Information processing device, information processing method, and program

Abstract

To provide an information processing device capable of more effectively delivering to a user a moving image regarding voting performed with prediction of a result of a predetermined game.SOLUTION: The information processing device which delivers to a user a moving image regarding voting performed with prediction of a result of a predetermined game comprises: a selection part which selects some of a plurality of moving images on the basis of a predetermined reference, as moving images of delivery candidates; and a list transmission part which transmits to the user a list indicative of the moving images of the selected delivery candidates.SELECTED DRAWING: Figure 10

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 over a network and view videos posted by other users. Recently, technologies have been developed that automatically classify a huge number of videos and allow users to select videos to distribute from a predetermined classification, as well as technologies 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] Japanese Patent Application Laid-Open No. 2002-320214 Summary of the Invention [Problem to be solved by the invention]

[0004] It is conceivable that content related to betting (betting) on ​​the outcome of a predetermined competition will be distributed as a video. When distributing such videos to users, it is preferable to distribute them to users more effectively, and this need 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 deliver to users videos related to voting that predicts the results of a specified competition. [Means for solving the problem]

[0006] An information processing device according to one aspect of the present inventor 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 comprising 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. [Effects of the Invention]

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

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

[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) shown in the accompanying drawings. It should be noted that 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. That is, the present invention may be modified or improved from the embodiment described below without departing from the spirit of the present invention. Furthermore, it goes without saying that the present invention also includes equivalents thereof.

[0010] Furthermore, 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 predetermined function alone, but also multiple devices that are separate from each other but work together to perform a predetermined function.

[0012] Furthermore, in this specification, the term "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 can also be a user, and is the unit to which a video distribution service account is assigned.

[0013] [Regarding the video distributed in this invention] Before describing the present invention, a description will be given of the video to be distributed in the present invention. In the present invention, the video to be distributed is distributed to users via a communication network such as 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 (pre-recorded) video is downloaded and played back.

[0014] In the present invention, videos are distributed in a digitized form in a specified file format, but the specific means used to shoot (record) videos, digitize them, and distribute them will not be explained here, as publicly known technologies can be used.

[0015] In the present invention, a "voting support video" is distributed as a video. The voting support video is a video related to voting by predicting the outcome of a predetermined competition, and is distributed to support users in voting. Specifically, the voting support video features at least one performer, and while filming the video, the performer predicts the outcome of the predetermined competition before it takes place, actually casts a vote, and watches the predetermined competition on TV or the like to check whether their prediction was correct. 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), i.e., a publicly managed 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 vote by casting a pseudo-currency (betting value) that can be used in a voting app, which will be described later. In addition, the specified competition is not limited to publicly managed competitions, but may be any competition for which users can predict the outcome and place votes, and may also include 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 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 more detail, each day's voting support video is a video that captures the conversations and actions of participants related to the race held on that day, and can be distributed and viewed only on that day. Note that races are not limited to being held every day, but may also 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 predetermined period, for example, multiple times a day, and users can vote for each race during that day. To explain in more detail, the time periods during which races (predetermined competitions) are held and the time periods during which voting is accepted alternate throughout the day, and users can vote for the corresponding race during each of the time periods, specifically the race that will be held immediately after that time period. In the voting support video, the cast members 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 daily, and each voting support video features a different cast member. Each voting support video features at least one cast member; for example, as shown in FIG. 1, multiple cast members form a group, and a voting support video is filmed and distributed on a group-by-group basis. Note that in a group appearing in a voting support video, each member (each cast member belonging to the group) may jointly predict the outcome 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. Users can watch at least one of the multiple voting support videos distributed on a given day, and, based on the words and actions of the performers (more specifically, performer groups) in the video they watched, predict the outcome of the race held on that day and cast their vote.

[0021] As shown in Figure 1, the voting support video also displays the details of the votes cast by the cast of the video on that day (in other words, the day the video was released) and information about the votes that were correct. Specifically, the details of the vote, the amount of the vote, the number of hits, the hit rate, the amount of the hit, and the number of wins are displayed. Users can refer to the above displayed information while watching the voting support video, and can, for example, cast a vote with the same prediction as the cast of the video they are watching, i.e., piggyback vote.

