Information processing device, information processing method, and program

Abstract

An information processing device, an information processing method, and a program are provided that can more effectively distribute to users videos relating to voting for predicting the results of a predetermined competition. [Solution] An information processing device 10X that distributes videos to users regarding voting to predict the results of a specified competition is equipped with a selection unit 36 ​​that selects some videos from multiple videos as candidate videos for distribution based on specified criteria, and a list transmission unit 37 that transmits a list showing the selected candidate videos for distribution to the user.

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Users can post videos through a network and watch videos posted by other users. Recently, technologies have been developed that automatically classify a huge number of video listings and allow users to select a video to distribute from a predetermined classification, and that can extract and distribute videos that are likely to interest viewers (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2011 / 126134 [Patent Document 2] JP 2002-320214 A Summary of the Invention [Problem to be solved by the invention]

[0004] It is conceivable that content related to betting (betting) on ​​the results of a specific competition will be distributed as a video. When distributing such videos to users, it is preferable to distribute them to users more effectively, and the need for this increases as the number of videos posted increases.

[0005] Therefore, an object of the present invention is to provide an information processing device, an information processing method, and a program that can more effectively distribute videos related to voting in which the results of a specified competition are predicted to users. [Means for solving the problem]

[0006] An information processing device according to one embodiment of the present invention is an information processing device that distributes videos related to voting to predict the results of a specified competition to users, and is characterized by having a selection unit that selects some videos from a plurality of videos as candidate videos for distribution based on specified criteria, and a list transmission unit that transmits a list showing the selected candidate videos for distribution to the user. Effect of the Invention

[0007] According to one aspect of the present invention, when distributing videos related to voting, some videos are selected from a plurality of videos according to predetermined criteria as candidates for distribution and presented to the user, thereby enabling the videos to be effectively distributed to the user. [Brief description of the drawings]

[0008] [Figure 1] FIG. 13 is a diagram showing a display screen of a video regarding voting. [Diagram 2] FIG. 2 illustrates an information processing apparatus and a user terminal according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram illustrating functions of an information processing apparatus according to an embodiment of the present invention. [Figure 4] FIG. 2 is an explanatory diagram of user-related information. [Diagram 5] FIG. 2 is an explanatory diagram of video related information. [Figure 6] FIG. 11 is an explanatory diagram of voting-related information. [Figure 7] FIG. 2 is an explanatory diagram of race-related information. [Figure 8] FIG. 13 is an explanatory diagram of hit result information. [Figure 9] FIG. 13 is a diagram showing a display screen of a distribution candidate list. [Figure 10] FIG. 2 is a diagram showing an information processing flow using the information processing device according to one embodiment of the present invention. [Figure 11] FIG. 11 is a diagram showing the flow of a selection process. [Figure 12] FIG. 11 is a diagram illustrating functions of an information processing device according to another embodiment of the present invention. [Figure 13] FIG. 11 is a diagram showing a flow of a selection process in another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] An information processing device, an information processing method, and a program according to the present invention will be described in detail below with reference to embodiments (first to third embodiments) illustrated in the accompanying drawings. The embodiment described below is merely an example given to facilitate understanding of the present invention, and is not intended to limit the present invention. In other words, the present invention may be modified or improved from the embodiment described below without departing from the spirit of the present invention. Naturally, the present invention also includes equivalents thereof.

[0010] In addition, the screen examples shown in the drawings referenced in the following description are merely examples, and the screen configuration examples, the content of the information displayed, and the GUI (Graphical User Interface), etc. can be freely designed according to the system design specifications and user preferences, and can be changed as appropriate.

[0011] Furthermore, in this specification, the term "device" includes not only one device that performs a specified function alone, but also multiple devices that are separated from each other but work together to perform a specified function.

[0012] Furthermore, in this specification, a "user" refers to a user of the information processing device of the present invention, specifically, a user of the service provided by the information processing device of the present invention, and more specifically, a person who can be a recipient of videos distributed by the information processing device of the present invention. Note that, unless otherwise specified, a user is an individual, but a group of multiple people may be a user, and is a unit to which a video distribution service account is assigned.

[0013] [About the video distributed in this invention] Before describing the present invention, a description will be given of the moving image distributed in the present invention. In the present invention, the video is distributed to the user via a communication line network from the Internet or a mobile communication network, and is distributed in real time (live distribution) by streaming, for example. However, the present invention is not limited to this, and can also be applied to cases where recorded (recorded) video is downloaded and played.

[0014] In the present invention, the video is distributed in a digitized state in a specified file format, but since publicly known technologies can be used to film (record) the video, digitize it, and distribute the data, a description of the specific means used will be omitted.

[0015] In the present invention, a "voting support video" is distributed as a video. The voting support video is a video about voting that is performed by predicting the outcome of a specified competition, and is distributed to support users in voting. In detail, at least one performer appears in the voting support video, and during the filming of the video, the performer predicts the outcome of the specified competition before the competition is held, actually casts a vote, and checks whether or not their prediction was correct while watching the specified competition on TV or the like. Users can watch the voting support video and use it as a reference when casting their vote.

[0016] A "predetermined competition" is, for example, a competition in which you predict the outcome and bet, and if your prediction is correct you will receive a dividend (payout), that is, a public competition (race), and specific examples include horse racing, bicycle racing, boat racing, and auto racing. In addition to purchasing a betting ticket by paying a betting amount, voting also includes casting a bet by casting a pseudo currency (betting value) that can be used in a voting app described below. In addition, the specified competition is not limited to public competitions, but may be any competition for which users can predict the outcome and place votes, and may also be other competitions, such as sports matches that are subject to betting, gaming tournaments such as so-called e-sports, and other competitions in which winners and losers compete.

[0017] In this specification, it is assumed that races are held periodically according to a schedule, for example, every day. The voting support video is distributed with different content each day. To explain in detail, the voting support video for each day is a video that captures the conversations and actions of the participants related to the race held on that day, and can be distributed and viewed only on that day. It is to be noted that the race is not limited to being held every day, but may be held every few days, every week, every few weeks, every month, or every few months, or may be held irregularly.

[0018] Furthermore, races are held repeatedly within a specified period, for example, multiple times a day, and users can vote for each race during the day. To explain in more detail, the time periods during which races (specified competitions) are held and the time periods during which voting is accepted alternate during the day, and during each of the time periods, users can vote for the corresponding race, specifically, the race to be held immediately after the time period. In the voting support video, the performers vote at least twice a day. The predetermined period is not limited to one day, but may be one to several hours, several days, one to several weeks, one to several months, or one to several years. Also, the number of races held within the predetermined period may be only one, in which case users can vote for one race within the predetermined period.

[0019] Multiple voting support videos can be distributed every day, and different performers appear in each voting support video. At least one performer appears in each voting support video, and for example, as shown in FIG. 1, multiple performers form a group, and the voting support video is filmed and distributed on a group basis. Note that in a group appearing in a voting support video, each member (each performer belonging to the group) may jointly predict the result of the race and cast the same vote among themselves, or each member may cast a different vote among themselves.

