Estimation device and estimation method

Abstract

An estimation device 20 comprises: a reception unit 21 (a first reception unit) that receives, from a plurality of users, information pertaining to the visual field of a user in a metaverse space and information pertaining to the excitement of the user; a model creation unit 22 that performs machine learning using the information pertaining to the visual field of the user as an explanatory variable and the information pertaining to the excitement as an objective variable, and that creates a machine learning model for estimating the information pertaining to the excitement; a reception unit 21 (a second reception unit) that receives information pertaining to the visual field of a target user whose excitement is to be estimated; an estimation unit 23 that estimates information pertaining to the excitement of the target user by inputting the information pertaining to the visual field of the target user into the machine learning model; and an output unit 24 that outputs the estimation result from the estimation unit 23.

Description

Estimation device and estimation method

[0001] One aspect of the present disclosure relates to an estimation device and an estimation method.

[0002] Patent Document 1 describes a method for calculating the degree of excitement of a user using measurement information or the like that indicates information about the user.

[0003] JP 2017-35198 A

[0004] In recent years, the metaverse, a virtual space on the Internet, has become increasingly popular. Events such as live music concerts are now being held in the metaverse, creating a demand for technology to estimate the level of excitement among users in the metaverse.

[0005] One aspect of the present disclosure has been made in consideration of the above-described circumstances, and provides an estimation device and an estimation method that can appropriately estimate the excitement of users in a metaverse space.

[0006] An estimation device according to one aspect of the present disclosure includes a first reception unit that receives, from a plurality of users, information regarding the user's field of view in a metaverse space and information regarding the user's excitement; a model creation unit that performs machine learning using the information regarding the user's field of view as an explanatory variable and the information regarding the excitement as a target variable to create a machine learning model that estimates information regarding the excitement; a second reception unit that receives information regarding the field of view of a target user for whom excitement is to be estimated; an estimation unit that estimates information regarding the target user's excitement by inputting information regarding the target user's field of view into the machine learning model; and an output unit that outputs the estimation result by the estimation unit.

[0007] In an estimation device according to an aspect of the present disclosure, information about a user's field of view in the metaverse space is used as an explanatory variable, and information about excitement is used as a target variable. Information about a plurality of users is learned, thereby creating a machine learning model that estimates information about excitement. Information about a target user's field of view is then input into the machine learning model, whereby information about the target user's excitement is estimated and an estimation result is output. With this configuration, information about excitement corresponding to information about the target user's field of view can be estimated with high accuracy using a machine learning model created by learning information about a user's field of view and information about excitement in the metaverse space in association with each other. As described above, the estimation device according to an aspect of the present disclosure can appropriately estimate a user's excitement in the metaverse space.

[0008] According to one aspect of the present disclosure, it is possible to appropriately estimate the excitement of users in a metaverse space.

[0009] FIG. 1 is a diagram illustrating an overview of an estimation device according to this embodiment. FIG. 2 is a diagram illustrating a functional configuration of the estimation device according to this embodiment. FIG. 3 is a diagram illustrating creation of a machine learning model. FIG. 4 is a diagram illustrating another example of acquisition of information related to a user's visual field. FIG. 5 is a diagram illustrating creation of a machine learning model. FIG. 6 is a diagram illustrating estimation of a position with a high degree of excitement. FIG. 7 is a flowchart illustrating processing executed by the estimation device. FIG. 8 is a diagram illustrating an example of a hardware configuration of the estimation device.

[0010] Hereinafter, the embodiments will be described in detail with reference to the drawings. In the description, the same elements or elements having the same functions are denoted by the same reference numerals, and redundant description will be omitted.

[0011] FIG. 1 is a diagram illustrating an overview of an estimation device according to this embodiment. The estimation device is a device that estimates information related to user excitement in a metaverse space. The metaverse space is a virtual space on the Internet. In the metaverse, for example, users can move freely through a three-dimensional virtual space via an avatar, which is their own alter ego, and enjoy events such as live concerts and interactions with other companies.