[0022] The "voting content" refers to the predicted content of the vote, specifically the predicted content of the finishing order in the race, more specifically the combination of betting types such as quinella, trifecta, trifecta, etc., and the predicted finishing order corresponding 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" refers to the number of votes for which the predictions were correct in one race, for example, the number of hit 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 hit votes up to that point in time on that day. The "hit rate" is the percentage of votes for which predictions were correct during a specified period. For example, if multiple races are held in one day, it is the hit rate at the current point in time for that day. Note that the hit rate may also be calculated from the total number of votes cast by the cast of the voting support video during the distribution of the video, including previously distributed videos, and the number of correct votes among those total. The "winning amount" is the payout (refund) for a bet that predicts a race correctly, for example, the payout for the most recent race. If multiple races are held in one day, the total amount of winning amounts up to that point on that day may be used as the "winning amount." The "number of wins" refers to the number of races in which predictions were correct. For example, if multiple races are held in a single day, it refers to the total number of correct predictions for that day. Note that the "number of wins" may also refer to the total (cumulative) number of races in which the performer of the voting support video has made correct predictions in all of the videos he or she has distributed so far, including videos distributed in the past.

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

[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 referred to as information processing device 10) and a user terminal used by a user (hereinafter referred to as 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. For ease of understanding, the following description will be based on the assumption that a voting support video is distributed to one user (hereinafter, user γ). However, the present invention is, of course, also applicable to cases where 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. More specifically, it 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 multiple computers distributed in parallel. Furthermore, the server computer may be a server computer for an 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 user γ are communicatively connected via a communication network 14, and cooperate with each other to form 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 meet predetermined conditions based on the analysis results to user γ. The information processing device 10 also accepts votes for races submitted by users through the voting app described above, acquires the results of the races, and determines whether the predictions for 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 whether the predictions were correct. Furthermore, the information processing device 10 distributes information regarding the calculated hit numbers, etc. for the performers in the voting support videos to user γ along with the voting support videos.

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

[0031] Specifically, a voting app is installed on the user terminal 12, and this app 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 information about the voting support video and the performers in the video. The information about the performers in the video includes profiles 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 content of the vote, the amount of the vote, etc.), and the number of winning predictions made 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 in 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 user γ can cast a vote for the race by clicking on the voting button Bt1 and entering the details of the vote on a voting screen (not shown).

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

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

[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 that constitutes the information processing device 10 has, as hardware devices, a processor 21, a memory 22, a communication interface 23, and a storage 24, which are electrically connected via a bus 25.

[0036] Furthermore, the 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 in accordance with the programs, the server computer functions as the information processing device of the present invention and executes a series of processes related to the present invention. The above 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 configured by a CPU (Central Processing Unit), MPU (Micro-Processing Unit), MCU (Micro Controller Unit), GPU (Graphics Processing Unit), DSP (Digital Signal Processor), TPU (Tensor Processing Unit), or ASIC (Application Specific Integrated Circuit), etc. The memory 22 may be configured by semiconductor memories such as a ROM (Read Only Memory) and a RAM (Random Access Memory).

[0038] The communication interface 23 may be configured by, 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 include communication by a wireless LAN based on Wi-fi (registered trademark), communication by a 3G to 5G or later generation mobile communication system, or 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), a universal serial bus memory (USB memory), or the like. The storage 24 may be built into the housing of a server computer constituting an information processing device, or may be attached to the server computer in an external format. The storage 24 may also be configured with an external computer (e.g., a database server) communicably connected to the server computer. A distributed ledger technology such as a blockchain may be used as a technology for recording various data in order to avoid unauthorized data tampering, etc.

[0040] The configuration of the information processing device 10 will be explained again from a functional perspective. The information processing device 10 has functional units shown in Fig. 3, namely, a video acquisition unit 31, a vote acquisition unit 32, a result acquisition unit 33, a memory 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 memory 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 that constitutes the information processing device 10 and programs installed on 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] Voting support videos are posted with the video's ID number, distribution date and time, etc., incorporated, and are 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 performer's attributes (category), specifically, the gender, age, occupation, etc. of each member of the performer group. The performer's attributes (category) may be determined by the performer themselves, or may be determined by the information processing device 10 in accordance with predetermined rules.