[0020] Furthermore, multiple voting support videos distributed on the same day are all videos about races held on the same day (predetermined competitions held on the same day), and strictly speaking, the content deals with voting for the race. A user can watch at least one of the multiple voting support videos distributed on a certain day, and refer to the words and actions of the performers (more specifically, the performer group) in the viewed video to predict the outcome of the race held on that day and cast a vote.

[0021] Furthermore, as shown in Fig. 1, the voting support video also displays the details of the votes cast by the performers of the video on that day (in other words, the distribution date of the video) and information about the votes that were correct. Specifically, the voting details, vote amount, number of hits, hit rate, hit amount, number of wins, etc. are displayed. The user can refer to the above displayed information while watching the voting support video, and can, for example, cast a vote with the same predicted details as the vote cast by the performer of the video they are watching, i.e., cast a piggyback vote.

[0022] It should be noted that the "vote content" refers to the prediction content related to the vote, specifically, the predicted finishing order in the race, more specifically, the combination of the betting type such as a double bet, a trifecta bet, a trifecta bet, etc., and the predicted finishing order according to that type. "Vote amount" is the amount of money cast in a vote (including the amount cast using pseudo-currency). The "number of hits" is the number of votes for which the predictions were correct in one race, for example, the number of correct votes in the immediately preceding race. Note that if multiple races are held in one day, the "number of hits" may be the total number of correct votes up to the current point in time on that day. The "hit rate" is the percentage of votes that were correct in a given period of time, and is the hit rate at the current point in time on that day when multiple races are held in one day. Note that the hit rate may be calculated from the total number of votes cast by the cast of the voting support video during the distribution of the video, including videos distributed in the past, and the number of correct votes among those total. The "winning amount" is the payout (refund) for a bet that is a winning prediction in one race, for example the payout in the previous race. Note that if multiple races are held in one day, the total amount of winning amounts up to the current point in time for that day may be used as the "winning amount." The "number of wins" is the number of races in which predictions were correct, and for example, if multiple races are held in one day, it is the total number of correct predictions at the current point in time for that day. Note that the "number of wins" may be the total (cumulative) number of races in which a performer in a voting support video has predicted correctly in all of the videos he or she has distributed so far, including videos distributed in the past.

[0023] The voting support video can be viewed using a dedicated application software (hereinafter, voting app). In other words, when the voting app is installed on a user's terminal (hereinafter, user terminal) and the user performs a predetermined operation after launching the app, the user terminal receives and unpacks the distribution data of the voting support video, and as a result, the voting support video is displayed on the user terminal.

[0024] [Regarding the information processing device and user terminal of the first embodiment] An information processing device according to a first embodiment of the present invention (hereinafter, information processing device 10) and a user terminal used by a user (hereinafter, user terminal 12) will be described.

[0025] In the following description, a "viewer" refers to a user who is watching or has watched a voting support video. In addition, in order to make the following description easier to understand, the description will be given assuming a case in which a voting support video is distributed to one user (hereinafter, user γ). However, the present invention is of course also applicable to cases in which a voting support video is distributed to multiple users.

[0026] The information processing device 10 is composed of a server computer (corresponding to an example of a computer), and distributes voting support videos to user γ, and more specifically, communicates with a user terminal 12 used by user γ and executes information processing for distributing the voting support videos. The server computer constituting the information processing device 10 may be a single computer, or may be multiple computers distributed in parallel. In addition, the above server computer may be a server computer for ASP (Application Service Provider), SaaS (Software as a Service), PaaS (Platform as a Service), or IaaS (Infrastructure as a Service).

[0027] 2, the information processing device 10 and the user terminal 12 of the user γ are communicatively connected via a communication network 14, and cooperate with each other to configure an information processing system S. The communication network 14 may include a LAN (Local Area Network), a WAN (Wide Area Network), an intranet, Ethernet (registered trademark), and the like.

[0028] In the information processing system S, the server computer constituting the information processing device 10 performs a series of information processing related to the present invention (excluding input and display of information), including video distribution. The user terminal 12 inputs information to be handed over to the server computer, and outputs (displays, plays, etc.) information distributed from the server computer.

[0029] The information processing device 10 acquires voting support videos posted from multiple groups, analyzes each video, and distributes voting support videos that satisfy predetermined conditions based on the analysis results to user γ. The information processing device 10 also accepts votes for races made by users through the voting app described above, acquires the results of the races, and determines whether or not the predictions related to each accepted vote were correct. Furthermore, the information processing device 10 calculates the number of hits, number of wins, winning amount, and hit rate (hereinafter, hit numbers, etc.) for each accepted vote based on the judgment result of the prediction hit. Furthermore, the information processing device 10 distributes information related to the number of hits, etc. calculated for the performers of the voting support video to user γ together with the voting support video.

[0030] The user terminal 12 of user γ is composed of a personal computer, a smartphone, a mobile phone, a tablet terminal, or any other terminal with communication functions, and is used by user γ to view voting support videos distributed from the information processing device 10 and to predict the results of the race and place a vote.

[0031] Specifically, a voting application is installed on the user terminal 12, and this application receives data sent from the information processing device 10, expands the data, and displays various information on the display of the user terminal 12. The data sent from the information processing device 10 includes a voting support video and information about the performers in the video. The information about the performers in the video includes a profile of the performers (specifically, each member of the group appearing in the video), information about the votes made by the performers during the video distribution (specifically, the race for which the vote was cast, the vote content and the vote amount, etc.), and the number of hits by the performers, etc.

[0032] 1, the screen of the user terminal 12 while the app is running will be described. A voting support video is displayed in area T1 of the screen, and information about the performers of the video is displayed in area T2, which is different from area T1. A voting button Bt1 is displayed in area T3 at the bottom of the screen, and the user γ can cast a vote for the race by clicking the voting button Bt1 and inputting the voting details on a voting screen (not shown).

[0033] Furthermore, when a performer of the voting support video predicts the result of the next race (for example, the race to be held immediately after) during the video distribution and casts a vote, the predicted content and the bet amount are displayed in area T2, and a piggybacking vote button Bt2 is displayed in area T3. In this case, user γ can click the piggybacking vote button Bt2 to cast a vote with the same predicted content as the vote cast by the applicant of the voting support screen being viewed, that is, to cast a piggyback vote.

[0034] Although the screen will be different from that shown in FIG. 1, after the race ends, information regarding the race results, etc. will be displayed on the user terminal 12, and user γ will be able to check whether or not the prediction related to his / her bet was correct, and the amount of winnings (payout) if his / her prediction was correct, etc.

[0035] [Configuration of information processing device] Next, the configuration of the information processing device 10 will be described. As shown in FIG. 2, the server computer constituting the information processing device 10 has, as hardware devices, a processor 21, a memory 22, a communication interface 23, and a storage 24, and these devices are electrically connected via a bus 25.

