer target content) to the user terminal 4. . In this case, although the certification information is held in the content transfer device 1, the content to be transmitted to the user terminal 4 is the content for which the certificate has been issued. can be regarded as

The sale request acquisition unit 21 acquires the sale request information by receiving the sale request information from the user terminal 4 and outputs the acquired sale request information to the transfer target output unit 17 . The sale request information is information indicating that the user wishes to sell the owned content with proof of ownership, and is information generated by the user terminal 4 based on input from the user. The sale request information includes identification information of the content with proof of ownership that the user wishes to sell. In this embodiment, the user can sell the transferred content with proof of ownership via the content transfer system 100 . In other words, the content with proof of ownership may be transferred multiple times. The second and subsequent transfers of content with proof of ownership will be described later.

As shown in FIG. 1, the user terminal 4 includes a distribution video receiving unit 41, a voting output unit 42, a transfer target acquisition unit 43, a transfer request output unit 44, a content acquisition unit 45 with ownership certificate, a sale request output unit 46, an output A unit 47 , an input unit 48 and a content storage unit 49 are provided.

The distributed video receiving unit 41 receives the distributed video from the imaging device 2 and outputs the received distributed video to the output unit 47 . Note that, as described above, the user terminal 4 may receive the distribution video transmitted from the imaging device 2 via another device. The output unit 47 has at least a display function, and displays the distribution video received from the distribution video reception unit 41, the transfer target information received from the transfer target acquisition unit 43, and the like. The output unit 47 also has a function as a speaker, and may output sound data added to the distributed video as sound. The input unit 48 receives input from the user. Although an example in which the output unit 47 of the user terminal 4 displays the distribution video will be described below, an output device for displaying the distribution video or the like may be provided separately from the user terminal 4 without being limited to this. In this case, the output unit 47 transmits the distribution video to the output device, and the output device displays the distribution video. Also, the output device may include a speaker to output sound data added to the distributed video as sound.

When the vote output unit 42 receives an input indicating the intention of voting from the user via the input unit 48, the vote output unit 42 transmits to the content transfer device 1 vote information including time information indicating the time at which the input was received.

The transfer target acquisition unit 43 acquires the transfer target information by receiving the transfer target information from the content transfer device 1 and outputs the acquired transfer target information to the output unit 47 . When the transfer request output unit 44 receives, via the input unit 48, input of transfer request information including the identification information of the transfer target content requested to be transferred from the user, the transfer request output unit 44 adds the user identification information to the received transfer request information. , the transfer request information including the user identification information is transmitted to the content transfer apparatus 1 .

The ownership-certified content acquisition unit 45 acquires the ownership-certified content by receiving the ownership-certified content from the content transfer device 1 and stores the acquired ownership-certified content in the content storage unit 49 . The content storage unit 49 stores content with proof of ownership. As described above, the certification information may be held in the content transfer device 1. In this case, the content acquisition unit 45 with ownership certificate acquires the content (content to be transferred) and stores the acquired content in the content storage unit 49. store in

The sale request output unit 46 receives, from the user via the input unit 48, the identification information of the content owned by the user, that is, the content with proof of ownership stored in the content storage unit 49, which the user desires to sell. When the input of the sale request information including is received, the received sale request information is transmitted to the content transfer apparatus 1 .

Also, here, an example in which content with proof of ownership owned by the user is stored in user terminal 4 will be described, but content with proof of ownership owned by the user may be stored in content transfer device 1 or another device. good. In this case, the user accesses the website using the user terminal 4 to view the content with proof of ownership, or uses application software installed in the user terminal 4 to view the content with proof of ownership. Further, when the user accesses the website using the user terminal 4, a list of content with proof of ownership owned by the user may be displayed. Further, in the display screen of this list, an input field may be provided for accepting the selection of the content with proof of ownership that is requested to be sold.

The possession certificate issuing system 3, as shown in FIG. The issue request acquisition unit 31 acquires issue request information by receiving the issue request information from the content transfer device 1 and outputs the acquired issue request information to the certificate issuing unit 32 . Based on the issuance request information received from the issuance request acquisition unit 31 , the certificate issuing unit 32 issues the ownership certificate, and outputs certification information, which is electronic data representing the ownership certificate, to the certificate output unit 33 . For the ownership certificate, for example, NFT can be used as the ownership certificate, but the ownership certificate is not limited to this as described above. The certificate output section 33 transmits the certificate information received from the certificate issuing section 32 to the content transfer device 1 .

Next, the operation of this embodiment will be described. FIG. 2 is a sequence diagram showing an example of transfer target content determination processing in the content transfer system 100 of the present embodiment. As shown in FIG. 2, the photographing device 2 transmits a distribution video to each user terminal 4 (step S1). As a result, each user terminal 4 displays the received distribution video. As described above, the user terminal 4 may receive the distributed video transmitted from the imaging device 2 via another device.

Each user terminal 4 accepts votes from users (step S2). Specifically, when the user is watching the displayed distribution video, if there is a scene of interest, such as a favorite scene, a moving scene, a scene in which he is interested, a scene in which he enthusiastically cheers, a scene that he would like to see again, etc. , perform a specific operation to indicate the intention of voting on the user terminal 4 . For example, the output unit 47 displays buttons (hereinafter referred to as voting buttons) displaying "Vote", "Like", "Good", "Excellent", etc. together with the distributed video. A user viewing a distributed video presses a voting button when there is a scene of interest. When the input unit 48 detects that the voting button has been pressed, the input unit 48 notifies the voting output unit 42 that the user has input an intention to vote. As described above, the specific operation is, for example, an operation of pressing a voting button displayed together with the distributed video, but the specific operation is not limited to this example as long as it is determined in advance. For example, the specific operation may be an operation of pressing a specific button in the input means that the input unit 48 of the user terminal 4 has in advance, or an operation of inputting specific characters.

The attention scene can be exemplified by the following. Note that the scene of interest may be an error scene, an accident scene, or the like. The attention scene can be arbitrarily selected by the user, and is not limited to these.
If the event is baseball: home run scene, hit scene, strikeout scene, etc. If the event is soccer or basketball: goal scene, save scene, etc. If the event is volleyball: attack scene, block scene, receive scene, etc. If the event is athletics or swimming, such as a serve scene: Goal scene, start scene, ranking change scene, etc. If the event is gymnastics: Landing scene, trick execution scene, etc. If the event is a horse race, bicycle race, or boat: Goal Scenes, ranking change scenes, etc.

Next, each user terminal 4 transmits voting information to the content transfer device 1 (step S3). Specifically, when the voting output unit 42 of the user terminal 4 is notified from the input unit 48 that the user has input the intention to vote, the voting output unit 42 outputs the voting information including the time information indicating the notified time. Generate. Since the time from when the user performs an input operation indicating the intention of voting to when the vote output unit 42 is notified is extremely short, the time when the notification is made is the time when the user performs a specific operation on the user terminal 4. can be considered to be Therefore, the time information indicates the scene focused by the user, that is, the position focused by the user in the distributed video. The vote output unit 42 transmits the generated vote information to the content transfer device 1 . Although illustration is omitted in FIG. 1, the distribution video is transmitted even during execution of step S3 until the distribution is completed. The number of times the user indicates the intention to vote is not limited to one time while viewing the distributed video, and the user may indicate the intention to vote at a plurality of timings. Submit your voting information at It is also possible to set an upper limit of the number of votes for one viewing of the distributed video by one user, and allow the user to indicate the intention of voting for each scene within the range of the upper limit.

Next, when the distribution of the distribution video ends, the content transfer device 1 extracts a transfer target scene (transfer target content) from the distribution video based on the voting information (step S4). Specifically, the voting information acquisition unit 15 of the content transfer device 1 receives voting information from each user terminal 4 and outputs the received voting information to the scene extraction unit 16 . The scene extraction unit 16 determines a scene to be transferred using the time information included in the received voting information, extracts a portion corresponding to the determined scene from the distribution video as content to be transferred, and transfers the content to be transferred. It is stored in the content storage unit 22 together with the identification information of the target content. The details of the method of extracting the scene to be transferred will be described later.

Next, the content transfer device 1 generates transfer target information including identification information of the transfer target content (step S5). Specifically, the transfer target output unit 17 uses the identification information and transfer target content stored in the content storage unit 22 to generate transfer target information indicating a list of transfer target content. As described above, the transfer target information includes the identification information of the transfer target content and the information indicating the content of the transfer target content corresponding to the identification information. The information indicating the content of the transfer target content may be the transfer target content itself, or may be an extracted part of the transfer target content. In addition, when the transfer is performed by auction, the transfer target information includes the initial auction price, the prompt decision price, etc., and when one transfer target content is transferred to multiple people, the number of transfer target persons is also included. . Further, when each transfer target content is sold at a predetermined sales price, the transfer target information includes the sales price of each transfer target content. Information such as the initial price of the auction, the prompt decision price, the number of people to be transferred, and the sales price may be transmitted from the content transfer device 1 to the user terminal 4 separately from the transfer target information.