[0043] When a user casts a vote for a race, the vote acquisition unit 32 acquires information about the vote from the user terminal 12 that cast the vote. Users from whom information about the vote is acquired include user γ, as well as 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 takes place during reception time slots set between races. During each reception time slot, the vote acquisition unit 32 acquires information about votes for the race that will be held immediately afterwards.

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

[0045] The storage unit 34 stores various types of 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 cast 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 results, amount of in-game currency owned, and group affiliation. 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, video ID, and whether or not a user has cast a piggyback vote while viewing a video, for voting support videos that the user has viewed so far. The voting history is information indicating the date and time of voting, the vote ID, and the like for the votes that the user has voted so far. The hit record is information indicating the hit record of the predictions related to the votes made by the user, and specifically includes, for example, the amount of winnings won in the most recent race, as well as the hit rate, number of hits, amount of winnings, 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 if 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, and evaluation value. The performer information is information about the group and its members appearing in the video, and specifically includes each member's ID (user ID), the amount of votes each member cast for the race held on the day the video is distributed, as well as the number of wins, number of hits, amount of hits, 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 the race (hereinafter referred to as the target race) whose results are predicted in the voting support video, such as the ID of the target race, the date and venue of the target race, etc. The evaluation value is an evaluation value (score) given to the voting support video through 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 related to the vote, the date, time, and location of the race, the type of vote, the predicted content (i.e., the predicted finishing order), the amount of the vote, the payout ratio (odds) if the prediction is correct, and the time of the vote. In addition, if 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.

[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 (rating), race date and time, venue, runner information, and voting candidate information. The 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 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 that the vote was about, 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 about the newly acquired voting support video to the storage unit 34. Furthermore, when the result acquisition unit 33 acquires information about the race results of a certain race, the update unit 35 reads out from the storage unit 34 voting-related information about the votes cast 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, such as updating the number of wins, number of hits, and winning amounts of users whose predictions were correct.

[0052] When multiple voting support videos are posted and acquired by the video acquisition unit 31, the selection unit 36 ​​selects some voting support videos from the multiple voting support videos as candidate videos to be distributed based on predetermined criteria. Candidate videos to be distributed are candidates for voting support videos to be distributed to user γ, and in the first embodiment, for example, three videos are selected as candidate videos to be distributed. Note that the number of voting support videos selected as candidate videos to be distributed is not particularly limited, as long as 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 candidate videos for distribution are selected according to the evaluation results of each video. In particular, in the first embodiment, an evaluation value is calculated for each voting support video, and candidate videos for distribution are selected according to the magnitude of the evaluation value.

[0054] 3, the selection unit 36 ​​has an analysis unit 36A and an evaluation value calculation unit 36B. The analysis unit 36A analyzes each of the multiple voting support videos acquired by the video acquisition unit 31 (strictly speaking, real-time voting support videos currently being shot) 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 perspective of the level of engagement of the performers in the video (e.g., the performers' words, actions, and facial expressions), the status of betting by the performers for races held on the video distribution day (e.g., betting amounts, 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 (predetermined period), and as votes are cast 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 results.

[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 based on the analysis results by the analysis unit 36A. In the first embodiment, the evaluation value calculation unit 36B calculates an 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 as needed 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 transmission unit 37 transmits to user γ a ​​list showing the distribution candidate videos selected by the selection unit 36 ​​(i.e., voting support videos with the first to third highest evaluation values). Specifically, the list transmission unit 37 reads information (e.g., video-related information and user-related information) 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 information. The generated list display data is transmitted to the user terminal 12 of user γ, and is displayed 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] The distribution candidate list displays selectable image information for each of the three selected distribution candidate videos. The image information for the video may be a thumbnail image created based on a portion of the distribution candidate video (specifically, a frame image), or may be a photograph 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 three selected distribution candidate videos may be set according to the evaluation value. For example, the three distribution candidate videos may be placed from the top to the bottom of the list display screen in descending order of 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 highest evaluation value and selected by the user γ himself from among the multiple posted voting support videos.

[0062] [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. The flow described below is merely an example, and unnecessary steps may be deleted, new steps may be added, or the order in which steps are performed may be changed, without departing from the spirit of the present invention.