[0012] As shown in FIG. 1A, in this embodiment, a live event is being held in the metaverse space, and each user is viewing the live performance on a stage (here, stage A or stage B). In the example shown in FIG. 1A, a user designated "Target 1" is viewing the direction of stage A, and a user designated "Target 2" is viewing the direction of another user next to stage B. In this case, as shown in FIG. 2B, the field of view of "Target 1" includes stage A and the back of the head of the other user. Also, as shown in FIG. 2C, the field of view of "Target 2" includes the face of another user next to stage B.

[0013] The estimation device according to this embodiment estimates information about the target user's emotional excitement based on information about the target user's field of view. To perform this estimation, the estimation device stores a machine learning model (a machine learning model that estimates information about excitement) that has been trained by associating information about the user's field of view with information about excitement. The estimation device estimates information about the target user's excitement by inputting information about the target user's field of view into the machine learning model. Furthermore, the estimation device estimates positions with high levels of excitement by associating information about excitement with the target user's location, and estimates scenes with high levels of excitement by associating information about excitement with information indicating scenes in a live performance. By outputting such estimation results, information about users' excitement in the metaverse space can be estimated with high accuracy, and this information can be used to improve and refine events in the metaverse space.

[0014] Fig. 2 is a diagram showing the functional configuration of an estimation device 20 according to this embodiment. Fig. 2 shows a metaverse space excitement estimation system including the estimation device 20 and a plurality of terminals 10. In the metaverse space excitement estimation system, the plurality of terminals 10 and the estimation device 20 are configured to be able to communicate with each other via a network including a wireless communication network and a fixed communication network.

[0015] Terminal 10 is a terminal used by a user participating in an event (here, a live event) in the metaverse space. Terminal 10 may be, for example, a personal computer, a smartphone, a tablet terminal, a feature phone, a server device, a game console, or the like. While FIG. 2 illustrates only one terminal 10, the metaverse space excitement estimation system actually includes multiple terminals 10. When a user participates in an event in the metaverse space, each terminal 10 transmits information about the user's field of view in the metaverse space (details will be described later) to the estimation device 20. Each terminal 10 also transmits information about the user's excitement in the metaverse space (details will be described later) to the estimation device 20. The timing for transmitting various pieces of information from each terminal 10 may be when the user's participation in the event ends, during the user's participation in the event (in real time), or at predetermined time intervals (periodic timing).

[0016] The estimation device 20 includes, as functional components, a reception unit 21 (first reception unit, second reception unit), a model creation unit 22, an estimation unit 223, an output unit 24, and a storage unit 25. Below, we will explain in order the function of the "learning process" related to the creation of a machine learning model and the function of the "estimation process" related to the estimation of information related to excitement using the created machine learning model.

[0017] (Learning process) The reception unit 21 receives information about the user's field of view in the metaverse space and information about the user's excitement from multiple users via each terminal 10. The information about the user's field of view and the information about the excitement are not necessarily received at the same time, but are associated with each other. The reception unit 21 stores the information received from each terminal 10 in the memory unit 25.

[0018] The reception unit 21 may receive information within the user's field of view as information regarding the user's field of view. The reception unit 21 may directly acquire information within the user's field of view, or may acquire information in a pseudo manner, for example, by giving the user an item in the metaverse and having the user wear it. In other words, the reception unit 21 may accept information indicating the field of view from an accessory worn by the user as information within the user's field of view.

[0019] The reception unit 21 may receive, as the information within the user's field of view, information regarding the stage of an event within the user's field of view. Specifically, as shown in Fig. 3, the reception unit 21 may receive, as the information regarding the stage of an event within the user's field of view, the time during which the stage is within the field of view, the time during which a stage performer is within the field of view, the maximum occupancy rate of the stage within the field of view, the maximum occupancy rate of a stage performer within the field of view, etc.