Next, the content transfer device 1 transmits transfer target information to each user terminal 4 (step S6). Specifically, the transfer target output unit 17 transmits the transfer target information to each user terminal 4 . Each user terminal 4 displays the transfer target information upon receiving the transfer target information. This allows the user to confirm what kind of content is to be transferred.

Note that the user terminal 4 that receives the distribution video and the user terminal 4 that receives the transfer target information may be different. That is, the user who views the distributed video and votes does not have to be the same user who requests the transfer of the transfer target content after the distribution of the distributed video ends. Therefore, FIG. 1 shows an example in which the user terminal 4 has both a function of receiving distribution video and transmitting voting information and a function of performing processing related to transfer of content to be transferred. should have at least one of these functions. The users who vote are the users who are watching the distributed video, and not all the users who are watching the distributed video want to transfer the transfer target content. There are also users who have not viewed the distributed video but wish to transfer the transfer target content. Therefore, the user terminal 4 of a user who views the distributed video and votes and does not wish to transfer the content to be transferred should at least have the function of receiving the distributed video and transmitting the voting information. Also, the user terminal 4 of the user who does not view the distributed video and wishes to transfer the transfer target content may at least have a function of performing processing related to transfer of the transfer target content.

Next, we will explain how to extract the scenes to be transferred. FIG. 3 is a flow chart showing an example of a transfer target scene extraction processing procedure in the scene extraction unit 16 of the present embodiment. As shown in FIG. 3, the scene extraction unit 16 divides the distribution video from the distribution start time to the distribution end time into sections (step S11). For example, assuming that distribution is performed in real time or quasi-real time, the distribution start time and distribution end time of the distributed video correspond to the start time and end time of the time period in which the event is captured as the distributed video, respectively. Specifically, the scene extraction unit 16 acquires, for example, the delivery start time and the delivery end time of the delivered video based on the property information of the delivered video stored in the delivered video storage unit 14 . The property information is information added to the electronic data when the distribution video is stored as electronic data in the distribution video storage unit 14 . The delivery start time and delivery end time may be input by the operator. The scene extraction unit 16 divides the distribution start time to the distribution end time into sections of predetermined time lengths such as one minute. One minute is an example, and the length of the section is not limited to one minute.

Next, the scene extraction unit 16 tallies the number of votes for each section (step S12). Specifically, the scene extraction unit 16 counts the number of voting information whose time information included in the voting information received from the voting information acquiring unit 15 is the time within the interval for each interval divided in step S11. FIG. 4 is a diagram schematically showing an example of sections in the present embodiment. In FIG. 4, the horizontal axis indicates time, and the vertical axis indicates the number of votes. The example shown in FIG. 4 shows three sections from section #1 to section #3. In the example shown in FIG. 4, the number of votes indicates the number of votes per time unit such as one second. For example, the scene extraction unit 16 calculates the total number of votes in section #1 as the number of votes in section #1, and calculates the total number of votes in section #2 as the number of votes in section #2. The number of votes for each section is counted. FIG. 4 schematically shows the number of votes, and the number of divided sections, the time from the distribution start time to the distribution end time, etc. are not limited to the example shown in FIG.

Next, the scene extraction unit 16 selects a section that satisfies the conditions for which the number of votes is determined (step S13). Specifically, the scene extraction unit 16 selects a section corresponding to the number of votes satisfying a predetermined condition from the number of votes counted for each section in step S12. Predetermined conditions are, for example, the condition that the ranking when arranging the number of votes in each section in descending order of the number of votes is within the range from the first place to a predetermined ranking, or the number of votes is a predetermined threshold or more. It is a condition that The defined conditions are not limited to these conditions, as long as they select the section with the most votes.

Next, the scene extraction unit 16 extracts the scene corresponding to the selected section from the distributed video (step S14). Specifically, the scene extracting unit 16 reads out the distribution video stored in the distribution video storage unit 14, and for each section selected in step S13, extracts a scene to be transferred, which is a portion corresponding to the section, from the distribution video. Extract.

Next, the scene extraction unit 16 stores the extracted scene as transfer target content (step S15). Specifically, the scene extraction unit 16 assigns identification information to the transfer target content, which is the transfer target scene extracted in step S14, for each selected section, and stores the transfer target content together with the identification information in the content storage unit 22. do.

By the above processing, the transfer target content is extracted from the distributed video and the transfer target content is stored in the content storage unit 22 . In the present embodiment, since content to be transferred is determined by user votes in this way, compared to the case where the operator of a video distribution company or the like decides content to be transferred, it is more difficult for users to select content to be transferred. This has the effect of making it easier to reflect the intention of

In the example described above, since the transfer target content is extracted in units of predetermined sections, the extracted transfer target content may be video up to the middle of a series of actions that many users are likely to pay attention to. , it may contain many extra parts. For example, if the event is baseball, it is assumed that the transfer target scene (transfer target content) extracted in step S14 is a video that starts with a scene in which a player starts running after a home run. It is assumed that there is a slight delay between the user confirming the home run scene and performing the voting operation. For example, it is expected that the number of votes will begin to gradually increase immediately after a player hits a home run. Therefore, the interval immediately after hitting the home run is likely to be the interval selected in step S13, but the start time of this interval does not always match the start time of the action of hitting the home run. Therefore, the scene extraction unit 16 of the content transfer device 1 may request the operator to confirm the transfer target content by displaying the transfer target content extracted in step S14 on a display unit (not shown). In this case, the operator confirms the displayed transfer target content for each transfer target content, and instructs the content transfer device 1 to change the section to be extracted when determining that adjustment is necessary. For example, by operating the input unit of the content transfer device 1 (not shown), the operator instructs the scene extraction unit 16 to shift the section to be extracted as the content to be transferred forward or backward, or remove an unnecessary portion. The section to be extracted as the transfer target content may be adjusted by giving an instruction to exclude it from the extraction target.

In the example shown in FIG. 3, the scene extraction unit 16 divides the period from the distribution start time to the distribution end time into sections, selects sections with a large number of votes, and transfers portions of the distributed video corresponding to the selected sections. Although the content is the target content, the transfer target content may be determined based on the distribution of the number of votes.

FIG. 5 is a flow chart showing another example of the procedure for extracting scenes to be transferred in the scene extraction unit 16 of the present embodiment. In the example shown in FIG. 5, based on the distribution of the number of votes, a portion of a time period including a time with a large number of votes is extracted as transfer target content from the distributed video. Specifically, first, as shown in FIG. 5, the scene extraction unit 16 tallies the number of votes for each unit time from the delivery start time to the delivery end time of the delivered video (step S16). Specifically, the scene extraction unit 16 uses the time information included in the voting information received from the voting information acquisition unit 15 to count the number of pieces of voting information for each unit time from the distribution start time to the distribution end time. The unit time is, for example, 1 second, but is not limited to this. For example, if the unit time is 1 second and the time information is indicated in units of 1/100 seconds, the time information indicates a value between 15:10:21.00 and 15:10:21.99. The voting information is counted as voting information corresponding to 15:10:21.

Next, the scene extraction unit 16 uses the number of votes per unit time to select a time that satisfies a predetermined condition (step S17). The predetermined conditions are, for example, the condition that the ranking when arranging in order of the number of votes is within the range from 1st place to a predetermined rank, and the condition that the number of votes is equal to or greater than a predetermined threshold. . In addition, the specified condition is that the total value and average value of the number of votes in consecutive multiple units of time must be within the range from 1st to a predetermined rank when the values are arranged in descending order. The condition may be a condition that the total value of the number of votes in a plurality of continuous unit times, or the average value is equal to or greater than a predetermined threshold value. For example, the scene extraction unit 16 may select the time when the unit time is 1 second and the total value for 3 seconds including 1 second before and after is equal to or greater than a threshold. The determined conditions are not limited to these conditions as long as the time with the most votes is selected.

FIG. 6 is a diagram schematically showing an example of times selected by the scene extraction unit 16 of this embodiment. In FIG. 6, the horizontal axis indicates time, and the vertical axis indicates the number of votes. The number of votes on the vertical axis indicates the number of votes per unit time. The example shown in FIG. 6 shows an example in which, in step S17, the times in the range of the first and second ranks when arranged in descending order of the number of votes are selected. 2 and the next most frequent time #1 are selected. FIG. 6 schematically shows the number of votes, and the distribution of the number of votes, the time from the delivery start time to the delivery end time, etc. are not limited to the example shown in FIG.

Returning to the description of FIG. Next, the scene extraction unit 16 determines an extraction period, which is a time zone corresponding to the scene to be extracted based on the selected time (step S18). For example, the scene extraction unit 16 determines _a time period from T1 seconds before to T2 seconds after the selected _time as an extraction period. FIG. 6 illustrates an extraction period #1 corresponding to time #1 and an extraction period #2 corresponding to time #2. Usually, when an action such as a home run or a goal is performed, many users vote after the home run or goal is determined. For this reason, the start of the action corresponding to the scene of interest is often disclosed, for example, one second to several seconds before the time when the number of votes increases. In general, when extracting a video such as a home run scene or a goal scene as a content to be transferred, it is desirable that the content be extracted so as to be included in the video from the start of the corresponding action. Therefore, T ₁ described above is preferably set to a value larger than T ₂ , and can be set to T ₁ =10, T ₂ =5, etc., for example. Appropriate values of T ₁ and T ₂ may vary depending on the type of sport that is the event, and the values of T ₁ and T ₂ are not limited to this example, and are determined as appropriate according to the type of event. I wish I could.