[0063] In the following, we will assume that multiple races are held on a certain day (hereinafter referred to as a race day), and that voting support videos are distributed in real time on the race day to user γ. In this case, the performers (more specifically, performer groups) in the voting support videos 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 the computer) that constitutes information processing device 10. Specifically, at a predetermined time on the day of the race, multiple performer groups (hereinafter referred to as groups A to Z) each begin filming and posting a voting support video. The voting support video posted by each group corresponds to multiple videos. The computer acquires the voting support video for each group (S001).

[0065] Furthermore, each user, including the cast members of the voting support video, predicts the outcome 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 their prediction. The computer acquires information about the votes cast by each user (S002). At this time, the computer determines whether each vote is a piggyback vote, and for piggyback votes, identifies the referring 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 of the votes 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, for example, the performers in the voting support video, specifically, for each of groups A to Z.

[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) from the voting support videos acquired for each group as candidate videos for distribution.

[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 about 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. The list display data is expanded on the user terminal 12 of user γ, and the distribution candidate list is displayed (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 termination condition is met. The termination condition may be that the final race on the race day has finished, or that user γ has stopped the voting app on user terminal 12 and finished watching the voting support video. Once the termination condition is met, the video distribution flow for the race day ends.

[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 determined based on at least one of the audio and image of the performers in the video (i.e., each member of groups A to Z), and more specifically, the behavior of the performers is determined. More specifically, in the first analysis process, the following (r1) to (r3) are determined 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 mouth movements in images of the performers, or may be determined based on both the voices and images of the performers. The content of the performers' speech can be identified by converting the performers' speech into text (character information) using speech recognition processing and natural language processing based on the audio in the video. The facial expressions of performers can be identified by extracting performers from a 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] In the first analysis process, it is more preferable to identify the degree of activity (specifically, the above items r1 to r3) during the betting acceptance period between races on the day the race is held.

[0075] In the second analysis process, for each voting support video, information regarding the votes cast by the cast members (i.e., each member of groups A to Z) on the race day is identified, specifically the betting amounts, winning results, etc. More specifically, in the second analysis process, for each of the multiple voting support videos, the following (r11) to (r13) are identified on a group-by-group basis. (r11) Number of winnings in the race immediately preceding the race date (r12) The number of consecutive votes in which the prediction was correct among multiple votes cast by the cast of the voting support video during the race day. (r13) Change in voting amount for each vote on the race day

[0076] The number of correct predictions in the immediately preceding race on the race day is a numerical value relating to the votes in which the predictions of the video participants were correct in the immediately preceding race, and is determined based on the information stored in memory unit 34 at the time after the immediately preceding race ended, specifically, the user-related information, voting-related information, and correct result information of the participants in the voting support video. The number of consecutive votes with correct predictions within a 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 audience that supports the performer for each voting support video is identified, specifically, the number of viewers who cast their piggyback votes while watching the video is identified for each group. The number of viewers who cast their piggyback votes is identified based on the video-related information and 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 content of the votes predicted by the video's performers is different.

[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 results of the analysis process.

[0080] The procedure for calculating the evaluation value based on the analysis results will be described below. When the number of people speaking in the video is identified in the first analysis process, if there is more than one person, 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., ten or more people), the evaluation value may be lowered if the number of people speaking exceeds a predetermined number.

[0081] Furthermore, when the speech content of the performer is identified in the first analysis process, if the speech content contains predetermined 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 positive and negative words may be stored in advance in the computer as keywords for judgment.

[0082] Furthermore, when the facial expression of a performer is identified 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 advisable to store positive and negative facial expressions in advance in the computer as facial expression patterns for judgment.

[0083] Furthermore, when the number of winning predictions in the immediately preceding race is identified in the second analysis process, the greater the value of the winning predictions, the higher the evaluation value will be. Furthermore, when the number of consecutive bets with correct predictions is identified in the second analysis process, the greater the consecutive number of bets, the higher the evaluation value will be. Furthermore, when the change in bet amount, i.e., the increase or decrease in bet amount, is identified in the second analysis process, the greater the increase in bet amount since the previous race, the higher the evaluation value will be.