[0020] The time a stage is within the user's field of view is the time the stage is within the user's field of view within a specified period (e.g., the time a live performance is being held on the stage).The time a stage performer is within the user's field of view is the time the stage performer is within the user's field of view within a specified period (e.g., the time a live performance is being held on the stage).The maximum stage occupancy rate within the user's field of view is the occupancy rate at which the stage occupies the highest percentage of the field of view when the stage is within the user's field of view.The maximum stage performer occupancy rate within the user's field of view is the occupancy rate at which the stage performer occupies the highest percentage of the field of view when the stage performer is within the user's field of view.

[0021] The receiving unit 21 may receive information about other users in the user's field of view as the information about the user's field of view. Specifically, as shown in FIG. 3 , the receiving unit 21 may receive information about the maximum occupancy rate of other users in the user's field of view and the number of eye contacts with other users.

[0022] The maximum occupancy rate of other users in a user's field of view is the highest occupancy rate of other users in the field of view when other users are included in the user's field of view. The number of eye contacts with other users is the number of times eye contact was made with other users within a specified period (e.g., during the time a live performance was being held on stage).

[0023] FIG. 4 is a diagram illustrating another example of information about the user's field of view. As shown in FIG. 4( a), the receiving unit 21 may receive, as information about the user's field of view, information about the user viewed from the side of an object viewed by the user. The object viewed by the user may be, for example, equipment on the live performance organizer's side, such as performers on stage, a stage, or an object for live viewing. In this case, the "information about the user viewed from the side of the object viewed by the user" is information that allows at least information about the user's field of view (such as where the user is viewing) to be estimated. For example, if the information about the user viewed from the object side shows that the user is facing the stage, it can be estimated that the user is viewing the stage. As shown in FIG. 4( b), for example, from the viewpoint of stage A, if a user is looking toward stage A, it can be estimated that the user is viewing the direction of stage A.

[0024] The reception unit 21 may receive information on the user viewed from the side of the object (here, as an example, a stage or an object for live viewing) that the user is viewing, such as the time spent gazing at the stage, the number of reactions toward the stage, the maximum occupancy rate of the stage within the field of view, the maximum occupancy rate of the stage performers within the field of view, the time spent gazing at the stage performers, and the time spent gazing at the live viewing screen for the stage, as shown in Figure 5.

[0025] The receiving unit 21 may receive, as the information related to excitement, at least one of voice, information related to the amount of operation, a biological reaction, and a questionnaire result related to excitement. The information related to excitement may be any information that quantitatively or qualitatively indicates whether the user is excited or not.

[0026] Audio refers to information about the audio during a live event, and may be, for example, volume, speaking time, number of words spoken, emotional analysis results of the speech content (the more happy words there are, the more excited the audience is), emotional analysis results of the spoken audio (the more speech that includes the emotion of joy, the more excited the audience is), etc.

[0027] The information related to the amount of operation is information indicating the amount of operation during a live event, and may be, for example, the amount of key input, the amount of movement in space, the amount of movement in the field of view, etc. The biological reaction is information indicating the biological reaction during a live event, and may be, for example, the user's heart rate, the number of blinks (a decrease indicates excitement = excitement), the amount of sweat, etc.

[0028] The survey results regarding the excitement are information indicating the direct evaluation results of users regarding the live event, and may be information entered by users after the live event, for example.

[0029] The various types of information regarding excitement described above may be information along the timeline of the live event (information indicating the excitement at each point in time) or information indicating the average excitement during the live event, but in either case it is associated with information regarding the user's field of view.

[0030] The model creation unit 22 creates a machine learning model that estimates information related to excitement by performing machine learning using the information received from each terminal 10 and stored in the storage unit 25. The model creation unit 22 stores the created machine learning model in the storage unit 25.

[0031] 3 and 5 are diagrams illustrating the creation of a machine learning model. As an example, FIG. 3 shows information about the visual fields of two users ("Target 1" and "Target 2") (more specifically, information within the users' visual fields) and information about excitement. The excitement information is shown here as a normalized score ranging from 0 to 100. Information about the user's visual field includes the time the stage was within the visual field, the time the stage performers were within the visual field, the maximum occupancy rate of the stage within the visual field, the maximum occupancy rate of the stage performers within the visual field, the maximum occupancy rate of other users, and the number of eye contacts with other users. These pieces of information may be shown as actual numerical values ​​or as normalized scores.