Next, the scene extraction unit 16 extracts the scene corresponding to the extraction period from the distributed video (step S19). Specifically, the scene extraction unit 16 reads out the distribution video stored in the distribution video storage unit 14, and distributes the transfer target scene corresponding to the extraction period for each extraction period determined in step S18. Extract from video. Step S15 after step S19 is the same as the example shown in FIG.

Although the two examples shown in FIGS. 3 and 5 have been described above, the scene extraction unit 16 may determine the content to be transferred based on the voting information, and the specific determination method is not limited to these examples.

Next, we will explain how to transfer the content to be transferred. FIG. 7 is a sequence diagram showing an example of a method of transferring content to be transferred in the content transfer device 1 of the present embodiment. It is assumed that the operation shown in FIG. 2 has been performed and the content transfer device 1 has already transmitted the transfer target information to the user terminal 4 before the operation shown in FIG. 7 is performed.

As shown in FIG. 7, the user terminal 4 receives input of transfer request information including identification information of transfer target content for which transfer is requested (step S21). Specifically, the input unit 48 receives input of transfer request information from the user and outputs the received transfer request information to the transfer request output unit 44 . When the transfer is made by auction, the transfer request information includes the bid amount.

The user terminal 4 transmits the transfer request information to the content transfer device 1 (step S22). Specifically, the transfer request output unit 44 transmits transfer request information to the content transfer device 1 . The transfer request information includes identification information of transfer target content that the user wishes to transfer and user identification information. As described above, the user identification information is determined, for example, at the time of user registration for using the content transfer system 100, and is stored in a storage unit within the user terminal 4 (not shown). User identification information can be obtained from the Also, the user identification information may be input by the user when viewing the distributed video.

The content transfer device 1 determines transfer conditions based on one or more pieces of transfer request information for each content to be transferred (step S23). Specifically, the transfer request acquisition unit 18 acquires transfer request information from the user terminal 4 and outputs the acquired transfer request information to the transfer condition determination unit 19 . The transfer condition determination unit 19 uses the transfer request information received from the transfer request acquisition unit 18 to determine the transferee. When the transfer is performed by auction, the transfer target user and the transfer amount are determined as described above based on the bid amount in the transfer request information received by the deadline of the auction for each transfer target content. When the transfer is made by selling at a predetermined sales price, the transfer condition determining unit 19 determines whether the number of transfer request information received is equal to or less than the number of transferees of the transfer target content for each transfer target content. For example, it decides to transfer to all users corresponding to the transfer request information. On the other hand, when the transfer condition determination unit 19 receives transfer request information in a number exceeding the number of transfer target users of the transfer target content, the transfer condition determination unit 19 determines transfer target users by a predetermined method such as lottery. The transfer condition determination unit 19 associates the identification information of the transfer target content with the user identification information of the transfer target person and notifies the issuance request unit 23 of them. When the transferee is determined, the transfer condition determination unit 19 notifies the transferee output unit 17 of the identification information of the transferable content for which the transferee has been determined. The transfer target information may be updated by deleting the identification information of the determined transfer target content, and the updated transfer target information may be transmitted to the user terminal 4 .

Next, the content transfer device 1 transmits ownership certificate issue request information to the ownership certificate issuing system 3 (step S24). Specifically, the issuance requesting unit 23 uses the identification information of the content to be transferred and the user identification information of the transferee notified from the transfer condition determining unit 19 to generate the issuance request information for requesting the issuance of the ownership certificate. and transmits the generated issuance request information to the possession certificate issuing system 3 .

Next, the ownership certificate issuing system 3 performs ownership certificate issuing processing (step S25). Specifically, the issue request acquiring unit 31 acquires issue request information from the content transfer device 1 and outputs the acquired issue request information to the certificate issuing unit 32 . The certificate issuing unit 32 issues an electronic possession certificate based on the issuance request information received from the issuance request acquisition unit 31, and outputs certification information indicating the electronic possession certificate to the certificate output unit 33. do. The ownership certificate is, for example, an unforgeable appraisal such as NFT and an ownership certificate, as described above.

Next, the ownership certificate issuing system 3 transmits the certificate information to the content transfer device 1 (step S26). Specifically, the certificate output unit 33 transmits the certificate information received from the certificate issuing unit 32 to the content transfer device 1 .

Next, the content transfer device 1 transmits the content with proof of ownership to the corresponding user terminal 4 (step S27). Specifically, the certificate acquisition unit 24 acquires the certification information from the ownership certificate issuing system 3 and outputs the acquired certification information to the ownership certificate attached content transfer unit 20 . The ownership-certified content transfer unit 20 reads the transfer target content corresponding to the certification information from the content storage unit 22, and generates the ownership-certified content using the read-out transfer target content and the certification information. The ownership-certified content transfer unit 20 transmits the generated ownership-certified content to the user terminal 4 of the user who is the transferee corresponding to the ownership-certified content. Note that, as described above, the content transfer device 1 may hold the certification information.

Through the above processing, the content transfer device 1 can support transfer of transfer target content to appropriate users. Although not shown in FIG. 7, the transfer target user pays for the content with proof of ownership. The settlement process for payment of the price may be performed by the content transfer device 1 or by a settlement system (not shown). The determination process may be performed by any method, and detailed description is omitted.

Also, as described above, the user can transfer the owned content with proof of ownership. The second and subsequent transfers are hereinafter also referred to as reassignments. FIG. 8 is a sequence diagram showing an example of the reassignment method according to this embodiment. As shown in FIG. 8, the user terminal 4 receives input of sale request information including identification information of content that the user wishes to sell out of the content owned by the user (step S31). Specifically, the input unit 48 receives input of sale request information from the user and outputs the accepted transfer request information to the sale request output unit 46 .

Next, the user terminal 4 transmits sale request information to the content transfer device 1 (step S32). Specifically, the sale request output unit 46 transmits the sale request information to the content transfer device 1 .

Next, the content transfer device 1 adds the content that the user wishes to sell to the content to be transferred (step S33). Specifically, the sale request acquisition unit 21 acquires sale request information from the user terminal 4 and outputs the acquired sale request information to the transfer target output unit 17 . Upon receiving the sale request information from the sale request acquisition unit 21, the transfer target output unit 17 adds the content with ownership certificate corresponding to the identification information included in the sale request information to the transfer target content, and adds the transfer target to the transfer target information. Update the transfer target information by reflecting the addition of content.

Next, the content transfer device 1 transmits the transfer target information to each user terminal 4 (step S6), as in step S6 of FIG. Specifically, the transfer target output unit 17 transmits the transfer target information updated in step S<b>33 to each user terminal 4 .

Thereafter, the transfer target content is transferred in the same manner as the process shown in FIG. If the content to be transferred is the content with proof of ownership requested by the user to sell, that is, in the case of reassignment, in step S23, the transfer condition determining unit 19 determines whether the content to be transferred that is the content with proof of ownership is the first time. , the transfer request information received from the transfer request acquisition unit 18 is used to determine the transferee and the transfer amount, and the identification information of the transferee content and the user identification information of the transferee are associated with each other and issued. The request unit 23 is notified. In step S24, the issue requesting unit 23 uses the identification information of the transfer target content and the user identification information of the transfer target person to issue an ownership certificate in which the owner has been changed to the new transfer target person. Make a request to System 3. When such owner change processing is performed, the proof of possession relating to the owner before the change becomes invalid. Therefore, even if the user terminal 4 that sold the content with proof of ownership still stores the content with proof of ownership, the proof of ownership in the content with proof of ownership is invalidated.

The second and subsequent transfers may be conducted by auction or by selling at a predetermined sales price. When the second and subsequent transfers are conducted by auction, the sale request information may include the initial auction price, the prompt purchase price, and the like. Also, the user terminal 4 may transmit the content with proof of ownership to be sold together with the sale request information. If the second and subsequent transfers are made by selling at a predetermined sales price, the sales price may be the same as or different from the first transfer. In addition, the transfer method differs between the first transfer and the second and subsequent transfers, such as the first transfer being made by selling at a predetermined sales price, and the second and subsequent transfers being made by auction. may be

Next, the hardware configuration of the content transfer device 1 of this embodiment will be described. The content transfer device 1 of the present embodiment functions as the content transfer device 1 by executing a program, which is a computer program in which processing in the content transfer device 1 is described, on the computer system. FIG. 9 is a diagram showing a configuration example of a computer system that realizes the content transfer device 1 of this embodiment. As shown in FIG. 9, this computer system comprises a control section 101, an input section 102, a storage section 103, a display section 104, a communication section 105 and an output section 106, which are connected via a system bus 107. there is

In FIG. 9, the control unit 101 is, for example, a processor such as a CPU (Central Processing Unit), and executes a program describing the processing in the content transfer device 1 of this embodiment. Note that part of the control unit 101 may be realized by dedicated hardware such as a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array). The input unit 102 is composed of, for example, a keyboard and a mouse, and is used by the user of the computer system to input various information. The storage unit 103 includes various memories such as RAM (Random Access Memory) and ROM (Read Only Memory) and storage devices such as hard disks, and stores programs to be executed by the control unit 101 and necessary information obtained in the process of processing. store data, etc. The storage unit 103 is also used as a temporary storage area for programs. The display unit 104 includes a display, LCD (liquid crystal display panel), etc., and displays various screens to the user of the computer system. A communication unit 105 is a receiver and a transmitter that perform communication processing. The output unit 106 is a printer, speaker, or the like. Note that FIG. 9 is an example, and the configuration of the computer system is not limited to the example in FIG.