[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 consideration. 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] Then, in the evaluation process, an evaluation value is calculated for each video (in other words, for each performer group) in the above manner. At this time, an evaluation value based on the analysis results of the first analysis process, an evaluation value based on the analysis results of the second analysis process, and an evaluation value based on the analysis results 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 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 to this, 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, the distribution candidate list subsequently sent to user γ may display information (image information) about the distribution candidate videos by category.

[0088] The selection process is repeatedly performed according to the above procedure during the race day; more specifically, it is performed each time a race is held on the race day. In other words, each time a race is held during the race day, an evaluation value is calculated (updated) for each of the multiple voting support videos. Then, distribution candidate videos are re-selected in accordance with the update of the evaluation value for each video. Regarding the distribution candidate list sent to user γ, a new list may be sent each time a distribution candidate video is selected. Alternatively, the distribution candidate list may be sent only once after user γ operates the 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. For example, distribution candidate videos may be selected not only based on evaluation value but also by taking into account the narrowing down criteria specified by user γ. Such a configuration (hereinafter, the second embodiment) will be described. Note that the following mainly describes the differences between the first embodiment and the second embodiment.

[0090] The video distribution flow according to the second embodiment is generally the same as 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 specifies in advance the filtering conditions for the videos to be distributed, and the specified filtering conditions are acquired in advance.

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

[0092] Then, in the selection process in the video distribution flow according to the second embodiment, distribution candidate videos are selected based on the evaluation value calculated for each voting support video and the filtering criteria 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 results of the first analysis process, an evaluation value based on the analysis results of the second analysis process, and an evaluation value based on the analysis results of the third analysis process are calculated separately. Then, in the selection process, distribution candidate videos are selected based on the evaluation value corresponding to the filtering criteria from among the evaluation values ​​calculated for each type of analysis result. For example, if user γ specifies the popularity of the performers in the video as a filtering criterion, the selection process selects distribution candidate videos based on the evaluation value based on the analysis result of the first analysis process (i.e., the popularity).

[0093] As described above, in the second embodiment, by selecting candidate videos to be distributed taking into consideration the narrowing down conditions specified by user γ, it is possible to determine candidate videos to be distributed in a manner that reflects the preferences or requests of user γ.

[0094] [Regarding the third embodiment] In the second embodiment, candidate videos for distribution are selected by taking into account the narrowing down conditions specified by user γ, so as to reflect the preferences or requests of user γ. On the other hand, if machine learning is performed on the characteristics of voting support videos that user γ has previously watched, and the learning results are used to select candidate videos for distribution, candidate videos for distribution can be determined so as to more accurately reflect user γ's preferences. This embodiment (hereinafter, referred to as the third embodiment) will be described with reference to FIGS. 12 and 13. Note that, hereinafter, differences between the third embodiment and the first embodiment will be mainly described, 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 video with votes, on which the user γ voted while watching, among the voting support videos that the user γ has previously previewed. More specifically, the feature identification unit 39 identifies, by machine learning, the features of a predetermined video among the videos with votes. The predetermined video is a video on which the user γ, while watching the video, has piggybacked on a vote, i.e., cast a vote with the same predicted content as the vote cast by the performer of the predetermined video.

[0096] Video features are the content that characterizes 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 (level of popularity), the types of races in which the performers are betting, and the tendencies of the performers' voting (number of votes and amount of voting). When extracting video features, known annotation tools can be applied. Annotation tools are tools that annotate the target video with tag information (metadata) related to the video.

[0097] The feature identification unit 39 performs machine learning using training data that includes 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 those videos. The machine learning algorithm is not particularly limited, 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. Furthermore, gradient descent or backpropagation may be used as a method for minimizing the objective function (loss function) in neural network machine learning.

[0098] In the third embodiment, as shown in FIG. 13, in the selection process during the video distribution flow, a computer constituting the information processing device 10X executes a feature identification process, and in this process, the features of a predetermined video (i.e., a video for which user γ has previously watched a voting support video and for which he or she has voted on the basis of a piggyback vote 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, but may be performed before the video distribution flow is executed.