[0032] FIG. 5 shows, as an example, information about the field of view of two users ("Target 1" and "Target 2") (more specifically, information about the user viewed from the side of the object viewed by the user) and information about excitement. Here, the information about excitement is shown as a score normalized to a value between 0 and 100. Furthermore, as information about the user viewed from the side of the object viewed by the user, the following information is shown: the time spent gazing at the stage, the number of reactions toward the stage, the maximum occupancy rate of the stage within the field of view, the maximum occupancy rate of the stage performers within the field of view, the time spent gazing at the stage performers, and the time spent gazing at the stage live viewing screen. These pieces of information may be shown as actual numerical values ​​or as normalized scores.

[0033] The model creation unit 22 performs machine learning using information about the user's field of view as an explanatory variable and information about excitement as a target variable to create a machine learning model that estimates information about excitement. For example, as shown in FIG. 3, the time the stage is in the field of view, the time the stage performers are in the field of view, the maximum occupancy rate of the stage in the field of view, the maximum occupancy rate of the stage performers in the field of view, the maximum occupancy rate of other users, and the number of eye contacts with other users are used as explanatory variables, and a score indicating the degree of excitement is used as the target variable. This process is performed for each user ("target 1," "target 2," ...) to perform machine learning, thereby creating a machine learning model that estimates information about excitement. The machine learning may be performed using an algorithm such as a random forest or a recurrent neural network.

[0034] The above-mentioned machine learning model may be created for each attribute information such as age, country, gender, and place of origin, or for each event content (music event, art event, social event). This allows for the creation of machine learning models specialized for attributes and event content.

[0035] (Estimation Process) The reception unit 21 receives information about the field of view of a target user whose excitement level is to be estimated from the target user's terminal 10. The information about the field of view here may be information about the user's field of view (such as the time the stage is in the field of view, the time the stage performers are in the field of view, the maximum occupancy rate of the stage in the field of view, the maximum occupancy rate of the stage performers in the field of view, the maximum occupancy rate of other users, and the number of eye contacts with other users) as shown in Fig. 3, or information about the user viewed from the side of an object viewed by the user (such as the time spent gazing at the stage, the number of reactions toward the stage, the maximum occupancy rate of the stage in the field of view, the maximum occupancy rate of the stage performers in the field of view, the time spent gazing at the stage performers, and the time spent gazing at the stage live viewing screen) as shown in Fig. 5.

[0036] The receiving unit 21 may receive information indicating the position of the target user together with information regarding the field of view of the target user from the target user's terminal 10. The information indicating the position of the target user may be, for example, information measured by the terminal 10.

[0037] The reception unit 21 may receive information indicating a scene in the user's field of view along with information regarding the field of view of the target user. The information indicating a scene is, for example, information indicating which scene in a live event it is, such as information indicating which song it is playing. The reception unit 21 may receive the information indicating the scene from the terminal 10, or may receive it from another server that stores information related to the event. The reception unit 21 stores the received information in the storage unit 25.

[0038] The estimation unit 23 estimates information about the target user's excitement by inputting information about the target user's field of view into the machine learning model. The estimation unit 23 acquires the machine learning model and information about the target user's field of view from the storage unit 25.

[0039] The estimation unit 23 may further estimate positions of high excitement levels based on the estimated information on the excitement levels of the target users and the positions of the target users received by the reception unit 21. Fig. 6 is a diagram illustrating the estimation of positions of high excitement levels. As shown in Fig. 6, for example, when information on the excitement levels of multiple target users is estimated, it is assumed that the excitement levels of the target users in the first row at the front of stage A and the target users in the second row at the front of stage A are high. In this case, the estimation unit 23 may estimate the positions of these target users to be positions of high excitement levels.