[0036] In addition, the above server computer has installed therein, as software, a program for an operating system (OS) and an application program for video distribution. These programs correspond to the "programs" of the present invention. When the processor 21 operates according to the above programs, the server computer functions as an information processing device of the present invention and executes a series of processes related to the present invention. The above-mentioned program may be obtained by reading it from a computer-readable recording medium, or may be obtained by receiving (downloading) it via a network such as the Internet or an intranet.

[0037] The processor 21 may be composed of a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), an MCU (Micro Controller Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), a TPU (Tensor Processing Unit), an ASIC (Application Specific Integrated Circuit), or the like. The memory 22 may be configured with semiconductor memory such as a Read Only Memory (ROM) and a Random Access Memory (RAM).

[0038] The communication interface 23 may be, for example, a network interface card, a communication interface board, etc. The standard of data communication by the communication interface 23 is not particularly limited, and examples thereof include communication by wireless LAN based on Wi-fi (registered trademark), communication by a 3G to 5G or later generation mobile communication system, and communication based on LTE (Long Term Evolution).

[0039] The storage 24 may be configured with a flash memory, a hard disc drive (HDD), a solid state drive (SSD), a flexible disc (FD), a magneto-optical disc (MO disc), a compact disc (CD), a digital versatile disc (DVD), a secure digital card (SD card), or a universal serial bus memory (USB memory). The storage 24 may be built into the housing of a server computer that constitutes an information processing device, or may be attached to the server computer in an external form. The storage 24 may also be configured with an external computer (e.g., a database server) that is communicably connected to the server computer. As a technology for recording various data, a distributed ledger technology such as a block chain may be used to avoid unauthorized data tampering.

[0040] The configuration of the information processing device 10 will be explained again from the functional aspect. The information processing device 10 has the functional units shown in Fig. 3, namely, a video acquisition unit 31, a vote acquisition unit 32, a result acquisition unit 33, a storage unit 34, an update unit 35, a selection unit 36, a list transmission unit 37, and a video distribution unit 38. Of these functional units, the storage unit 34 is realized by the memory 22 or the storage 24, and the other functional units are realized by cooperation between various hardware devices of the server computer constituting the information processing device 10 and programs installed in the server computer. Each functional unit will be described in detail below.

[0041] The video acquisition unit 31 acquires the posted voting support videos on a group basis (i.e., for each performer group). In the first embodiment, since the voting support videos are distributed live, the video acquisition unit 31 acquires the voting support videos being shot in real time (strictly speaking, including delay times that occur during data communication).

[0042] The voting support video is posted with the video's ID number and distribution date and time, etc., and is also linked to information about the performers of the video (more specifically, the performer group and its members). The information about the performers includes the performers' attributes (categories), and specifically includes the gender, age, and occupation of each member of the performer group. The performers' attributes (categories) may be determined by the performers themselves, or may be determined by the information processing device 10 in accordance with a predetermined rule.

[0043] When a user casts a vote for a race, the vote acquisition unit 32 acquires information related to the vote from the user terminal 12 that cast the vote. Users from whom information related to the vote is acquired include user γ, performers in the voting support video, and viewers of voting support videos that are currently being distributed or have been distributed in the past. In the first embodiment, multiple races are held in one day, and voting is performed during reception time periods set between races. During each reception time period, the vote acquisition unit 32 acquires information related to the vote for the race that will be held immediately after.

[0044] When each race is completed, the result acquisition unit 33 acquires information about the result of the race. The information about the result of the race includes the ID of the race, the finishing order of each runner who participated in the race, information about the dividend according to the race result (specifically, odds), and other information about the race result.

[0045] The storage unit 34 stores various information necessary for information processing by the information processing device 10, including distribution of voting support videos. The information stored in the storage unit 34 includes user-related information, video-related information, voting-related information, race-related information, and winning result information, as shown in FIG.

[0046] User-related information is information about users, including the performers and viewers of the voting support video, and is stored for each user. As shown in Figure 4, user-related information includes the user's name, ID, profile, video viewing history, voting history, winning record, amount of in-game currency owned, and group to which the user belongs. The profile is information about the attributes (categories) of the user, such as gender, age, and occupation. The video viewing history is information indicating the viewing date and time of voting support videos that the user has viewed in the past, the video ID, and whether or not the user cast a piggyback vote while viewing the video. The voting history is information indicating the date and time of voting, the vote ID, and the like for votes that the user has previously cast. The winning record is information indicating the winning record of the predictions related to the votes made by the user, and specifically includes, for example, the winning amount won in the most recent race, as well as the winning rate, number of winnings, winning amount and number of wins on the day of the race. The group to which the user belongs is information indicating the performer group to which the user belongs, when the user is a performer in the voting support video.

[0047] The video related information is information about the posted voting support video, and is stored for each video. As shown in Fig. 5, the video related information includes the video ID, distribution date and time, the name of the group appearing in the video, performer information, attributes (category), target race information, evaluation value, etc. The performer information is information about the group and its members who appear in the video, and specifically includes each member's ID (user ID), the amount of votes each member cast for races held on the day the video is distributed, and the number of wins, number of hits, winning amounts, and hit rate on the day the video is distributed. The attribute (category) is information about the attribute (category) of the group appearing in the video, and is determined according to the attributes (for example, age, sex, occupation, etc.) of the members of the group. The target race information is information about a race whose result is predicted in the voting support video (hereinafter referred to as the target race), such as the ID of the target race, the date and time of the target race, and the venue. The evaluation value is an evaluation value (score) that is given to the voting support video by video analysis by the information processing device 10. The evaluation value will be described in detail later.

[0048] The voting-related information is information about a vote made by a user, and is stored for each vote. As shown in Fig. 6, the voting-related information includes the ID of the vote, the ID of the user who made the vote, the ID of the race to which the vote relates, the date and place of the race, the type of vote, the predicted content (i.e., the predicted finishing order), the amount of the vote, the payout rate (odds) if the prediction is correct, and the time of the vote. In addition, when a user makes a piggyback vote, the voting-related information includes information for identifying the voting support video (hereinafter referred to as the reference video) that the user was watching when the piggyback vote was made, specifically, the ID of the reference video, etc.

[0049] Race-related information is information about a race, and is stored for each race. As shown in Fig. 7, the race-related information includes the race ID, race grade (ranking), race date and time, venue, runner information, and voting candidate information. Voting candidate information is information about predicted finishing order candidates in a race, and as shown in Fig. 7, includes the candidate ID, voting details (specifically, the details of the predicted finishing order), odds, etc.

[0050] The winning result information is information indicating the winning result of the race result predicted by the user at the time of voting, and is stored for each vote. As shown in Fig. 8, the winning result information includes the vote ID, the ID of the user who cast the vote, the ID of the race for which the vote was cast, whether the prediction was correct or not, the amount of the dividend won if the prediction was correct (winning amount), and the payment deadline for the dividend (refund deadline).

[0051] The update unit 35 appropriately updates the information stored in the storage unit 34. For example, when the video acquisition unit 31 acquires a new voting support video, the update unit 35 adds video-related information related to the newly acquired voting support video to the storage unit 34. In addition, when the result acquisition unit 33 acquires information on the race results for a certain race, the update unit 35 reads out from the storage unit 34 vote-related information related to votes placed for the race, and determines whether or not each vote was a hit based on the information acquired by the result acquisition unit 33. Furthermore, the update unit 35 updates the hit result information and user-related information based on the determination result, and updates, for example, the number of wins, the number of hits, and the amount of hit of a user whose prediction was correct.