Here, an example of the operation of the computer system until the program of the present embodiment becomes executable will be described. In the computer system having the above configuration, for example, a computer program is stored in a storage unit from a CD-ROM or DVD-ROM set in a CD (Compact Disc)-ROM drive or a DVD (Digital Versatile Disc)-ROM drive (not shown). 103 installed. Then, when the program is executed, the program read from storage unit 103 is stored in the main storage area of storage unit 103 . In this state, control unit 101 executes processing as content transfer apparatus 1 of the present embodiment according to the program stored in storage unit 103 .

In the above description, a program describing the processing in the content transfer device 1 is provided using a CD-ROM or DVD-ROM as a recording medium. For example, a program provided by a transmission medium such as the Internet via the communication unit 105 may be used depending on the capacity of the computer.

The scene extraction unit 16, the transfer condition determination unit 19, and the ownership-certified content transfer unit 20 shown in FIG. 1 are executed by the control unit 101 shown in FIG. It is realized by being The storage unit 103 shown in FIG. 9 is also used to realize the scene extraction unit 16, the transfer condition determination unit 19, and the ownership-certified content transfer unit 20 shown in FIG. The distribution video storage unit 14 and the content storage unit 22 shown in FIG. 1 are part of the storage unit 103 shown in FIG. The distribution video acquisition unit 13, the voting information acquisition unit 15, the transfer target output unit 17, the transfer request acquisition unit 18, the sale request acquisition unit 21, the issuance request unit 23, and the certificate acquisition unit 24 shown in FIG. It is implemented by the communication unit 105 shown. The control unit 101 is also used to realize the distribution video acquisition unit 13, the voting information acquisition unit 15, the transfer target output unit 17, the transfer request acquisition unit 18, the sale request acquisition unit 21, the issuance request unit 23, and the certificate acquisition unit 24. . Also, the content transfer device 1 may be realized by a plurality of computer systems. For example, the content transfer device 1 may be realized by a cloud computer system.

Similarly, the possession certificate issuing system 3 is also realized by, for example, a computer system with the configuration shown in FIG. The user terminal 4 is similarly realized by, for example, a computer system with the configuration shown in FIG. The distribution video receiving unit 41, the voting output unit 42, the transfer target acquisition unit 43, the transfer request output unit 44, the content with ownership certificate acquisition unit 45, and the sale request output unit 46 shown in FIG. 105. The control unit 101 is also used to implement the distribution video receiving unit 41, the voting output unit 42, the transfer target acquisition unit 43, the transfer request output unit 44, the ownership-certified content acquisition unit 45, and the sale request output unit 46. The content storage unit 49 shown in FIG. 1 is part of the storage unit 103 shown in FIG. The input unit 48 shown in FIG. 1 is implemented by the input unit 102 shown in FIG. Output unit 47 shown in FIG. 1 is implemented by display unit 104 shown in FIG. 9 and output unit 106, which is a speaker.

<Modification 1>
Next, a content transfer system according to Modification 1 of the present embodiment will be described. FIG. 10 is a diagram showing a configuration example of a content transfer system according to Modification 1 of the present embodiment. As shown in FIG. 10, the content transfer system 100a of Modification 1 of the present embodiment includes a content transfer device 1a instead of the content transfer device 1, and the data acquisition device 5 is added. is the same as the content transfer system 100 shown in FIG. Components having the same functions as those of the content transfer system 100 shown in FIG. 1 are assigned the same reference numerals as in FIG. 1, and overlapping descriptions are omitted. Differences from the example shown in FIG. 1 will be mainly described below.

The content transfer device 1a is the same as the content transfer device 1 shown in FIG. be. The scene selection unit 11a is the same as the scene selection unit 11 shown in FIG.

In Modified Example 1 of the present embodiment, the data acquisition device 5, which is another content acquisition device, acquires other content that is content related to an event and is content other than distribution video. The data acquisition device 5 acquires, for example, three-dimensional point cloud data as other content while the event is being held, that is, while the distribution video is being shot. That is, the 3D point cloud data, which is an example of other content, is acquired at the same time as the delivered video, and can be associated with the delivered video based on the acquired time. The content transfer device 1a acquires the 3D point cloud data from the data acquisition device 5, and extracts transfer target content from the 3D point cloud data, which is other content. The method of determining the content to be transferred and the method of transfer are the same as those of the content transfer apparatus 1 shown in FIG. The user terminal 4 acquires three-dimensional point cloud data as content with proof of ownership. As a result, the user can create, for example, a figure of a player using the 3D point cloud data with a 3D printer (three-dimensional printing machine).

The other content acquisition unit 25 acquires the 3D point cloud data by receiving the 3D point cloud data from the data acquisition device 5, and stores the 3D point cloud data in the other content storage unit 26 as other content. When extracting the transfer target content in step S4 shown in FIG. 2, the scene extraction unit 16a extracts the transfer target content from the three-dimensional point cloud data stored in the other content storage unit 26 based on the voting information. do. For example, when the processing shown in FIG. 3 is performed, in step S14, the scene extracting unit 16a extracts the time zone portion corresponding to the selected section from the three-dimensional point cloud data. When the process shown in FIG. 5 is performed, in step S19, the scene extraction unit 16a extracts the time zone portion corresponding to the extraction period from the three-dimensional point cloud data. As described above, in Modification 1 of the present embodiment, the user views and votes for the distributed video, but the content to be transferred is the other content acquired at the time corresponding to the distributed video. For example, 3D point cloud data, which is an example of other content, cannot be viewed as it is. The user's intention can be reflected in the selection of transfer target content. Here, the content to be transferred is the 3D point cloud data, but both the distributed video and the 3D point cloud data may be the content to be transferred. Both the distribution video and the 3D point cloud data may be set as transfer targets, or the distribution video and the 3D point cloud data may be transfer targets individually.

As described above, in the first modification of the present embodiment, the scene selection unit 11a acquires the voting information from the user terminal 4, and uses the acquired voting information to acquire the transfer target content at the same time as the distribution video. Extract from content. Therefore, the content to be transferred is extracted from the acquired content, which is the delivered video or other content acquired at the same time as the delivered video. In the example shown in FIG. 1, the acquired content is the delivered video. In Modification 1 shown in , the acquired content is other content.

Also, in the above example, 3D point cloud data was acquired from the data acquisition device 5, but the data acquisition device 5 may be a plurality of free-viewpoint cameras that perform shooting to create free-viewpoint video. In this case, the other content acquisition unit 25 acquires a plurality of images acquired by the free viewpoint camera from the data acquisition device 5 as other content, and stores them in the other content storage unit 26 . When extracting the transfer target content in step S4 shown in FIG. 2, the scene extracting unit 16a extracts a scene from a plurality of images acquired by the free-viewpoint camera stored in the other content storage unit 26 based on the voting information. Extract transferable content. A plurality of images captured by the free-viewpoint camera have a large amount of data compared to normal distribution images, and the number of user terminals 4 that receive them may be limited. Multiple images captured by the free-viewpoint camera may be used as distributed images to receive voting information, but in this case, the number of user terminals 4 that receive the multiple images captured by the free-viewpoint camera may be limited. be. Therefore, if a user votes using a normal distribution video and selects a plurality of videos acquired by a free-viewpoint camera as other content as content to be transferred, the intentions of many users will be reflected in the selection of content to be transferred. be able to. In this example, multiple videos captured by the free-viewpoint camera are used as the transfer target content, but both the distributed video and the multiple video captured by the free-viewpoint camera may be transfer target content. Both the distribution video and the multiple videos acquired by the free-viewpoint camera may be transferred as a set, or the distribution video and the multiple videos acquired by the free-viewpoint camera may be individually transferred.

It should be noted that multiple images acquired by the free-viewpoint camera may be distributed as distribution images. That is, at least some of the user terminals 4 may receive a plurality of images captured by the free-viewpoint camera as distributed images.

Also, the content transfer device 1a may use three-dimensional data created from a plurality of images acquired by a free-viewpoint camera as other content. In this case, the content transfer device 1a may generate three-dimensional data using a plurality of images captured by the free-viewpoint camera, or another device (not shown) may generate the three-dimensional data. In this case as well, both the distributed video and the 3D data may be transferable content.

As described above, in the present embodiment, the content transfer devices 1 and 1a use the voting information received from the user terminals 4 of the users viewing the distribution video of the event to determine the content to be transferred, When transfer request information requesting transfer is received from the user terminal 4, the transfer request information is used to determine the user to whom the transfer target content is to be transferred. Then, the content transfer devices 1 and 1a transmit the content to be transferred to the user terminal 4 as content with an ownership certificate. Therefore, the content transfer devices 1 and 1a can support appropriate transfer of the digital content corresponding to the scene in the event to the user.