[0040] The estimation unit 23 may further estimate highly exciting scenes based on the estimated information on the excitement of the target user and the information indicating the scenes received by the reception unit 21. By estimating highly exciting scenes in this manner, it becomes possible to create a digest video using the highly exciting scenes. The estimation unit 23 stores various estimation results in the storage unit 25.

[0041] The output unit 24 acquires the estimation result by the estimation unit 23 from the storage unit 25 and outputs it. That is, the output unit 24 outputs information related to the excitement of the target user. The output unit 24 may also output information related to positions with high excitement levels or scenes with high excitement levels.

[0042] Next, the process executed by the estimation device 20 will be described with reference to Fig. 7. Fig. 7 is a flowchart showing the process executed by the estimation device 20.

[0043] As shown in FIG. 7, first, the estimation device 20 receives viewing information (information about the user's field of view in the metaverse space and information about the user's excitement) from each terminal 10 (step S1).

[0044] Next, the estimation device 20 performs machine learning on multiple pieces of viewing information, using information about the user's field of view as an explanatory variable and information about excitement as a target variable, and creates a machine learning model that predicts information about excitement (step S2).

[0045] Next, in the estimation device 20, information about the field of view of the target user is input to the machine learning model, and information about the excitement of the target user is estimated (step S3).

[0046] Finally, the estimation device 20 outputs information according to the estimation result regarding the excitement (step S4).

[0047] Next, the effects of the estimation device 20 according to this embodiment will be described.

[0048] The estimation device 20 includes a reception unit 21 (first reception unit) that receives information about users' field of view in the metaverse space and information about users' excitement from multiple users, a model creation unit 22 that performs machine learning using information about the users' field of view as an explanatory variable and information about excitement as a target variable to create a machine learning model that estimates information about excitement, a reception unit 21 (second reception unit) that receives information about the field of view of a target user whose excitement is to be estimated, an estimation unit 23 that estimates information about the target user's excitement by inputting information about the target user's field of view into the machine learning model, and an output unit 24 that outputs the estimation result by the estimation unit 23.

[0049] In the estimation device 20 according to the present embodiment, information about a user's field of view in the metaverse space is used as an explanatory variable, and information about excitement is used as a target variable. Information about a plurality of users is learned, thereby creating a machine learning model that estimates information about excitement. Information about a target user's field of view is then input into the machine learning model, whereby information about the target user's excitement is estimated and an estimation result is output. With this configuration, information about excitement corresponding to information about the target user's field of view can be estimated with high accuracy using a machine learning model created by learning information about a user's field of view and information about excitement in the metaverse space in association with each other. As described above, the estimation device 20 according to the present embodiment can appropriately estimate the excitement of users in the metaverse space.

[0050] As events such as live music concerts are increasingly held in the metaverse, there has been a demand for a method to estimate how excited users are. Unlike existing video viewing, the metaverse space is different in that each user has a different perspective, making it difficult to determine whether a user is excited or not, even if they are in the same space and the same location. In this regard, by creating an inference device (machine learning model) that can infer a user's level of excitement based on information within the user's field of view in the metaverse space, it is possible to appropriately estimate a user's level of excitement in the metaverse space. For businesses hosting events in the metaverse space, being able to appropriately obtain information about users' levels of excitement allows them to easily and appropriately adjust the content and structure of the event.

[0051] The receiving unit 21 may receive information about the user's field of view as the information about the user's field of view, thereby making it possible to directly and appropriately acquire information about the user's field of view.

[0052] The reception unit 21 may receive information indicating the field of view from an accessory worn by the user as information within the user's field of view. This allows information within the user's field of view to be pseudo-obtained by having the user wear an item in the metaverse, even if the information within the user's field of view cannot be directly obtained.

[0053] The receiving unit 21 may receive information about the stage of an event within the user's field of view as information about the user's field of view. This allows, when a live event or the like has started, information about the user's field of view for the live event to be appropriately associated with the user's level of excitement and learned.