[0052] When multiple voting support videos are posted and acquired by the video acquisition unit 31, the selection unit 36 ​​selects some voting videos from the multiple voting support videos as candidate videos for distribution based on a predetermined criterion. Candidate videos for distribution are candidates for voting support videos to be distributed to user γ, and in the first embodiment, for example, three videos are selected as candidates for distribution. Note that the number of voting support videos selected as candidate videos for distribution is not particularly limited, and it is sufficient that at least one video is selected.

[0053] The predetermined criteria are set in advance as criteria (conditions) for selecting candidate videos for distribution, and for example, each of a plurality of voting support videos is evaluated, and a candidate video for distribution is selected according to the evaluation result of each video. In particular, in the first embodiment, an evaluation value is calculated for each voting support video, and a candidate video for distribution is selected according to the magnitude of the evaluation value.

[0054] Specifically, the selection unit 36 ​​has an analysis unit 36A and an evaluation value calculation unit 36B, as shown in Fig. 3. The analysis unit 36A analyzes each of the multiple voting support videos (strictly speaking, real-time voting support videos currently being shot) acquired by the video acquisition unit 31, based on the audio and images (video) in the video and various information stored in the storage unit 34. In the first embodiment, the analysis unit 36A analyzes each voting support video from the viewpoint of the degree of activity of the performers in the video (e.g., the performers' words and actions, expressions, etc.), the status of the votes cast by the performers for the race held on the video distribution day (e.g., the amount of votes cast, winning results, etc.), and the size of the viewers who support the performers (e.g., the number of viewers who cast piggyback votes, etc.).

[0055] In the first embodiment, multiple races are held in one day (a predetermined period), and when voting is performed for each race, the information stored in the memory unit 34 is updated as needed throughout the day by the update unit 35. In addition, the analysis unit 36A analyzes the voting support video every time a race is held in one day, and updates the analysis result.

[0056] The evaluation value calculation unit 36B evaluates each of the multiple voting support videos, and specifically, calculates an evaluation value for each voting support video according to the analysis results by the analysis unit 36A. In the first embodiment, the evaluation value calculation unit 36B calculates the evaluation value for each voting support video from the three perspectives described above. Also, in the first embodiment, as described above, the analysis results for each voting support video are updated at any time within a day (predetermined period), and when the analysis results are updated, the evaluation value calculation unit 36B recalculates the evaluation value for each voting support video based on the updated analysis results.

[0057] Then, the selection unit 36 ​​selects candidate videos for distribution based on the evaluation results (i.e., evaluation values) of each voting support video, and specifically, selects the top three voting support videos with the highest evaluation values ​​as candidate videos for distribution.

[0058] The list transmitting unit 37 transmits to user γ a ​​list indicating the distribution candidate videos selected by the selecting unit 36 ​​(i.e., voting support videos having the first to third highest evaluation values). Specifically, the list transmitting unit 37 reads out information (e.g., video-related information and user-related information, etc.) stored in the storage unit 34 for the voting support videos selected as distribution candidate videos, and generates list display data based on the read out information. The generated list display data is transmitted to the user terminal 12 of user γ, and is deployed in the user terminal 12 of user γ after receiving the list display data. As a result, the distribution candidate list shown in FIG. 9 is displayed on the user terminal 12 of user γ.

[0059] In the distribution candidate list, image information related to each of the three selected distribution candidate videos is displayed in a selectable manner. The image information related to the video may be a thumbnail image created based on a part of the distribution candidate video (specifically, a frame image), or may be a facial photo of a cast member of the distribution candidate video. User γ selects one of the distribution candidate videos and touches the image information of the selected video in the displayed distribution candidate list.

[0060] In addition, in the distribution candidate list, the placement positions of the videos of the three selected distribution candidates may be set according to the magnitude of their evaluation values. For example, the videos of the three distribution candidates may be placed from the top to the bottom of the list display screen in order of highest evaluation value.

[0061] The video distribution unit 38 distributes (more specifically, distributes in real time) to the user γ the voting support video selected by the user γ from among the distribution candidate videos displayed in the distribution candidate list. This allows the user γ to watch the video with the high evaluation value and selected by the user γ himself from among the multiple voting support videos posted.

[0062] [Regarding the information processing method according to the first embodiment] Next, as an example of the information processing method of the present invention, an information processing flow (hereinafter, video distribution flow) for distributing a voting support video to user γ using the information processing device 10 will be described. The video distribution flow employs the information processing method of the present invention. In other words, each step in the video distribution flow corresponds to a component of the information processing method of the present invention. It should be noted that the flow described below is merely an example, and unnecessary steps may be deleted, new steps may be added, or the order of steps may be changed without departing from the spirit of the present invention.

[0063] In the following, we assume that multiple races are held on a certain day (hereinafter referred to as a race day), and a voting support video is distributed in real time on the race day to user γ. In this case, the performers (more specifically, a performer group) of the voting support video predict the race results for each of the multiple races held on the race day and cast their votes.

[0064] The video distribution flow proceeds according to the flow shown in Fig. 10, and each step in the flow is mainly performed by a server computer (hereinafter simply referred to as computer) constituting information processing device 10. Specifically, at a predetermined time on the race day, multiple performer groups (hereinafter referred to as groups A to Z) each start shooting and posting a voting support video. The voting support video posted for each group corresponds to multiple videos. The computer acquires the voting support video for each group (S001).

[0065] Furthermore, each user, including the performers in the voting support video, predicts the result of the next race, i.e., the race to be held immediately after, during the voting acceptance period on the race day (i.e., the period between races), and casts a vote according to the prediction. The computer acquires information regarding the votes cast by each user (S002). At this time, the computer determines whether each vote is a piggyback vote or not, and for piggyback votes, identifies the reference video (S003, S004).

[0066] The computer also obtains information on the race results after the end of each race (S005), determines whether or not the predictions for each vote made during the immediately preceding reception period were correct, and determines the number of correct votes for those votes that were correct (S006, S007). At this time, the computer determines the number of correct votes for each of the performers in the voting support video, specifically, for each of groups A to Z, for example.

[0067] On the other hand, after starting step S001 (i.e., when the computer starts acquiring voting support videos), it executes a selection process (S008). In the selection process, the computer selects a predetermined number of videos (specifically, three) as candidates for distribution from among the voting support videos acquired for each group.

[0068] After the selection process is executed, the computer transmits a list showing the selected distribution candidate videos, i.e., a distribution candidate list, to user γ (S009). Specifically, the computer reads information on the voting support videos selected as distribution candidate videos from the storage unit 34, generates list display data based on the read information, and transmits the generated list display data to the user terminal 12 of user γ. In the user terminal 12 of user γ, the list display data is expanded to display the distribution candidate list (see FIG. 9).