[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 day of the race (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 constituting the information processing device 10X (more specifically, the list sending unit 37) sends a distribution candidate list indicating the selected distribution candidate videos to user γ, and at this time, the distribution candidate list is determined according to the characteristics of the videos identified in step S031. Specifically, after selecting a predetermined number of distribution candidate videos in descending order of evaluation value, the computer 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 showing distribution candidate videos that are selected based on the evaluation value and have the characteristics of predetermined videos identified by machine learning is transmitted to user γ. As described above, in the third embodiment, distribution candidate videos can be determined based on the preferences of user γ. More specifically, videos that have the same characteristics as videos 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 on 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 the 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, but the above-described embodiment is merely an example, and other embodiments may be considered.

[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, such as the selection unit 36, may be realized by the user terminal 12. In other words, the user terminal 12 may evaluate each of a plurality of voting support videos based on the popularity of the performers and the number of hits, etc., and select candidate videos to distribute based on the evaluation results.

[0105] Furthermore, in the above embodiment, when evaluating each of the multiple voting support videos, each video was analyzed, specifically, the speech content 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 while watching the video were identified. Then, each voting support video was evaluated based on the identified analysis results (i.e., the results of the analysis performed from three perspectives), and more specifically, an evaluation value was calculated based on each of the analysis results performed from the three perspectives. However, this is not limited to this, and 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 predetermined period, information obtained during that distribution period is used, such as the number of hits by a performer during the distribution period and the number of viewers who voted during the distribution period. In other words, in the above embodiment, even if the voting support video features the same performer, 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 this, and voting support videos may also be evaluated using fixed information that is not affected by the distribution period (e.g., personal information of the performer, etc.).

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

[0108] [summary] The information processing device 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 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 based on a predetermined criterion are selected from among a plurality of videos related to voting, and the selected videos are presented to the user as candidate videos for distribution, thereby enabling effective distribution of videos to the user, so that the user can watch videos that are useful for voting.

[0109] In the information processing device of the present invention, the selection unit may select a candidate video to be distributed from among a plurality of videos, depending on an evaluation result of at least one of the audio and the image of the performers in the video. 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 video 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 speak in the video, and select candidate videos for distribution based on 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 speak. According to the above configuration, the popularity of each video can be appropriately evaluated by calculating an evaluation value for each video based on the number of performers who speak in the video. Then, by selecting videos to be distributed according to the evaluation value, more appropriate videos (i.e., videos with a high popularity) can be presented to the user.

[0111] In the above configuration, the selection unit may identify facial expressions of performers in each of the multiple videos from images of the performers, calculate an evaluation value based on the identified facial expressions of the performers, and select candidate videos for distribution based on 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, by calculating an evaluation value for each video based on the facial expressions of the performers in the video, it is possible to more appropriately evaluate the popularity of the video. Then, by selecting candidate videos for distribution according to the evaluation value, it is possible to present more appropriate videos (i.e., videos with a high popularity) to the user.

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

[0113] Furthermore, in a 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 outcome of the predetermined competition immediately prior to the predetermined period, and select candidate videos for distribution based on the evaluation value of each video. In this case, it is preferable that the selection unit assign a higher evaluation value the larger the numerical value. 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 predicted correctly in the specified competition of 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 a plurality of videos based on the number of consecutive votes that have been correct among multiple votes cast by the performers of the videos within a predetermined period, and select candidate videos for distribution based on the evaluation value of each video. In this case, it is preferable that the selection unit assign a higher evaluation value to videos with a higher 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 have been correctly predicted, and candidate videos for distribution are selected based on the evaluation value, so that more appropriate videos (i.e., videos that are useful for users when voting) can be presented to users.

[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 amount of votes cast by the participants in the video in each vote 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 amount of votes cast by the performers in each vote, and candidate videos for distribution are selected based on the evaluation value, so that more appropriate videos (i.e., videos that are useful for users when voting) can be presented to users.

[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 such as the number of viewers who voted while watching each video, so that more appropriate videos, for example, videos that are useful for the user when voting, 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 the votes 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 for the same predicted content 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 users when voting) can be presented to users.

[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 the multiple 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 in the video are voting and the video is popular. Therefore, by selecting candidate videos for distribution based on the evaluation value of the video during the voting acceptance time period, it is possible to present more appropriate videos to users, i.e., videos that are popular and videos that are useful for users when voting.