Embodiment 2.
FIG. 11 is a diagram illustrating a configuration example of a content transfer system according to a second embodiment; A content transfer system 100b of the present embodiment includes a content transfer device 1b and a photographing device 2. FIG. The content transfer device 1b is the same as the content transfer device 1 of the first embodiment except that the scene selection unit 11 is replaced with a scene selection unit 11b. Components having functions similar to those of the first embodiment are denoted by the same reference numerals as those of the first embodiment, and overlapping descriptions are omitted. Differences from the first embodiment will be mainly described below.

Possession certificate issuing system 3 of the present embodiment is the same as that of the first embodiment. 4-1 to 4-N are the same. The user terminals 4a-1 to 4a-N are referred to as user terminals 4a when they are not distinguished from each other.

In the first embodiment, the content to be transferred is determined by user voting. The learned model is used to determine the content to be transferred. That is, in the present embodiment, the information indicating whether or not the scene included in the distribution video is suitable for transfer is the information for inferring from the input video whether or not the scene included in the video is suitable for transfer. It is a trained model. The scene selection unit 11b extracts the transfer target content from the delivered video using the inference result obtained by inputting the delivered video to the trained model.

FIG. 12 is a diagram showing a configuration example of the scene selection unit 11b of this embodiment. As shown in FIG. 12 , the scene selection unit 11 b of this embodiment includes a learned model generation unit 111 , a learned model storage unit 112 and an inference unit 113 .

The inference unit 113 reads out the learned model stored in the learned model storage unit 112, and inputs the distribution video read out from the distribution video storage unit 14 to the read-out learned model, thereby obtaining an appropriate transfer target. Estimate the scene. A trained model is generated, for example, as follows, by the trained model generation unit 111 before the start of provision of the video of the event.

The trained model generation unit 111 generates a trained model using a plurality of learning data sets including video footage of the event and content to be transferred, which is the corresponding correct data, and uses the trained model as a trained model. Stored in the model storage unit 112 . The video of the event captured may be a distributed video captured by the imaging device 2 in the past, or may be a video captured by another imaging device. The correct data is the transfer target content extracted from the corresponding video, that is, the video of the attention scene extracted from the corresponding video. The content to be transferred, which is the correct data, may be determined by the operator by checking the video, or may be determined based on the voting information described in the first embodiment in the past. , may be determined by other methods.

The generation of a trained model in the trained model generation unit 111 is performed, for example, by supervised learning. Any supervised learning algorithm may be used, and for example, a neural network model may also be used. A neural network consists of an input layer made up of multiple neurons, an intermediate layer (hidden layer) made up of multiple neurons, and an output layer made up of multiple neurons. The intermediate layer may be one layer, or two or more layers.

FIG. 13 is a schematic diagram showing an example of a neural network. For example, in a three-layer neural network as shown in FIG. 13, when multiple inputs are input to the input layer (X1-X3), the value is multiplied by the weight W1 (w11-w16) and the intermediate layer ( Y1-Y2), and the result is multiplied by weight W2 (w21-w26) and output from the output layer (Z1-Z3). This output result changes depending on the value of weight W1 and the value of weight W2.

In the present embodiment, by adjusting the weight W1 and the weight W2 so that the output from the output layer when the video is input becomes the appropriate content to be transferred, the transfer of the video and the correct data is performed. A relationship with the target content is learned. Note that machine learning algorithms are not limited to neural networks.

In the example shown in FIG. 12, the scene selection unit 11b includes a learned model generation unit 111, but a learning device for generating a learned model is provided separately from the content transfer device 1b. A finished model generation unit 111 may be provided. In this case, the scene selection unit 11b does not need to include the trained model generation unit 111, and the trained model generation unit 111 of the learning device generates a trained model in the same manner as described above. Then, the learned model generated by the learning device is stored in the learned model storage unit 112 of the scene selection unit 11b.

In this embodiment, instead of step S4 shown in FIG. , a process of estimating an appropriate transfer target scene is performed. Other operations in content transfer system 100b of the present embodiment are the same as those of the first embodiment.

In the above example, an example of using a trained model that inputs the entire distribution video was explained, but a trained model that divides the distribution video and inputs it may also be used. In this case, for example, the trained model generation unit 111 uses a plurality of learning data sets that include video for a predetermined length of time and the determination result as to whether or not the video is transfer target content. Generate a trained model. In addition, even when this trained model is used, it is possible to infer a video suitable for the content to be transferred using the determination result. It is a trained model for inferring whether or not The video input as the learning data set may be, for example, a video selected in the past as content to be transferred, or a video of a notable scene such as a home run scene or a goal scene captured by another camera. may Further, when the input image is suitable for the transfer content, the determination result of the correct data corresponding to the image is given as a value indicating that the content is the transfer target content. The learned model generation unit 111 also receives a scene that is not suitable for transfer content as a video of the learning data set, and in this case, is given a value indicating that it is not transfer target content as a result of determination that it is correct data. .

When a trained model that obtains a determination result with input of a video for a predetermined length of time is used, the inference unit 113 selects the video for the predetermined length of time from among the distribution videos stored in the distribution video storage unit 14. A video is read out as an input video, and the input video is input to a trained model to obtain a determination result as to whether or not the input video is an appropriate transfer target scene, that is, whether or not the input video is transfer target content. The inference unit 113 changes the position (time) of the distributed video to be read as the input video, that is, the position of the input video extracted from the distributed video, inputs the changed input video to the trained model, and obtains the determination result. repeat. For example, assuming that the predetermined length of time is 10 seconds, the inference unit 113 first inputs the video from the delivery start time of the delivery video to 10 seconds after the delivery start as an input video to the trained model, and obtains the determination result. obtain. Next, the inference unit 113 inputs the input video from 1 second after the distribution start time of the distribution video to 11 seconds after the distribution start time to the trained model to obtain the determination result. In this way, the inference unit 113 sequentially changes the time corresponding to the input video, inputs it to the trained model, and obtains the determination result by inputting the input including the end of the distribution video (the part corresponding to the distribution end time). Repeat until the image is processed. As a result, it is determined whether or not each input image is the content to be transferred. The inference unit 113 stores the input video determined as the transfer target content in the content storage unit 22 as the transfer target content. In this case, since the same scene of interest may be included in a plurality of continuous input videos, the operator may check the transfer target content and remove unnecessary transfer target content. Alternatively, the operator may input the progress of the game and automatically select transfer content based on the input information. For example, if there are transferable contents for the homerun that decided the winning team and the homerun for the losing team, the operator inputs the result of the winning or losing team so that the transferable content corresponding to the winning homerun is selected. may Note that the predetermined length of time and the amount of change when changing the position of the input video extracted from the distribution video are not limited to the examples described above.

Further, in the above example, the position of the input video is changed by a change amount shorter than the predetermined time length. The distribution video may be divided by a predetermined length of time, and the divided video may be input to the trained model as the input video. In this case, the scene of interest may be divided into two input videos, so as described in Embodiment 1, the operator may check the content to be transferred and adjust the position to extract from the distribution video. . In the above example, the determination result is a binary value indicating whether or not the content is to be transferred. However, the present invention is not limited to this. may be used as the determination result.

For example, the value is divided into 10 levels, and the higher the value, the higher the judgment result (evaluation value) is set. When generating a trained model, 10 is given as correct data corresponding to the image considered to be the most valuable. Similarly, by giving the value of the video in the learning data set as correct data, the trained model generation unit 111 can generate a learning data set that outputs judgment results in multiple stages. As in the above example, the inference unit 113 sequentially inputs the input video extracted from the distributed video while changing the positions thereof, and obtains the determination result for each input video. The inference unit 113 stores, in the content storage unit 22, the input video whose determination result is equal to or greater than a predetermined threshold as transfer target content. It is possible to adjust the number of transferable contents by setting the threshold to a lower value when you want to increase the number of transferable contents, and by setting the threshold to a higher value when you want to decrease the number of transferable contents. Become. Also, a learned model may be generated for each evaluation value.

Also, a learned model may be generated by using, as correct data, the types of scenes such as home run scenes, hit scenes, and strikeout scenes, and determination results that are information as to whether or not the content is appropriate for transfer. In this case, when the learned model is generated, for example, as correct data corresponding to the video of the home run scene in the learning data set, information indicating that the video is a home run scene and indicating that the video is the content to be transferred. The information shown is given. The inference unit 113 inputs the input video extracted from the distribution video to the trained model, thereby obtaining the type of scene and the determination result as to whether or not the content is the transfer target content. In this case, the scene type selected as the transfer target content can be changed according to the event by specifying the scene type selected as the transfer target content using the inference result.

In addition, in both the case of inputting the entire distribution video and the case of inputting video for a predetermined length of time, a trained model may be generated using not only video but also audio data related to the event. For example, audio data of an announcement when a video is distributed may be input together with the video to generate a trained model, and audio data distributed together with the distributed video may also be input to the trained model during inference. As a result, for example, if the event is baseball, the announcer's voice such as "Home run!" and "Strike out!"