[0069] Thereafter, when user γ selects one of the three voting support videos on the distribution candidate list (i.e., a video from one of groups A to Z) and touches it on the list display screen, the computer accepts the video selection by user γ (S010). This triggers the computer to distribute the voting support video selected by user γ to user terminal 12 of user γ, specifically, in real time (S011).

[0070] The above series of steps S001 to S011 are repeated until a predetermined end condition is met. The end condition is that the final race on the race day is finished, or that user γ stops the voting application on user terminal 12 and ends viewing of the voting support video, etc. Then, when the end condition is met, the video distribution flow for the race day is terminated.

[0071] Next, the selection step in the video distribution flow will be described in detail with reference to FIG. In the selection step, the computer executes an analysis process for each of the voting support videos acquired for each group in step S001, specifically, executes a first analysis process, a second analysis process, and a third analysis process (S021).

[0072] In the first analysis process, for each voting support video, the degree of activity in the video is identified based on at least one of the voices and images of the performers in the video (i.e., each member of groups A to Z), and specifically, the behavior of the performers is identified. To explain in more detail, in the first analysis process, the following (r1) to (r3) are identified for each of the multiple voting support videos. (r1) Number of people speaking in the video (r2) Content of the performers' speech (topics) (r3) Facial expressions of the performers in the video

[0073] The number of people speaking in a video can be determined based on the voices of the performers in the video using known speaker recognition technology such as voiceprint personal identification technology, etc. The number of people speaking may also be determined using image processing technology that analyzes the mouth movements in the images of the performers, or may be determined based on both the voices and images of the performers. The content of the speech of the performers can be identified by converting the speech of the performers into text (textual information) using speech recognition processing and natural language processing based on the audio in the video. The facial expressions of the performers can be identified by extracting the performers in the video using well-known object analysis algorithms such as R-CNN (Region-based CNN), Fast R-CNN, YOLO (You only Look Once), and SDD (Single Shot Multibox Detector), and then applying image analysis techniques for facial expression recognition based on FACS theory (Facial Action Coding System) based on the images of the extracted performers.

[0074] It is more preferable that the first analysis process specifies the degree of activity (specifically, the above items r1 to r3) during the voting acceptance period between races on the day the races are held.

[0075] In the second analysis process, for each voting support video, information regarding the votes placed by the cast members (i.e., each member of groups A to Z) on the race day is identified, specifically, the bet amount, winning results, etc. To explain in more detail, in the second analysis process, the following (r11) to (r13) are identified on a group basis for each of multiple voting support videos. (r11) The number of winning tickets in the previous race on the race day (r12) The number of consecutive votes in which the prediction was correct among multiple votes cast by the person appearing in the voting support video during the race day (r13) Change in voting amount for each vote on race day

[0076] The number of correct answers in the immediately preceding race on the race day is a numerical value relating to votes in which the predictions of the video performers in the immediately preceding race were correct, and is determined based on information stored in memory unit 34 after the immediately preceding race ended, specifically, user-related information, voting-related information, and correct result information of the performers in the voting support video. The number of consecutive votes with correct predictions during the race day is identified based on the video-related information of the voting support video stored in the storage unit 34, the voting-related information, and the correct result information. The change in betting amount on a race day is the increase or decrease in the betting amount for each race on the race day, and is determined based on the user-related information of the performers in the voting support video stored in memory unit 34 and the voting-related information.

[0077] In the second analysis process, instead of identifying the number of winning combinations in the immediately preceding race on the race day, or in addition to identifying the number of winning combinations in the immediately preceding race, the cumulative number of winning combinations up to the current point on the race day may be identified.

[0078] In the third analysis process, the size of the viewers who support the performers is identified for each voting support video, and specifically, the number of viewers who have participated in the piggybacking vote while watching the video is identified for each group. The number of viewers who have participated in the piggybacking vote is identified based on the video-related information and the voting-related information of the voting support video stored in the memory unit 34. In addition, in the third analysis process, instead of identifying the number of viewers who cast their votes while watching the video, or in addition to identifying the number of viewers who cast their votes, the number of viewers who cast their votes while watching the video may be identified regardless of whether the votes were different from the predictions made by the participants in the video.

[0079] After the analysis process is completed, the computer executes an evaluation process (S022) to evaluate each of the voting support videos acquired for each group in step S001. In the evaluation process, an evaluation value for each voting support video is calculated for each group based on the analysis result of the analysis process.

[0080] The procedure for calculating the evaluation value based on the analysis result will be described. When the number of people speaking in the video is identified in the first analysis process, if the number of people is more than one, the evaluation value is set to a higher value. Here, if the number of people speaking is the number of members of the group (i.e., three people), the evaluation value is set to the highest. However, this is not limited to this, and for example, if the number of members of the group is more than three (e.g., 10 or more people), the evaluation value may be lowered if the number of people speaking exceeds a predetermined number.

[0081] In addition, when the speech content of the performer is identified in the first analysis process, if the speech content contains certain words, for example, positive words, the evaluation value may be set to a higher value, and conversely, if the speech content contains negative words, the evaluation value may be set to a lower value. Note that the positive and negative words may be stored in the computer in advance as keywords for judgment.

[0082] In addition, when the facial expression of the performer is specified in the first analysis process, if the facial expression is a predetermined facial expression, for example, a positive facial expression such as a smile, the evaluation value is set to a higher value, and conversely, if the facial expression is a negative facial expression such as an angry face or a crying face, the evaluation value is set to a lower value. Note that it is preferable to store the positive and negative facial expressions in advance in the computer as facial expression patterns for judgment.

[0083] In addition, when the number of hits in the immediately preceding race is identified in the second analysis process, the greater the number of hits, the higher the evaluation value is set.In addition, when the number of consecutive votes with correct predictions is identified in the second analysis process, the greater the consecutive number of hits, the higher the evaluation value is set.In addition, when the change in the bet amount, i.e., the increase or decrease in the bet amount, is identified in the second analysis process, the greater the increase in the bet amount from the previous race, the higher the evaluation value is set.

[0084] Furthermore, when the number of viewers who have cast a piggyback vote while watching the video is identified in the third analysis process, the greater the number of viewers, the higher the evaluation value will be.

[0085] When calculating the evaluation value, factors other than the analysis results of the analysis process may be taken into account. For example, the evaluation value may be calculated based on the current number of viewers of the voting support video, or the cumulative number of viewers on the day of the race.

[0086] In the evaluation process, an evaluation value is calculated for each video (in other words, for each cast group) in the above manner. At this time, an evaluation value based on the analysis result of the first analysis process, an evaluation value based on the analysis result of the second analysis process, and an evaluation value based on the analysis result of the third analysis process may be calculated separately, and these three evaluation values ​​may be added together to obtain a final evaluation value. In this case, a weight may be set for each evaluation value, and each of the three evaluation values ​​may be multiplied by the corresponding weight, and the products may be summed up to obtain a final evaluation value.

[0087] After the evaluation process is completed, the computer selects a predetermined number (e.g., three) of candidate videos to be distributed from among the multiple voting support videos according to the evaluation value calculated for each video (S023). More specifically, the computer selects the predetermined number of voting support videos as candidate videos to be distributed in descending order of evaluation value. However, without being limited thereto, for example, multiple voting support videos may be divided by attribute (category), and the voting support video with the highest evaluation value for each attribute may be selected as a distribution candidate video. In this case, in the distribution candidate list subsequently transmitted to user γ, information (image information) regarding the distribution candidate videos may be displayed by category.

[0088] The selection process is repeatedly executed according to the above procedure during the race day, and more specifically, is executed each time a race is held on the race day. In other words, an evaluation value is calculated (updated) for each of the multiple voting support videos each time a race is held during the race day. Then, as the evaluation value of each video is updated, distribution candidate videos are reselected. Regarding the distribution candidate list transmitted to user γ, a new list may be transmitted each time a distribution candidate video is selected. Alternatively, the distribution candidate list may be transmitted only once after user γ operates user terminal 12 to launch the voting app. In this case, the distribution candidate list indicates the distribution candidate videos selected immediately before the app was launched.

[0089] [Regarding the second embodiment] In the first embodiment, a predetermined number of voting support videos are selected as distribution candidate videos in descending order of evaluation value. However, this is not limited to this, and for example, distribution candidate videos may be selected not only based on evaluation value but also taking into account narrowing down conditions specified by user γ. Such a configuration (hereinafter, the second embodiment) will be described. Note that, in the following, the points of the second embodiment that differ from the first embodiment will be mainly described.

[0090] The video distribution flow according to the second embodiment is generally common to the video distribution flow according to the first embodiment. On the other hand, in the video distribution flow according to the second embodiment, the user γ is made to specify in advance the filtering conditions for the videos of the candidates for distribution, and the specified filtering conditions are acquired in advance.

[0091] The filtering conditions are matters that should be prioritized when selecting videos to be distributed, and examples of such conditions include the popularity of the performers in each video, the number of hits made by the performers on race days, the number of viewers who voted on the spot while watching each video, etc. Furthermore, the attributes (category) of the video and the type of race (grade, type, race venue, etc.) may also be included in the filtering conditions.

[0092] Then, in the selection process in the video distribution flow according to the second embodiment, a video to be distributed is selected according to the evaluation value calculated for each voting support video and the narrowing down conditions specified by user γ. Specifically, in the second embodiment, when calculating the evaluation value for each voting support video, an evaluation value based on the analysis result of the first analysis process, an evaluation value based on the analysis result of the second analysis process, and an evaluation value based on the analysis result of the third analysis process are each calculated individually. Then, in the selection process, a video to be distributed is selected according to an evaluation value corresponding to the narrowing down conditions among the evaluation values ​​calculated for each type of analysis result. For example, if user γ specifies the success level of the performers of the video as a narrowing down condition, in the selection process, a video to be distributed is selected according to an evaluation value based on the analysis result of the first analysis process (i.e., the success level).

[0093] As described above, in the second embodiment, candidate videos for distribution are selected taking into consideration the narrowing down conditions specified by user γ, so that candidate videos for distribution can be determined in a manner that reflects the preferences or requests of user γ.

[0094] [Regarding the third embodiment] In the second embodiment, a candidate video for distribution is selected by taking into account the narrowing down conditions specified by the user γ, so as to reflect the preferences or requests of the user γ. On the other hand, if machine learning is performed on the characteristics of voting support videos that the user γ has previously watched, and the candidate videos for distribution are selected using the learning results, the candidate videos for distribution can be determined by more accurately reflecting the preferences of the user γ. Such an embodiment (hereinafter, the third embodiment) will be described with reference to FIGS. 12 and 13. In the following, the third embodiment will be described mainly in terms of differences from the first embodiment, and in FIG. 12, functional units common to the first embodiment are denoted by the same reference numerals as those in the first embodiment (i.e., the reference numerals shown in FIG. 3).

[0095] As shown in FIG. 12, the information processing device 10X according to the third embodiment further includes a feature identification unit 39 in addition to the functional units included in the information processing device 10 according to the first embodiment. The feature identification unit 39 identifies, by learning, the features of a voted video for which the user γ voted while watching, among the voting support videos that the user γ has previously viewed. To explain in more detail, the feature identification unit 39 identifies, by machine learning, the features of a predetermined video among the voted videos. The predetermined video is a video in which the user γ has piggybacked on a vote while watching the video, that is, cast a vote with the same predicted content as the vote cast by the performer of the predetermined video.

[0096] The features of a video are the contents that characterize the video, specifically, the attributes (category) of the performers in the video, the number of performers, commonalities between members of the performer group, the performers' words and actions in the video (activity level), the types of races for which the performers vote, and the voting trends of the performers (number of votes and amount of votes). When extracting features in a video, a publicly known annotation tool can be applied. An annotation tool is a tool that adds tag information (metadata) related to the target video as an annotation.

[0097] The feature identification unit 39 performs machine learning using training data that is a set of features (specifically, metadata) of voting support videos that user γ has previously viewed, and whether or not user γ has voted on the basis of piggybacking while viewing the video. There are no particular limitations on the machine learning algorithm, and examples that can be used include genetic programming, inductive logic programming, support vector machines, clustering, Bayesian networks, extreme learning machines (ELM), and decision tree learning. In addition, as a method for minimizing an objective function (loss function) in machine learning of a neural network, a gradient descent method or an error backpropagation method may be used.

[0098] In the third embodiment, as shown in FIG. 13, in the selection process during the video distribution flow, a computer constituting an information processing device 10X executes a feature identification process, and in the process, the features of a specific video (i.e., a video for which user γ has previously viewed a voting support video and for which he or she has voted on the spot while watching the video) are identified by machine learning (S031). The feature identification process is not limited to being performed during the video distribution flow, and may be performed before the video distribution flow is implemented.

[0099] The flow of the selection process after step S031 is the same as in the first embodiment, where the first to third analysis processes are executed for each of the multiple voting support videos posted on the race day (S032), an evaluation value is calculated based on the analysis results (S033), and candidate videos for distribution are selected based on the evaluation value (S034).

[0100] Thereafter, the computer (more specifically, the list sending unit 37) constituting the information processing device 10X sends a distribution candidate list indicating the selected distribution candidate videos to the user γ, and at this time, the distribution candidate list is determined according to the characteristics of the videos identified in step S031. Specifically, when the computer selects a predetermined number of distribution candidate videos in descending order of evaluation value, it determines whether each selected video has the characteristic identified in step S031, and adds the video having the characteristic to the distribution candidate list.

[0101] That is, in the third embodiment, a distribution candidate list indicating distribution candidate videos having the characteristics of a predetermined video selected according to the evaluation value and identified by machine learning is transmitted to user γ. As described above, in the third embodiment, distribution candidate videos can be determined according to the preferences of user γ, and more specifically, videos having the same characteristics as a video for which user γ has previously voted can be added to the distribution candidate list and recommended to user γ.