[0119] 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 a plurality of videos that are distributed in real time within the specified period, each time the specified competition is held within the specified period, and select candidate videos for distribution based on the evaluation value of each video. As each predetermined competition is held within a predetermined period, the evaluation results of the videos may change accordingly. Taking this into consideration, the above configuration calculates an evaluation value for each video each time a predetermined competition is held, and distribution candidates are selected based on the evaluation value. This allows distribution candidate videos to be selected based on the current evaluation value, making it possible to effectively distribute videos that are currently recommended 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 videos with votes that the user voted on while watching, among the videos that the user has previously viewed, and the list transmission unit may transmit to the user a list showing videos that are candidates for distribution and 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 (videos with votes) are identified, and when videos with votes are presented to the user as candidates for distribution, the characteristics of the videos with votes are reflected. This makes it possible to present videos as 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, while watching the predetermined video, cast a vote with the same predicted content as a vote by a cast member of the predetermined video. According to the above configuration, the characteristics of the voted videos in which the user has voted for the same predicted content as the cast member's vote (i.e., piggyback voting) are identified, and the characteristics are reflected when presenting candidate videos for distribution to the user. This makes it possible to present candidate videos for distribution to the user based on the user's preferences (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 users, characterized in that a computer selects some videos from a plurality of videos as candidate videos for distribution based on specified criteria, and the computer transmits a list showing the selected candidate videos for distribution to the user. By using the above information processing method, videos related to voting can be effectively distributed to users, and users can 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 to predict the results of a specified competition, and is a program that causes a computer to select some videos from a plurality of videos as candidate videos for distribution, and transmits a list showing the selected candidate videos for distribution to the user. By having a computer execute the above program, videos related 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 Unit 32 Vote Acquisition Department 33 Result acquisition part 34 Storage section 35 Update section 36 Selection Department 36A Video Analysis Unit 36B Evaluation value calculation unit 37 List transmission unit 38 Video Distribution Department 39 Feature Analysis Unit S Information Processing System

Claims

1. a processor, the processor comprising: Accepting votes from users on the results of the competition; For each of a plurality of videos related to voting to predict the outcome of the competition, when the user specifies a narrowing down condition corresponding to the first evaluation criterion among a plurality of narrowing down conditions corresponding to each of a plurality of evaluation criteria including at least a first evaluation criterion based on at least one of audio and images in the video, calculate an evaluation value for each of the plurality of videos based on the first evaluation criterion, and display a list showing one or more videos selected based on the calculated evaluation value on the user terminal of the user in a manner showing an image of each video, one video selected by the user from 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, for each of a plurality of videos relating to voting for predicting the outcome of the competition, among a plurality of filtering conditions corresponding to each of a plurality of evaluation criteria including at least a first evaluation criterion based on at least one of audio and images in the video, when a filtering condition corresponding to the first evaluation criterion is designated by the user, calculates an evaluation value for each of the plurality of videos based on the first evaluation criterion, and displays on the user terminal of the user a list showing one or more videos selected based on the calculated evaluation value in a manner showing an image of each video; a processor delivering one video selected by the user from the videos included in the displayed list to the user terminal; Information processing methods.

3. causing the processor to accept votes from users regarding the outcome of the competition; a processor for calculating an evaluation value for each of a plurality of videos related to voting for predicting the outcome of the competition based on the first evaluation criterion when the user specifies a filtering condition corresponding to the first evaluation criterion among a plurality of filtering conditions corresponding to each of a plurality of evaluation criteria including at least a first evaluation criterion based on at least one of audio and images in the videos, and displaying a list showing one or more videos selected based on the calculated evaluation value on the user terminal of the user in a manner showing an image of each video; causing the processor to deliver to the user terminal one video selected by the user from 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 on the results of the competition from users based on input to the user terminal; For each of a plurality of videos related to voting to predict the outcome of the competition, when the user specifies a narrowing down condition corresponding to a first evaluation criterion among a plurality of narrowing down conditions corresponding to each of a plurality of evaluation criteria including at least a first evaluation criterion based on at least one of audio and images in the video, calculate an evaluation value for each of the plurality of videos based on the first evaluation criterion, and display on the user terminal a list showing one or more videos selected based on the calculated evaluation value in a manner showing an image of each video, one video selected by the user from the videos included in the displayed list is delivered to the user terminal; system.

Images