Also, when inputting the entire distribution video, it is necessary to include video (or video and voice data) as correct data, the learned model generation unit 111 generates a learned model such that the output transfer content including the length of the extracted transfer content approaches the correct data. may

In addition, when a video for a predetermined length of time is input, a value indicating the degree of similarity with the correct data is given as correct data separately from the judgment result of the scene during learning, and the inference unit 113 uses the judgment result. After selecting a candidate for the content to be transferred by using the By selecting a clipping position with a high degree of similarity, an appropriate transfer content including length may be selected. Further, when information indicating the reliability of the output can be obtained at the time of inference, the inference unit 113 selects a transfer target content candidate, then cuts out a part of the selected transfer content candidate, and sets the cutout start position. Information indicating the reliability may be obtained by sequentially changing the length and input to the trained model, and the information indicating the reliability may be used to select an appropriate transfer content including the length.

Also, a trained model is generated for each type of event such as baseball, soccer, volleyball, and horse racing, and the inference unit 113 selects the trained model to be used according to the type of event to be determined as the content to be transferred. can be

As in Modification 1 of Embodiment 1, when other content such as three-dimensional point cloud data or three-dimensional data is set as content to be transferred, the scene selection unit 11b similarly uses the learned model. Alternatively, the content to be transferred, which is other content, may be determined by In this case, the trained model generation unit 111 may perform learning using a video equivalent to the distribution video in the same manner as described above, or instead of the video, three-dimensional point cloud data, which is other content, 3 Similar learning may be performed using dimensional data or the like. When learning using video is performed, the inference unit 113 inputs the distribution video to the learned model during inference, that is, when determining the transfer target content, and selects an appropriate section (time period) as the transfer target content from the distribution video. ). Then, the inference unit 113 determines, as the transfer target content, the other content corresponding to the section obtained using the learned model. When learning using other content is performed, the inference unit 113 determines the content to be transferred by inputting 3D point cloud data, 3D data, etc., which are other content, to the learned model.

<Modification 1>
Next, a content transfer system according to Modification 1 of the present embodiment will be described. FIG. 14 is a diagram showing a configuration example of a content transfer system according to Modification 1 of the present embodiment. As shown in FIG. 14, the content transfer system 100c of Modification 1 of the present embodiment includes a content transfer device 1c and a photographing device 2. In FIG. A content transfer system 100c of Modification 1 of the present embodiment is a combination of the content transfer system 100b shown in FIG. 11 and the first embodiment. The content transfer device 1c is the same as the content transfer device 1 of the first embodiment except that the scene selection unit 11 is replaced with a scene selection unit 11c. Components having functions similar to those of the first embodiment are denoted by the same reference numerals as those of the first embodiment, and overlapping descriptions are omitted.

The user terminals 4-1 to 4-N and ownership certificate issuing system 3 shown in FIG. 14 are the same as the user terminals 4-1 to 4-N and ownership certificate issuing system 3 of the first embodiment.

The scene selection unit 11c, like the scene selection unit 11b described above, obtains an inference result by inputting the distribution video to the learned model. Also, the scene selection unit 11c acquires voting information in the same manner as the scene selection unit 11 of the first embodiment. Then, the scene selection unit 11c extracts transfer target content candidates, which are transfer target content candidates, from the distribution video using the inference result, and determines the transfer target content from among the transfer target content candidates using voting information.

The scene selection unit 11c includes a learned model generation unit 111, a learned model storage unit 112, and an inference unit 113, similar to the scene selection unit 11b shown in FIG. The scene selection unit 11c further includes a voting information acquisition unit 15 and a scene extraction unit 16b similar to those in the first embodiment. The operations of the trained model generation unit 111, the trained model storage unit 112, and the inference unit 113 are the same as the examples shown in FIGS. However, the inference unit 113 outputs the transfer target content obtained using the learned model to the scene extraction unit 16b as a transfer target content candidate. The voting information acquiring unit 15 acquires voting information from the user terminal 4 and outputs the acquired voting information to the scene extracting unit 16b in the same manner as in the first embodiment. The scene extraction unit 16b receives the transfer target content candidates from the inference unit 113, and totals the number of votes for each transfer target content candidate during the period corresponding to the transfer target content candidate. Then, the scene extraction unit 16 b determines transfer target content candidates that satisfy the conditions for which the number of votes is determined as transfer target content, and stores the determined transfer target content in the content storage unit 22 . The predetermined condition is, for example, the condition that the number of votes in each period corresponding to the content candidate to be transferred is within the range from the first place to a predetermined rank when the number of votes in each period is arranged in descending order of the number of votes. The condition is that the number is greater than or equal to a predetermined threshold. The determined conditions are not limited to these conditions, as long as the transfer target content candidate with a large number of votes is selected.

Also, the content transfer system 100b shown in FIG. 11 and the modification 1 of the first embodiment may be combined. For example, transfer target content candidates that are other contents may be determined using machine learning, and transfer target content may be selected from the transfer target content candidates using voting information. In this case, as described above, at the time of learning, learning may be performed using a video equivalent to the distributed video, or instead of the video, other content such as 3D point cloud data, 3D data, etc. may be used. You may study

<Modification 2>
Next, a content transfer system according to Modification 2 of the present embodiment will be described. FIG. 15 is a diagram showing a configuration example of a content transfer system according to Modification 2 of the present embodiment. As shown in FIG. 15, a content transfer system 100d of Modification 2 of the present embodiment includes a content transfer device 1d and a photographing device 2. FIG. A content transfer device 1d of Modification 2 of the present embodiment is shown in FIG. This is the same as the content transfer device 1b. Components having the same functions as those of the content transfer system 100b shown in FIG. 11 are assigned the same reference numerals as in FIG. 11, and overlapping descriptions are omitted.

The user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG. 15 are the same as the user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG.

The player information storage unit 28 stores attention level information, which is information about the attention level of each player participating in the event, and images of the players. The attention level information is, for example, at least one of batting average, ERA, number of home runs, points earned, and odds, but is not limited to these. Attention level information may be input by an operator or may be transmitted from another device. Also, the attention level information is updated each time the value is updated or at a predetermined cycle. The player video extraction unit 27 reads the distribution video from the distribution video storage unit 14, reads the player image from the player information storage unit 28, and uses the player image for each player so that the player appears in the distribution video. A player video, which is video, is extracted. In addition, the player video extraction unit 27 outputs the player video and attention level information of the player to the scene selection unit 11d for each player.

The scene selection unit 11d includes a learned model generation unit 111a, a learned model storage unit 112, and an inference unit 113a. The trained model generation unit 111a generates a trained model using a plurality of learning data sets including a video, attention level information corresponding to the video, and correct data. When the input video is for one distribution video, for example, the correct data is the extracted transfer target content itself, similar to the examples shown in FIGS. In the case of a video of a specified length of time, it may be the result of judgment as to whether or not it is content to be transferred, or the result of judgment (evaluation value) showing the value of the video in stages. may The learning algorithm in the trained model generation unit 111a is the same as the learning algorithm in the trained model generation unit 111 shown in FIG. The learned model generation unit 111 a stores the generated learned model in the learned model storage unit 112 .

The inference unit 113a inputs the player video received from the player video extraction unit 27 and attention degree information of the player to the learned model read from the learned model storage unit 112, thereby obtaining the content to be transferred or the determination result. obtain. For example, when the trained model is generated with video for one distribution video as input, the inference unit 113a directly converts the player video and attention level information received from the player video extraction unit 27 into the trained model. input, and obtain the content to be transferred as the output of the trained model. For example, when the trained model is generated by inputting a video of a predetermined length of time, the inference unit 113a receives the player video from the player video extraction unit 27 as in the example shown in FIG. An image is extracted, an input image and attention level information are input to a trained model, and an output of the trained model and a determination result are obtained. Then, the inference unit 113a sequentially changes the input video in the same manner as in the example shown in FIG. The target content is stored in the content storage unit 22 .

As described above, in the modified example 2 of the present embodiment, the trained model uses the video and the attention level information indicating the attention level of the player appearing in the video to determine whether the scene included in the distribution video is suitable for transfer. It is a trained model for inferring whether or not Then, the scene selection unit 11d extracts transfer target content from the distribution video using an inference result obtained by inputting the player video and attention level information of the player corresponding to the player video to the trained model.

The operations of the content transfer system 100d other than those described above are the same as those of the content transfer system 100b shown in FIG.

For example, the home run scenes of contestants who have a high number of home runs and are competing for the number of home runs will attract a lot of attention. In addition, the goal scenes of players with high odds will attract more attention. When the degree of attention of a participating player is high, many users are also interested in the scene regarding the player. For this reason, it is preferable that scenes such as home run scenes and goal scenes related to players with a high degree of attention are preferentially selected as content to be transferred. The content transfer device 1d of Modification 2 of the present embodiment can perform learning using attention level information, thereby more appropriately determining content to be transferred.