[0102] In the above case, the characteristics of the voted videos in which the user γ voted while watching the video were identified among the voting support videos that the user γ had previously viewed, but the present invention is not limited to this. For example, the characteristics of videos with long viewing times (specifically, videos with viewing times exceeding a predetermined time) among the voting support videos that the user γ had previously viewed may be identified, and distribution candidate videos may be selected based on the identification results.

[0103] [Other embodiments] Up to this point, the information processing device, information processing method, and program of the present invention have been described using specific examples. However, the above-described embodiment is merely an example, and other embodiments may be conceived.

[0104] In the above embodiment, the server computer performs the functions of the information processing device of the present invention, but some of the functions of the server computer, for example, the selection unit 36, may be realized by the user terminal 12. In other words, the user terminal 12 may evaluate each of the multiple voting support videos based on the popularity of the performers and the number of hits, and select a candidate video for distribution according to each evaluation result.

[0105] In addition, in the above embodiment, when evaluating each of the multiple voting support videos, each video is analyzed, and specifically, the speech and facial expressions of the performers in the video, the amount of votes cast by the performers, the number of winning votes, and the number of viewers who voted piggybacking while watching the video are identified. Then, each voting support video is evaluated according to the identified analysis results (i.e., the results of the analysis performed from three perspectives), and more specifically, an evaluation value is calculated based on each of the analysis results performed from the three perspectives. However, without being limited to this, each voting support video may be evaluated based on any of the analysis results performed from the above three perspectives.

[0106] Furthermore, in the above embodiment, when evaluating each voting support video that can be distributed during a specified period, information obtained during that distribution period is used, such as the number of hits by the performers during the distribution period, and the number of viewers who took advantage of the distribution period to cast their votes. In other words, in the above embodiment, even if the voting support video features the same performers, if the distribution period (distribution date) changes, the information used for the evaluation will change, and the evaluation (evaluation value) will also fluctuate. However, this is not limited to the above, and the voting support video may be evaluated using fixed information that is not affected by the distribution period (for example, personal information of the performers, etc.).

[0107] In the above embodiment, the selection unit 36 ​​uses the evaluation results of each video, more specifically the magnitude of the evaluation value of each video, as a criterion (predetermined criterion) when selecting a candidate video for distribution from among multiple voting support videos, but this is not limited to this. For example, without evaluating each video, candidate videos for distribution may be selected by narrowing down the users as in the second embodiment described above, or the machine learning in the third embodiment described above may be used to identify characteristics of a predetermined video, and a voting support video containing the characteristics may be selected as a candidate video for distribution.

[0108] [summary] The information processing device of the present invention is an information processing device that distributes videos related to voting to predict the outcome of a specified competition to users, and is equipped with a selection unit that selects some videos from a plurality of videos as candidate videos for distribution based on specified criteria, and a list transmission unit that transmits a list showing the selected candidate videos for distribution to the user. According to the above configuration, videos that can be recommended to a user are selected based on a predetermined criterion from among a plurality of videos related to voting, and the selected videos are presented to the user as videos to be distributed, thereby making it possible to effectively distribute videos to the user. As a result, the user can watch videos that are useful for voting.

[0109] In addition, in the information processing device of the present invention, the selection section may select candidate videos to be distributed from among the multiple videos, depending on an evaluation result regarding at least one of the audio and the image of the performers in the videos. According to the above configuration, when selecting candidate videos for distribution from among multiple videos, the selection is made based on the evaluation results of the audio and visual appearance of the performers in each video, so that more appropriate videos, for example videos in which the performers are highly popular, can be presented to the user as candidates for distribution.

[0110] In the above configuration, the selection unit may calculate an evaluation value for each of the multiple videos based on the number of performers who are speaking in the video, and select candidate videos for distribution according to the evaluation value of each video. In this case, it is preferable that the selection unit assign a higher evaluation value to a video in which multiple performers are speaking. According to the above configuration, the evaluation value of each video is calculated based on the number of performers who are speaking in the video, so that the degree of popularity of the video can be appropriately evaluated. Then, by selecting candidate videos for distribution according to the evaluation value, more appropriate videos (i.e., videos with high degrees of popularity) can be presented to the user.

[0111] In the above configuration, the selection unit may specify facial expressions of performers in each of the multiple videos from the images of the performers, calculate an evaluation value based on the specified facial expressions of the performers, and select candidate videos for distribution according to the evaluation value of each video. In this case, it is preferable that the selection unit assign a higher evaluation value to videos in which performers with a predetermined facial expression are shown. According to the above configuration, the degree of popularity of each video can be more appropriately evaluated by calculating an evaluation value for each video based on the facial expressions of the performers in the video. Then, by selecting candidate videos for distribution according to the evaluation value, more appropriate videos (i.e., videos with high degrees of popularity) can be presented to the user.

[0112] In the information processing device of the present invention, the selection section may select candidate videos for distribution from among a plurality of videos in accordance with an evaluation result based on information regarding votes by performers in the videos. According to the above configuration, when selecting candidate videos for distribution from among multiple videos, the selection is made based on the evaluation results of the voting content and winning results by the performers of each video, etc., so that more appropriate videos, for example videos that are useful to the user when voting, can be presented to the user as candidates for distribution.

[0113] In addition, in the case where a predetermined competition is held multiple times within a predetermined period and voting is conducted for each predetermined competition, the selection unit may calculate an evaluation value for each of the multiple videos based on the numerical value of votes for which the performers of the videos correctly predicted the results of the predetermined competition immediately prior to the predetermined period, and select candidate videos for distribution according to the evaluation value of each video. In this case, it is preferable for the selection unit to set a higher evaluation value as the numerical value is larger. According to the above configuration, an evaluation value is calculated for each video based on the numerical values ​​(e.g., betting amount, dividend, etc.) related to the votes that were correctly predicted in the specified competition in the previous round, and candidate videos for distribution are selected according to the evaluation value, so that more appropriate videos (i.e., videos that are useful for the user when voting) can be presented to the user.

[0114] The selection unit may also calculate an evaluation value for each of the multiple videos based on the number of consecutive votes that were correct among multiple votes cast by the performers of the videos within a predetermined period, and select candidate videos for distribution according to the evaluation value of each video. In this case, it is preferable for the selection unit to set a higher evaluation value for a larger number of consecutive votes. According to the above configuration, an evaluation value is calculated for each video based on the number of consecutive votes that are correct, and candidate videos for distribution are selected based on the evaluation value, so that more appropriate videos (i.e., videos that are useful for the user when voting) can be presented to the user.

[0115] In addition, in cases where a specified competition is held multiple times within a specified period and voting is conducted for each specified competition, the selection unit may calculate an evaluation value for each of the multiple videos based on the change (increase or decrease) in the voting amount for each vote cast by the participants in the video during the specified period, and select candidate videos for distribution based on the evaluation value of each video. According to the above configuration, an evaluation value is calculated for each video based on the change (increase or decrease) in the voting amount for each vote by the performers in that video, and candidate videos for distribution are selected based on the evaluation value, so that more appropriate videos (i.e., videos that are useful for the user when voting) can be presented to the user.

[0116] In addition, in the information processing device of the present invention, the selection unit may select a candidate video for distribution from among a plurality of videos in accordance with an evaluation result based on information about viewers of the video who voted while watching the video. According to the above configuration, when selecting candidate videos for distribution from among multiple videos, the selection is made based on evaluation results regarding, for example, the number of viewers who voted while watching each video, so that more appropriate videos, for example videos that are useful for the user to vote, can be presented to the user as candidates for distribution.

[0117] In addition, in the above configuration, the selection unit may calculate an evaluation value for each of the multiple videos based on the number of viewers who, while watching the video, cast votes with the same predicted content as those cast by the video's performers, and select candidate videos for distribution based on the evaluation value of each video. According to the above configuration, for each video, an evaluation value is calculated based on the number of viewers who voted in the same predicted manner as the cast of the video (i.e., piggybacking votes), and candidate videos for distribution are selected based on the evaluation value, so that more appropriate videos (i.e., videos that are useful for the user when voting) can be presented to the user.

[0118] In addition, in cases where the time period during which a specified competition is held and the time period during which voting is accepted alternately, the selection unit may calculate an evaluation value for each of a plurality of videos during the time period during which voting is accepted, and select candidate videos for distribution based on the evaluation value of each video. According to the above configuration, for each video, an evaluation value of the video during the voting acceptance time period is calculated. Here, during the voting acceptance time period, the performers of the video are voting and the video is having fun. Therefore, by selecting candidate videos for distribution according to the evaluation value of the video during the voting acceptance time period, it is possible to present to the user more appropriate videos, i.e., videos with high activity rates and videos that are useful for the user to vote.

[0119] In addition, in the case where a specified competition is held multiple times within a specified period and voting is conducted for each specified competition, the selection unit may calculate an evaluation value for each of a number of videos that are distributed in real time within the specified period, each time a specified competition is held within the specified period, and select candidate videos for distribution based on the evaluation value of each video. When each predetermined competition is held within a predetermined period, the evaluation results of the videos may change accordingly. In the above configuration, taking this into consideration, the evaluation value of each video is calculated each time a predetermined competition is held, and distribution candidates are selected according to the evaluation value. This makes it possible to select distribution candidate videos based on the current evaluation value, so that videos that can be recommended at the current time can be effectively distributed to users.

[0120] In addition, the information processing device of the present invention may have a feature identification unit that identifies, through learning, the features of voted videos for which the user voted while viewing, among the videos that the user has viewed in the past, and the list transmission unit transmits to the user a list indicating candidate videos for distribution that have the features selected by the selection unit and identified by the feature identification unit. According to the above configuration, the characteristics of videos that a user has previously viewed and voted for while viewing (voted videos) are identified, and when videos that are candidates for distribution are presented to the user, the characteristics of the voted videos are reflected. This makes it possible to present videos that are candidates for distribution to the user based on the user's preferences and inclinations.

[0121] In the above configuration, the feature identification unit may identify the feature of a predetermined video among the videos with votes. In this case, the predetermined video may be a video in which a user has voted for the same predicted content as a vote by a cast member of the predetermined video while watching the predetermined video. According to the above configuration, the characteristics of a video with votes in which the user has cast the same expected vote as the cast member (i.e., a piggyback vote) are identified, and the characteristics are reflected when videos that can be distributed to the user are presented. This makes it possible to present videos that can be distributed to the user based on the user's inclination (thoughts regarding voting).

[0122] In addition, the information processing method of the present invention is an information processing method for distributing videos related to voting to predict the results of a specified competition to a user, characterized in that a computer selects some videos from a plurality of videos as candidate videos for distribution based on specified criteria, and transmits a list showing the selected candidate videos for distribution to the user. By using the above-described information processing method, videos related to voting can be effectively distributed to users, allowing the users to watch videos that are useful for voting.

[0123] In addition, the program of the present invention is a program for distributing videos to users regarding voting that predicts the results of a specified competition, and causes a computer to select some videos from a plurality of videos as candidate videos for distribution, and to transmit a list showing the selected candidate videos for distribution to the user. By causing a computer to execute the above program, videos relating to voting can be effectively distributed to users, allowing users to watch videos that are useful for voting. [Explanation of symbols]

[0124] 10,10X Information Processing Device 12 User terminal 14 Telecommunications Network 21 Processors 22 Memory 23 Communication Interface 24 Storage 25 Bus 31 Video Acquisition Section 32 Vote Acquisition Department 33 Result acquisition part 34 Storage section 35 Update section 36 Selection Department 36A Video Analysis Section 36B Evaluation value calculation unit 37 List transmission section 38 Video Distribution Department 39 Feature Analysis Unit S Information Processing System

Claims

1. a processor, the processor comprising: Accepting votes from users regarding the results of the competition; Calculating a first evaluation value for each of the plurality of videos related to the vote, based on at least one of the voice and the image of the performers in the video; When a narrowing-down condition corresponding to the first evaluation value is designated by the user among a plurality of narrowing-down conditions corresponding to each of a plurality of evaluation values ​​including at least the first evaluation value, a list showing one or more videos selected based on the first evaluation value is displayed on a user terminal of the user; one video selected by the user from among the videos included in the displayed list is delivered to the user terminal; Information processing device.

2. The processor accepts votes from users regarding the outcome of the competition; A processor calculates a first evaluation value for each of the plurality of videos related to the vote, the first evaluation value being based on at least one of the voice and the image of the performer in the video; a processor, when a narrowing-down condition corresponding to the first evaluation value is designated by the user among a plurality of narrowing-down conditions corresponding to each of a plurality of evaluation values ​​including at least the first evaluation value, causes a user terminal of the user to display a list showing one or more videos selected based on the first evaluation value; A processor delivers, to the user terminal, one video selected by the user from among the videos included in the displayed list. Information processing methods.

3. causing the processor to accept votes from users regarding the outcome of the competition; A processor is caused to calculate a first evaluation value for each of the plurality of videos related to the vote, the first evaluation value being based on at least one of a voice and an image of a performer in the video; a processor, when a narrowing-down condition corresponding to the first evaluation value is designated by the user, among a plurality of narrowing-down conditions corresponding to each of a plurality of evaluation values ​​including at least the first evaluation value, causing a list indicating one or more videos selected based on the first evaluation value to be displayed on a user terminal of the user; causing the processor to deliver, to the user terminal, one video selected by the user from among the videos included in the displayed list; A program for executing a process.

4. A server and a user terminal are provided, The server, accepting votes from users on the results of the competition based on input to the user terminal; Calculating a first evaluation value for each of the plurality of videos related to the vote, based on at least one of the voice and the image of the performers in the video; When a narrowing-down condition corresponding to the first evaluation value is designated by the user among a plurality of narrowing-down conditions corresponding to each of a plurality of evaluation values ​​including at least the first evaluation value, a list showing one or more videos selected based on the first evaluation value is displayed on the user terminal; one video selected by the user from among the videos included in the displayed list is delivered to the user terminal; system.

Images