[0054] The receiving unit 21 may receive information about other users in the user's field of view as the information within the user's field of view. This allows the information about interactions with others to be appropriately associated with the user's level of excitement and learned.

[0055] The receiving unit 21 may receive information about the user's field of view from the side of an object that the user is viewing, as information about the user's field of view. This makes it possible to efficiently obtain information about the field of view of multiple users, for example.

[0056] The receiving unit 21 may receive, as the information related to excitement, at least one of voice, information related to the amount of operation, biological reaction, and the results of a questionnaire related to excitement. This makes it possible to obtain with high accuracy the degree of excitement of users at a live event or the like.

[0057] The receiving unit 21 receives information about the target user's field of view as well as information about the target user's location, and the estimation unit 23 may further estimate locations with high levels of excitement based on the information about the target user's excitement and the target user's location. This makes it possible to visually confirm locations with high levels of excitement (in some cases, chronologically), which can be used to improve and refine Metaverse events, etc.

[0058] The receiving unit 21 receives information about the target user's field of view as well as information indicating a scene in the user's field of view, and the estimation unit 23 may further estimate a scene with a high level of excitement based on the information about the target user's excitement and the information indicating the scene. This makes it possible to obtain scenes with a high level of excitement and create a digest video of a live event, for example, and use the information to improve and refine Metaverse events, etc.

[0059] The estimation device and estimation method of the present disclosure have the following configuration.

[0060] [1] An estimation device comprising: a first reception unit that receives, from a plurality of users, information about the user's field of view in a metaverse space and information about the user's excitement; a model creation unit that performs machine learning using the information about the user's field of view as an explanatory variable and the information about the excitement as a target variable to create a machine learning model that estimates information about the excitement; a second reception unit that receives information about the field of view of a target user whose excitement is to be estimated; an estimation unit that estimates information about the target user's excitement by inputting the information about the target user's field of view into the machine learning model; and an output unit that outputs an estimation result by the estimation unit.

[0061] [2] The estimation device according to [1], wherein the first reception unit receives information within the user's visual field as information regarding the user's visual field.

[0062] [3] The estimation device according to [2], wherein the first reception unit receives information indicating a field of view from an accessory worn by the user, by regarding the information as information within the field of view of the user.

[0063] [4] The estimation device according to [2] or [3], wherein the first reception unit receives, as the information within the user's field of view, information about a stage of an event within the user's field of view.

[0064] [5] The estimation device according to any one of [2] to [4], wherein the first reception unit receives information about another user within the user's field of view as the information within the user's field of view.

[0065] [6] The estimation device according to any one of [1] to [5], wherein the first reception unit receives, as information about the user's field of view, information about the user viewed from the side of an object visually recognized by the user.

[0066] [7] The estimation device according to any one of [1] to [6], wherein the first reception unit receives at least one of voice, information related to the amount of operation, a biological reaction, and a questionnaire result related to excitement as the information related to excitement.

[0067] [8] The estimation device according to any one of [1] to [7], wherein the second reception unit receives information indicating the position of the target user together with information regarding the field of view of the target user, and the estimation unit further estimates a position with a high level of excitement based on information regarding the excitement of the target user and the position of the target user.

[0068] [9] The estimation device according to any one of [1] to [8], wherein the second reception unit receives information indicating a scene in the user's field of view together with information regarding the target user's field of view, and the estimation unit further estimates a scene with a high level of excitement based on the information regarding the target user's excitement and the information indicating the scene.

[0069]

[10] An estimation method performed by an estimation device, the estimation method including: receiving, from a plurality of users, information about the user's field of view in a metaverse space and information about the user's excitement; performing machine learning using the information about the user's field of view as an explanatory variable and the information about the excitement as a target variable to create a machine learning model that estimates information about the excitement; receiving information about the field of view of a target user whose excitement is to be estimated; inputting the information about the target user's field of view into the machine learning model to estimate information about the target user's excitement; and outputting an estimation result.

[0070] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (e.g., via wire, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0071] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0072] For example, an estimation device 20 constituting a metaverse space excitement estimation system according to an embodiment of the present disclosure may function as a computer that performs processing of the control method of the present disclosure. FIG. 7 is a diagram illustrating an example of the hardware configuration of the estimation device 20 according to this embodiment. The estimation device 20 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like. Note that the estimation device 20 may be configured as a computer device including at least one processor such as a CPU or GPU, or may be configured as a computer device including multiple processors or may be configured to include multiple computer devices. The terminal 10 may also have a similar hardware configuration.

[0073] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the estimation apparatus 20 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0074] Each function in the estimation device 20 is realized by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0075] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned reception unit 21, model creation unit 22, estimation unit 23, output unit 24, etc. may be realized by the processor 1001.

[0076] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these programs. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the reception unit 21, the model creation unit 22, the estimation unit 23, and the output unit 24 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be used for other functional blocks. While the above-described various processes have been described as being executed by a single processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0077] The memory 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a control method according to an embodiment of the present disclosure.

[0078] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0079] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned reception unit 21, output unit 24, etc. may be realized by the communication device 1004.

[0080] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0081] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0082] The estimation device 20 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0083] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0084] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0085] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0086] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0087] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0088] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0089] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0090] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0091] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0092] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0093] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or other corresponding information. For example, a radio resource may be indicated by an index.

[0094] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0095] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," and the like may be used interchangeably.

[0096] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0097] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0098] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0099] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0100] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0101] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0102] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0103] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0104] 20: Estimation device, 21: Reception unit, 22: Model creation unit, 23: Estimation unit, 24: Output unit.

Claims

1. An estimation device comprising: a first reception unit that receives information about users' field of view in a metaverse space and information about users' excitement from multiple users; a model creation unit that performs machine learning using the information about the users' field of view as an explanatory variable and the information about the excitement as a target variable to create a machine learning model that estimates information about excitement; a second reception unit that receives information about the field of view of a target user whose excitement is to be estimated; an estimation unit that estimates information about the target user's excitement by inputting information about the target user's field of view into the machine learning model; and an output unit that outputs the estimation result by the estimation unit.

2. The estimation device according to claim 1, wherein the first receiving unit receives information about the user's field of view as information about the user's field of view.

3. The estimation device according to claim 2, wherein the first receiving unit receives information indicating a field of view from an accessory worn by the user, by regarding the information as information within the field of view of the user.

4. The estimation device according to claim 2, wherein the first receiving unit receives information about a stage of an event within the user's field of view as the information within the user's field of view.

5. The estimation device according to claim 2, wherein the first receiving unit receives information about other users within the user's field of view as the information within the user's field of view.

6. The estimation device according to claim 1, wherein the first receiving unit receives, as information about the user's field of view, information about the user viewed from the side of an object that the user is viewing.

7. The estimation device according to claim 1, wherein the first receiving unit receives at least one of voice, information on the amount of operation, biological reaction, and questionnaire results on excitement as the information on excitement.

8. The estimation device of claim 1, wherein the second reception unit receives information indicating the position of the target user along with information regarding the field of view of the target user, and the estimation unit further estimates a position with a high level of excitement based on information regarding the excitement of the target user and the position of the target user.

9. The estimation device of claim 1, wherein the second reception unit receives information indicating a scene within the target user's field of view together with information regarding the target user's field of view, and the estimation unit further estimates a scene with a high level of excitement based on the information regarding the target user's excitement and the information indicating the scene.

10. An estimation method performed by an estimation device, comprising: receiving, from a plurality of users, information regarding the user's field of view in a metaverse space and information regarding the user's excitement; performing machine learning using the information regarding the user's field of view as an explanatory variable and the information regarding the excitement as a target variable to create a machine learning model that estimates information regarding excitement; receiving information regarding the field of view of a target user whose excitement is to be estimated; inputting the information regarding the target user's field of view into the machine learning model to estimate information regarding the target user's excitement; and outputting the estimation result.