<Modification 3>
Next, a content transfer system according to Modification 3 of the present embodiment will be described. FIG. 16 is a diagram showing a configuration example of a content transfer system according to Modification 3 of the present embodiment. As shown in FIG. 16, the content transfer system 100e of Modification 3 of the present embodiment includes a content transfer device 1e and a photographing device 2. FIG. A content transfer device 1e of Modification 3 of the present embodiment is the same as that shown in FIG. It is the same as the content transfer device 1b. Components having the same functions as those of the content transfer system 100b shown in FIG. 11 are assigned the same reference numerals as in FIG. 11, and overlapping descriptions are omitted.

The user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG. 16 are the same as the user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG.

The scene designating unit 29 is implemented by, for example, the input unit 102 shown in FIG. 9, and receives scene information designating a specific scene from the operator when there is a possibility that a specific scene in the event will attract attention. accept. For the operator, in the target event, notable scenes such as achievement of commemorative hits such as 1000 hits, 2000 hits, update of league record, update of all-time record, etc. occurred. In this case, the corresponding player and the type of scene are input as scene information. For example, if the event is baseball, there are a home run scene, a hit scene, a stolen base scene, and a strikeout scene. If the event is soccer, there are a goal scene and a save scene. For example, in the case of an event in which player A has achieved 2,000 hits, even if it is an infield hit, there is a possibility that not only the 2,000th hit but also the hits before and after that will attract attention. , the information of player A's hit scene is input.

The scene designation unit 29 outputs the input scene information to the player video extraction unit 27a. The player video extraction unit 27a holds an image for each player, reads the distribution video from the distribution video storage unit 14, reads the image of the player specified by the scene information, and extracts the distribution video using the image of the player. A player video, which is a video in which the player is captured, is extracted. The player video extraction unit 27a outputs the player video and the scene type specified by the scene information to the scene selection unit 11e.

The scene selection unit 11e includes a learned model generation unit 111b, a learned model storage unit 112, and an inference unit 113b. The trained model generating unit 111b generates a first trained model, which is a trained model similar to the trained model generated by the trained model generating unit 111 shown in FIG. generates a second trained model which is a trained model of . The trained model generation unit 111b stores the generated first trained model and the second trained model for each scene type in the trained model storage unit 112 . The trained model generating unit 111b generates the second trained model using a plurality of learning data sets including, for example, a video of a predetermined length of time and the types of scenes that are correct data for the video.

Similar to the inference unit 113 shown in FIG. 12, the inference unit 113b determines the content to be transferred by inputting the distribution video to the first learned model read from the learned model storage unit 112, and determines the content to be transferred. The target content is stored in the content storage unit 22 . Further, the inference unit 113b extracts an input image of a predetermined length of time from the player video received from the player video extraction unit 27a, and inputs the input image to the second trained model read from the trained model storage unit 112. This infers the scene type of the input image. The inference unit 113b sequentially changes the position (time) at which the input image is extracted from the selected images, and obtains an inference result of the scene type for each input image. Then, the inference unit 113b further stores in the content storage unit 22 an input image whose inference result matches the type of scene received from the player video extraction unit 27a as transfer target content. As a result, even if the scene is not extracted by extracting the content to be transferred using the first learned model, if the scene has a special meaning, the scene is extracted as the content to be transferred.

Although the scene information is input after the event in the above example, the scene information may be input before or during the event. In this case, if the operator determines that there is a possibility that a notable scene will occur in the event, based on the information indicating the performance of each player, such as the number of hits (hits), home runs, stolen bases, goals, etc. , the scene information can be input in the same way.

<Modification 4>
Next, a content transfer system according to Modification 4 of the present embodiment will be described. FIG. 17 is a diagram showing a configuration example of a content transfer system according to Modification 4 of the present embodiment. As shown in FIG. 17, a content transfer system 100f of Modification 4 of the present embodiment includes a content transfer device 1f and a photographing device 2. FIG. A content transfer device 1f according to Modification 4 of the present embodiment has the contents transfer device 1b shown in FIG. is similar to Components having the same functions as those of the content transfer system 100b shown in FIG. 11 are assigned the same reference numerals as in FIG. 11, and overlapping descriptions are omitted.

The user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG. 17 are the same as the user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG.

The event attention level designation unit 30 is realized by, for example, the input unit 102 shown in FIG. 9, and receives input of event attention level information indicating the event attention level from the operator. For example, high-class races such as championship games, league shunting games, boat races, and horse races are attracting a lot of attention. For this reason, the event attention level information is defined as, for example, a numerical value indicating that the greater the value, the higher the attention level, and the operator inputs the event attention level information corresponding to the event to the content transfer device 1f.

The event attention degree designation unit 30 notifies the received event attention degree information to the scene selection unit 11f. The scene selection unit 11f includes a learned model generation unit 111c, a learned model storage unit 112, and an inference unit 113c. The trained model generation unit 111c generates a trained model using a plurality of learning data sets including a video, event attention level information corresponding to the video, and content to be transferred as correct data corresponding to the video. and stores the generated learned model in the learned model storage unit 112 . The trained model generation processing of the trained model generation unit 111c is the same as that of the trained model generated by the trained model generation unit 111 shown in FIG. It is the same as the generation process.

The inference unit 113c inputs the distribution video read from the distribution video storage unit 14 and the event attention degree information notified from the event attention degree designation unit 30 to the learned model read from the learned model storage unit 112. , the transfer target content is determined, and the determined transfer target content is stored in the content storage unit 22 .

As described above, in Modification 4 of the present embodiment, the trained model determines whether or not the scene included in the distribution video is suitable for transfer using the distribution video and the event attention level information indicating the attention level of the event. It is a trained model for inferring The scene selection unit 11f extracts the transfer target content from the delivered video using the inference result obtained by inputting the delivered video and the event attention level information corresponding to the delivered video to the learned model.

The content transfer device 1f of Modification 4 of the present embodiment determines content to be transferred according to event attention level information. Therefore, it is possible to appropriately determine transfer target content according to the degree of attention of the event, such as extracting a large number of transfer target content for an event with a high degree of attention. Note that a trained model may be generated for each degree of attention, that is, for each value of event attention level information. Switch the trained model to use according to

<Modification 5>
Next, a content transfer system according to Modification 5 of the present embodiment will be described. FIG. 18 is a diagram showing a configuration example of a content transfer system according to Modification 5 of the present embodiment. As shown in FIG. 18, a content transfer system 100g of Modification 5 of the present embodiment includes a content transfer device 1g and a photographing device 2. In FIG. A content transfer device 1g of Modified Example 5 of the present embodiment is the same as the content transfer device 1b shown in FIG. 11 except that a scene selection section 11g is provided instead of the scene selection section 11b. Components having the same functions as those of the content transfer system 100b shown in FIG. 11 are assigned the same reference numerals as in FIG. 11, and overlapping descriptions are omitted.

The user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG. 18 are the same as the user terminals 4a-1 to 4a-N and the ownership certificate issuing system 3 shown in FIG.

Here, it is assumed that the transfer is made by auction, and the transfer amount determined by the auction is fed back to learning. Specifically, the transfer condition determination unit 19 notifies the determined transfer condition to the scene selection unit 11g.

The scene selection unit 11 g includes a learned model generation unit 111 d , a learned model storage unit 112 and an inference unit 113 . The trained model generation unit 111d generates a trained model using a plurality of learning data sets including videos and correct data, similarly to the trained model generation unit 111 shown in FIG. In the fifth modification of the present embodiment, for example, it is assumed that the video is a video of a predetermined length of time, and as the correct data, the high value, that is, the degree of suitability for transfer, is represented by multiple levels of values ( evaluation value) is given. Further, when the transfer condition is input from the transfer condition determination unit 19, the learned model generation unit 111d reads the transfer target content corresponding to the identification information of the transfer target content included in the transfer condition from the content storage unit 22, and The evaluation value is determined using the transfer amount included in the transfer conditions. For example, the learned model generation unit 111d holds correspondence information that defines the correspondence between the transfer amount and the evaluation value. For example, if the transfer amount is X ₁ yen or more, the evaluation value is 10, and if the transfer amount is X ₂ yen or more and less than X ₁ yen, the evaluation value is 9, and so on. It is information that defines a value. Note that the correspondence information may be appropriately updated in accordance with an overall change in the transfer amount. In addition, instead of reading out the transfer target content from the content storage unit 22, the learned model generation unit 111d reads out the corresponding distribution video from the distribution video storage unit 14, and extracts the portion corresponding to the transfer target content from the distribution video. may

Like the inference unit 113 shown in FIG. 12 , the inference unit 113 determines the content to be transferred by inputting the distribution video into the learned model stored in the learned model storage unit 112 . It should be noted that in Modification 5 of the present embodiment, the inference unit 113 sequentially inputs input videos of a predetermined length of time extracted from the distribution video while changing the positions thereof, and obtains determination results for each input video. The inference unit 113 stores, in the content storage unit 22, the input video whose determination result is equal to or greater than a predetermined threshold as transfer target content. A threshold may be set for each event.

As described above, in Modification 5 of the present embodiment, the learned model is a model for inferring an evaluation value indicating the degree to which a scene included in a video is suitable for transfer from a video, and the scene selection unit 11g uses the evaluation value obtained by using the trained model to extract the transfer target content from the distribution video. Then, a corresponding evaluation value is calculated using the transfer amount determined by the transfer condition determining unit 19, and the learned model is updated using the calculated evaluation value and the corresponding transfer target content as a learning data set.

In Modified Example 5 of the present embodiment, since the inference unit 113 determines transfer target content using an evaluation value that reflects the transfer price in the auction, a video of a scene similar to the transfer target content with a high transfer price is selected. It will be included in the content to be transferred with priority. As a result, scenes that are likely to result in a high transfer price can be included in the transfer target content.

Content transfer device 1b of the present embodiment shown in FIG. is implemented by a computer system in the same manner as Also, the

content transfer devices

1b, 1c, 1d, 1e, 1f, and 1g may be realized by a plurality of computer systems. For example, the

content transfer devices

1b, 1c, 1d, 1e, 1f, and 1g may be realized by a cloud computer system.

Also, in the second to fifth modifications of the present embodiment, in combination with the first embodiment, the

content transfer apparatuses

1d, 1e, 1f, and 1g determine transfer target content candidates by machine learning, and determine the transfer target content candidates. The transfer target content may be determined based on the number of votes in the section corresponding to the transfer target content candidate. Also, modifications of the present embodiment may be combined. In addition, as described in Modification 1 of Embodiment 1, in Modification 2 to Modification 5 of the present embodiment, other content such as 3D point cloud data and 3D data is the content to be transferred. In this case, similarly, a learned model may be used to determine transfer target content, which is other content.

The configurations shown in the above embodiments are only examples, and can be combined with other known techniques, or can be combined with other embodiments, without departing from the scope of the invention. It is also possible to omit or change part of the configuration.

1, 1a, 1b, 1c, 1d, 1e, 1f, 1g content transfer device, 2 photography device, 3 ownership certificate issuing system, 4-1 to 4-N, 4a-1 to 4a-N user terminal, 5 data acquisition Device, 11, 11a, 11b, 11c, 11d, 11e, 11f, 11g scene selection unit, 12 transfer adjustment unit, 13 delivery video acquisition unit, 14 delivery video storage unit, 15 voting information acquisition unit, 16, 16a, 16b scene Extraction unit 17 Transfer target output unit 18 Transfer request acquisition unit 19 Transfer condition determination unit 20 Content transfer unit with ownership certificate 21 Sale request acquisition unit 22, 49 Content storage unit 23 Issuance request unit 24 Certificate Acquisition unit 25 Other content acquisition unit 26 Other content storage unit 27, 27a Player image extraction unit 28 Player information storage unit 29 Scene designation unit 30 Event attention level designation unit 31 Issuance request acquisition unit 32 Certificate Issuing unit, 33 certificate output unit, 41 distribution video reception unit, 42 vote output unit, 43 transfer target acquisition unit, 44 transfer request output unit, 45 content acquisition unit with ownership certificate, 46 sale request output unit, 47 output unit, 48 input unit, 100 content transfer system, 111, 111a, 111b, 111c, 111d learned model generation unit, 112 learned model storage unit, 113, 113a, 113b, 113c reasoning unit.

Claims

a delivery video acquisition unit that acquires a delivery video from a camera that shoots an event;
Acquiring information indicating whether or not a scene included in the distribution video is suitable for transfer, and using the obtained information, transfer target content, which is content to be transferred, to the distribution video or simultaneously to the distribution video a scene selection unit that extracts from acquired content that is acquired other content;
a transfer target output unit that outputs transfer target information including identification information of the transfer target content;
A content transfer device comprising:
The content transfer device according to claim 1, wherein the obtained content is the distribution video.
The content transfer device according to claim 1, wherein the acquired content is three-dimensional point cloud data acquired at the same time as the distributed video.
　The content transfer device according to claim 1, wherein the acquired content is a video shot for generating a free-viewpoint video that is acquired simultaneously with the distributed video.
5. The content transfer device according to any one of claims 1 to 4, wherein the information is voting information indicating a position in the distribution video that a user watching the distribution video focused on.
The information is a trained model for inferring from the input video whether or not the scene included in the video is suitable for transfer,
3. The content transfer device according to claim 2, wherein the scene selection unit extracts the transfer target content from the acquired content using an inference result obtained by inputting the distribution video to the trained model. .
The information is a trained model for inferring from the input video whether or not the scene included in the video is suitable for transfer,
The scene selection unit acquires voting information indicating a position in the delivered video focused by a user viewing the delivered video, and uses the inference result obtained by inputting the delivered video to the trained model. 3. The content transfer device according to claim 2, wherein candidates for the content to be transferred are extracted from the content, and the content to be transferred is determined from among the candidates using the voting information.
A player video extracting unit for extracting a video including a player participating in the event from the distributed video as a player video;
with
The learned model is a trained model for inferring whether or not a scene included in the distribution video is suitable for transfer by using the video and attention level information indicating the attention level of the player appearing in the video. and
The scene selection unit extracts the transfer target content from the player video using an inference result obtained by inputting the player video and the attention level information of the player corresponding to the player video into the learned model. 8. The content transfer device according to claim 6 or 7, characterized by:
The trained model is a trained model for inferring whether or not a scene included in the delivered video is suitable for transfer by using the delivered video and event attention level information indicating the attention level of the event. ,
The scene selection unit extracts the transfer target content from the acquired content using an inference result obtained by inputting the distribution video and event attention level information corresponding to the distribution video to the trained model. 8. The content transfer device according to claim 6 or 7.
a transfer request acquisition unit for acquiring transfer request information including user identification information that is identification information of a user who requests purchase of the transfer target content and indicating a request to purchase the transfer target content;
a transfer condition determination unit that determines a transfer target person who is the user to whom the transfer target content is transferred, based on the transfer request information;
an issuance request unit that requests issuance of an ownership certificate for the corresponding transfer target content using the user identification information corresponding to the transfer target person;
a certificate acquisition unit that acquires certification information indicating that the owner of the content to be transferred is the person to be transferred;
a content transfer unit with proof of ownership that generates content with proof of ownership using the certification information and the corresponding content to be transferred, and transmits the content with proof of ownership to the corresponding user terminal of the transferee;
6. The content transfer device according to any one of claims 1 to 5, characterized by comprising:
a transfer request acquisition unit for acquiring transfer request information including user identification information that is identification information of a user who requests purchase of the transfer target content and indicating a request to purchase the transfer target content;
a transfer condition determination unit that determines a transfer target person who is the user to whom the transfer target content is transferred, based on the transfer request information;
an issuance request unit that requests issuance of an ownership certificate for the corresponding transfer target content using the user identification information corresponding to the transfer target person;
a certificate acquisition unit that acquires certification information indicating that the owner of the content to be transferred is the person to be transferred;
a content transfer unit with proof of ownership that generates content with proof of ownership using the certification information and the corresponding content to be transferred, and transmits the content with proof of ownership to the corresponding user terminal of the transferee;
10. The content transfer device according to any one of claims 6 to 9, comprising:
The content to be transferred is transferred by auction,
the transfer request information includes a bid price for the auction;
The transfer condition determination unit determines the transfer target person and the transfer price corresponding to the transfer target person based on the bid amount included in the transfer request information for each transfer target content. 12. The content transfer device according to claim 10 or 11.
The content to be transferred is transferred by auction,
the transfer request information includes a bid price for the auction;
The transfer condition determination unit determines the transfer target person and the transfer price corresponding to the transfer target person based on the bid amount included in the transfer request information for each transfer target content,
The trained model is a trained model for inferring an evaluation value indicating the degree to which a scene included in the video is suitable for transfer from the video,
The scene selection unit extracts the transfer target content from the distribution video using the evaluation value obtained using the learned model, and responds using the transfer amount determined by the transfer condition determination unit. 12. The content transfer device according to claim 11, wherein an evaluation value is calculated, and the learned model is updated using the calculated evaluation value and the corresponding transfer target content as a learning data set.
a shooting device that shoots an event and acquires distribution video;
a content transfer device;
with
The content transfer device is
a delivery video acquisition unit that acquires the delivery video from the imaging device;
Acquiring information indicating whether or not a scene included in the distribution video is suitable for transfer, and using the obtained information, transfer target content, which is content to be transferred, to the distribution video or simultaneously to the distribution video a scene selection unit that extracts from acquired content that is acquired other content;
a transfer target output unit that outputs transfer target information including identification information of the transfer target content;
A content transfer system characterized by comprising:
another content acquisition device that acquires content related to the event;
with
15. The content transfer system according to claim 14, wherein the obtained content is the content obtained by the other content obtaining device.
a user terminal that acquires the transfer target information from the content transfer device and transmits voting information indicating a position in the distribution video that a user viewing the distribution video focused on to the content transfer device;
16. The content transfer system according to claim 14 or 15, comprising:
A content transfer method in a content transfer device,
a step of acquiring a distribution video from a camera that shoots an event;
Acquiring information indicating whether or not a scene included in the distribution video is suitable for transfer, and using the obtained information, transfer target content, which is content to be transferred, to the distribution video or simultaneously to the distribution video a step of extracting from the obtained content, which is the obtained other content;
a step of outputting transfer target information including identification information of the transfer target content;
A content transfer method, comprising: