Program and system

Abstract

To provide a program capable of disclosing a unique profile created in accordance with an object, in association with the object without increasing a user's workload.SOLUTION: A program causes a computer to function as: object generation means for generating an object according to a plurality of setting values selected by a user for setting visual features of the object to be disposed in an online space; profile generation means for generating a profile of the object by inputting a plurality of setting values to a pre-trained learned model; and disclosing means for disclosing the profile generated by the profile generation means in the online space in association with the object generated by the object generation means.SELECTED DRAWING: Figure 13

Description

[Technical field]

[0001] The present invention relates to a program and a system. [Background technology]

[0002] Conventionally, there has been known a technique for opening the profile of one's own avatar to other avatars in a VR (Virtual Reality) space (see, for example, Patent Documents 1 and 2). In the VR space, a user can view the profile of another avatar and decide whether or not to become friends with that avatar (so-called friend registration).

[0003] Similarly, in cooperative online games in which multiple avatars play together, or in competitive online games in which multiple avatars play against each other, players can view the profiles of other avatars and select an avatar to cooperate or compete with. Thus, the importance of each avatar's profile is increasing in VR spaces and online games. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-170013 A [Patent Document 2] JP 2018-106499 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, many users find the task of creating a profile burdensome, and as a result, if there are many avatars that use predefined default profiles, the profile becomes useless when selecting an avatar to register as a friend or cooperate with or compete against.

[0006] The present invention has been made in consideration of the above circumstances, and its object is to provide a program that can publish a unique profile created to match an object in association with the object, without increasing the workload of the user. [Means for solving the problem]

[0007] In order to solve the above problem, the program of the present invention causes a computer to function as an object generation means that generates an object in accordance with a plurality of setting values ​​selected by a user to set the visual characteristics of the object to be placed in an online space, a profile generation means that inputs the plurality of setting values ​​into a trained model that has been trained in advance to generate a profile of the object, and a publishing means that associates the profile generated by the profile generation means with the object generated by the object generation means and publishes it in the online space. Effect of the Invention

[0008] According to the present invention, a unique profile created to match an object can be made public in association with the object without increasing the workload of the user. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram showing an overview of a system according to an embodiment of the present invention; [Diagram 2] This is a conceptual diagram of the AI ​​learning process (A) and generation process (B). [Diagram 3] FIG. 1 is a diagram showing a neural network that realizes AI. [Figure 4] FIG. 2 is a hardware configuration diagram of a user terminal. [Diagram 5] FIG. 2 is a functional block diagram of a user terminal. [Figure 6] 13 is an example of an avatar generation screen. [Figure 7]1 shows an example of the priority (A) of setting parameters and setting value histograms (B) and (C). [Figure 8] 1 shows an example of profiles (A), (B) and learning data (C). [Figure 9] 13 is another example of the avatar generation screen. [Figure 10] 13 shows another example of the priority (A) of setting parameters and the setting value histograms (B) and (C). [Figure 11] 13 is another example of profiles (A), (B) and learning data (C). [Figure 12] 1 shows an example of a profile editing screen (A) and learning data (B). [Figure 13] 13 shows examples of a user selection screen (A) and a user details screen (B). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The system 1 according to the embodiment will be described below with reference to the drawings. Note that the embodiment of the present invention described below is an example of how the present invention is embodied, and the scope of the present invention is not limited to the scope of the embodiment. Therefore, the present invention can be implemented by making various changes to the embodiment.

[0011] [System 1 Overview] FIG. 1 is a diagram showing an overview of a system 1 according to this embodiment. As shown in FIG. 1, the system 1 mainly includes a game server 10, an AI server 15, and a plurality of user terminals 20. Although three user terminals 20 are shown in FIG. 1, the number of user terminals 20 included in the system 1 is not limited to this. The game server 10, the AI ​​server 15, and the user terminals 20 are connected to each other so as to be able to communicate with each other via a communication network 2. A specific example of the communication network 2 is not particularly limited, and may be, for example, the Internet, a mobile communication system (e.g., 4G, 5G, etc.), a wireless network such as Wi-Fi (registered trademark), or a combination of these.

[0012] As one example, the system 1 realizes a cooperative online game in which users (players) of multiple user terminals 20A, 20B, and 20C cooperate to play. As another example, the system 1 realizes a competitive online game in which users (players) of multiple user terminals 20A, 20B, and 20C compete against each other. As yet another example, the system 1 realizes a virtual space in which users of multiple user terminals 20A, 20B, and 20C communicate with each other via avatars. These systems 1 are already well known, so detailed explanations will be omitted.

[0013] [Game Server 10 Configuration] The game server 10 is realized by, for example, a general-purpose computer such as a workstation or a personal computer. As one example, the game server 10 providing an online game synchronizes game data of each of the multiple user terminals 20. As another example, the game server 10 providing a virtual space synchronizes virtual space data of each of the multiple user terminals 20. Hereinafter, the online game and virtual space in which multiple users can participate are collectively referred to as "online space." That is, the game server 10 provides the online space to multiple user terminals 20A, 20B, and 20C.

[0014] [AI Server 15 Configuration] The AI ​​server 15 is realized by, for example, a general-purpose computer such as a workstation or a personal computer. The AI ​​server 15 realizes AI (Artificial Intelligence) 16 including a trained model. As an example, the AI ​​server 15 may be equipped with a dedicated AI optimized for the online space provided by the game server 10. In this case, the game server 10 and the AI ​​server 15 may be realized by a single piece of hardware. As another example, the AI ​​server 15 may be equipped with a general-purpose AI that can be used for various purposes. The configuration of the AI ​​16 is already well known, so a detailed description will be omitted, but for example, it has the following configuration.

[0015] FIG. 2 is a conceptual diagram of the learning process (A) and the generation process (B) by the AI ​​16. FIG. 3 is a diagram showing a neural network 17 that realizes the AI ​​16. The AI ​​16 mounted on the AI ​​server 15 is a so-called "generation AI" that processes input data and generates output data. The AI ​​16 executes the learning process shown in FIG. 2(A) and the generation process shown in FIG. 2(B). The AI ​​16 also generates output data from the input data using, for example, a neural network 17 shown in FIG. 3.

[0016] As shown in FIG. 2(A), the AI ​​16 includes a learning model 16a. The learning model 16a refers to the neural network 17 before the learning process is performed. The learning model 16a becomes a trained model 16b by learning data including input data and correct answer data. The input data refers to data input to the AI ​​16. The correct answer data refers to data that should be output when the input data is input. By inputting a plurality of learning data to the learning model 16a, the neural network 17 is optimized as described below, and becomes the trained model 16b.

[0017] As shown in FIG. 2(B), the trained model 16b generates and outputs output data by inputting input data to the neural network 17. The input data and output data may be in any format, such as text, image, or audio. The input data and output data may also be in different formats. On the other hand, the format of the correct answer data is the same as the format of the output data.

[0018] 2A may be executed not only for the learning model 16a but also for the trained model 16b. Also, the AI ​​16 does not need to learn using the input data and correct answer data actually used in the present invention, and may learn using general-purpose learning data. Furthermore, the learning process executed by the AI ​​16 is not limited to "supervised learning" in which input data and correct answer data are input, but may be "unsupervised learning" in which correct answer data is not input, or may execute reinforcement learning, transfer learning, or the like.

[0019] As shown in FIG. 3, the neural network 17 of the learning model 16a or the trained model 16b is composed of an input layer L1 composed of a plurality of nodes I1, I2, and I3, an intermediate layer L2 composed of a plurality of nodes H1, H2, H3, H4, and H5, and an output layer L3 composed of a plurality of nodes O1, O2, and O3. In the example of FIG. 3, the number of nodes in the input layer L1 and the output layer L3 is the same, but the number of nodes in the input layer L1 and the output layer L3 may be different. The neural network 17 may also have a plurality of intermediate layers L2. Furthermore, FIG. 3 shows a fully connected neural network 17 in which a plurality of nodes constituting each layer L1, L2, and L3 are connected to all nodes in the adjacent layer, but the structure of the neural network 17 is not limited to this.

[0020] The learning process is a process of adjusting the weighting coefficients and biases of each node so that correct data is output from the output layer L3 when input data is input to the input layer L1. The generation process is a process of generating output data by processing the input data input to the input layer L1 using weighting coefficients and biases previously adjusted for each node, and outputting the output data from the output layer L3.

[0021] However, the specific example of the processing by the AI ​​16 described above is merely an example, and the AI ​​server 15 can employ various well-known methods. As another example, the AI ​​16 may have a network structure such as a Convolutional Neural Network (CNN). As yet another example, the network structure may have a configuration such as a Large Language Model (LLM), a Recurrent Neural Network (RNN), or a Long Short-Term Memory (LSTM). In other words, the AI ​​16 may have a network structure other than deep learning.

[0022] In this embodiment, an example will be described in which input data in text format is input to the trained model 16b, and output data in text format is generated. More specifically, the input data according to this embodiment is a plurality of setting values ​​selected by a user to set visual or auditory (at least visual) characteristics of an avatar (object). Also, the output data according to this embodiment is a profile of the avatar (object) generated according to the plurality of setting values.

[0023] [Configuration of user terminal 20] The user terminal 20 is realized, for example, as an HMD set, a tablet terminal, a smartphone, a feature phone, a laptop computer, a desktop computer, etc. In this embodiment, an example of the user terminal 20 as a tablet terminal will be described as shown in FIG.

[0024] Fig. 4 is a hardware configuration diagram of the user terminal 20. As shown in Fig. 4, the user terminal 20 mainly includes a processor 21, a memory 22, a storage 23, a communication interface 25, a monitor 31, cameras 33 and 34, a microphone 35, a speaker 36, a motion sensor 41, and an operation device 42 (operation unit). Each component of the user terminal 20 is connected to a communication bus 29.

[0025] The processor 21 realizes the processes described below by executing a series of instructions included in a terminal program 23P stored in the memory 22 or the storage 23. The processor 21 is realized as, for example, a central processing unit (CPU), a graphics processing unit (GPU), a micro processing unit (MPU), a field-programmable gate array (FPGA), or other devices.

[0026] The memory 22 temporarily holds the terminal program 23P and data. The terminal program 23P is loaded, for example, from the storage 23. The data includes data input to the user terminal 20 and data generated by the processor 21. For example, the memory 22 is realized as a RAM (Random Access Memory) or other volatile memory.

[0027] The storage 23 permanently holds the terminal program 23P and data. The storage 23 is realized, for example, as a ROM (Read-Only Memory), a hard disk drive, a flash memory, or other non-volatile storage device. The storage 23 may also be realized as a removable storage device such as a memory card. As yet another example, the storage 23 may be connected to the user terminal 20 as an external storage device instead of being built into the user terminal 20. With this configuration, for example, in a scene where a plurality of user terminals 20 are used, such as an amusement facility, it becomes possible to collectively update the terminal program 23P and data.

[0028] The communication interface 25 communicates with other devices (e.g., the game server 10, the AI ​​server 15) connected to the communication network 2. The communication interface 25 is realized as, for example, a wired communication interface such as a Local Area Network (LAN), or a wireless communication interface such as Wi-Fi (Wireless Fidelity), Bluetooth (registered trademark), or NFC (Near Field Communication).

[0029] The monitor 31 is provided on the surface of a flat housing, as shown in Fig. 1 for example. The monitor 31 is a display device (display unit) that displays images or videos. The camera 33 is a so-called in-camera that is attached to the surface of the flat housing and captures an image of the face of a user viewing the monitor 31. The camera 34 is a so-called out-camera that is attached to the back surface of the flat housing (the surface opposite to the monitor 31) and captures an image of the surroundings.

[0030] The microphone 35 converts the user's speech into an audio signal (electrical signal) and outputs it to the computer 26. The speaker 36 converts the audio signal output from the computer 26 into voice and outputs it to the user. Note that the user terminal 20 may include an earphone instead of the speaker 36.

[0031] The motion sensor 41 detects the motion of the housing (for example, rotation around three mutually orthogonal axes). The motion sensor 41 may be realized by, for example, an angular velocity sensor, a geomagnetic sensor, or an acceleration sensor.

[0032] The operation device 42 accepts input (operation) of commands by the user to the user terminal 20. The operation device 42 is, for example, a touch panel that is superimposed on the monitor 31 and accepts various touch operations by the user. That is, the monitor 31 according to this embodiment is a touch panel type display unit. However, the specific hardware configuration of the user terminal 20 is not limited to the above example. As another example, the user terminal 20 may include a controller equipped with buttons, an operation stick, etc. as the operation device 42.

[0033] [Functional block diagram of user terminal 20] Fig. 5 is a functional block diagram of the user terminal 20. As shown in Fig. 5, the terminal program 23P loaded into the memory 22 causes the user terminal 20 (computer) to function as an object generating means 210, a selecting means 220, a profile generating means 230, an automatic editing means 240, a learning means 250, a publishing means 260, a user editing means 270, and a feedback means 280.

[0034] 5 mainly illustrates functional blocks for generating and publishing a profile, the user terminal 20 also has a function for realizing processes in the online space (e.g., participation in a game, avatar movements). Also, while FIG. 5 mainly illustrates functional blocks of the user terminal 20A, the user terminals 20B and 20C also have similar functional blocks.

[0035] First, an example of a process for generating an avatar and a profile in the user terminal 20A will be described with reference to Figs. 6 to 8. Fig. 6 is an example of an avatar generation screen. Fig. 7 is an example of the priority (A) of setting parameters and setting value histograms (B) and (C). Fig. 8 is an example of profiles (A), (B) and learning data (C).

[0036] The object generation means 210 displays an avatar generation screen shown in Fig. 6 on the monitor 31. The avatar generation screen is a screen for generating an avatar (character). The avatar generation screen is also a screen for allowing the user of the user terminal 20A to select multiple setting values ​​in order to set the visual (appearance) or auditory (tone of voice) characteristics of the avatar. The process of generating an avatar and a profile, focusing on visual characteristics, will be described below.

[0037] An avatar is an example of an object that acts as an avatar of a user in an online space. The avatar may be in a 3D format, which is a shape drawn three-dimensionally, or in a 2D format, which is a shape drawn two-dimensionally. The avatar is specified by data representing visual characteristics (e.g., image data, 3D model data) and data representing auditory characteristics (e.g., audio data), or a combination of these.

[0038] As shown in FIG. 6, the avatar creation screen includes an avatar display area A1, a gender selection area A2, an age selection area A3, a setting parameter selection area A4, a candidate icon selection area A5, and a "OK" icon.

[0039] Avatar display area A1 is an area for displaying an image of an avatar (hereinafter, referred to as an "avatar image") generated according to setting values ​​selected by the user through selection areas A2 to A5. That is, the appearance of the avatar image displayed in avatar display area A1 changes as the setting values ​​are changed through selection areas A2 to A5. Furthermore, the displayed avatar image may change (for example, rotate, shrink, enlarge) by a touch operation on avatar display area A1 (for example, a swipe operation, a pinch-in operation, a pinch-out operation).

[0040] The gender selection area A2 is an area for selecting the gender of the avatar. The gender selection area A2 includes a "Male" icon and a "Female" icon as candidates for the setting value of the gender parameter. The age selection area A3 is an area for selecting the age of the avatar. The age selection area A3 includes a "Child" icon and an "Adult" icon as candidates for the setting value of the age parameter. However, the age selection area A3 may also include a text box or pull-down menu for inputting the date of birth.

[0041] The setting parameter selection area A4 is an area for selecting setting parameters (contour, hairstyle, skin colour, clothing, colour). The setting parameter selection area A4 includes an [Contour] tab, an [Hairstyle] tab, a [Skin Colour] tab, a [Clothing] tab, and a [Color] tab. The contour indicates the contour of the avatar's face. The hairstyle indicates the avatar's hairstyle. The skin colour indicates the avatar's skin colour. The clothing indicates the clothing worn by the avatar. The colour indicates the base colour of the avatar (e.g. the colour of the hair, clothing, accessories).

[0042] The candidate icon selection area A5 is an area for selecting a setting value corresponding to the selection parameter selected in the setting parameter selection area A4. As an example, when the [Hairstyle] tab is selected in the setting parameter selection area A4, a plurality of candidate icons showing candidates for the setting value of selectable hairstyle (e.g., short, bun, twin tail, ponytail) are displayed in the setting parameter selection area A4. As another example, when the [Clothes] tab is selected in the setting parameter selection area A4, a plurality of candidate icons showing candidates for the setting value of selectable clothes (e.g., one-piece, miniskirt, denim, maid) are displayed in the setting parameter selection area A4.

[0043] In the example of Fig. 6, gender, age, contour, hairstyle, skin color, clothes, and color are examples of setting parameters. Also, candidate icons (e.g., [Male] icon, [Female] icon) that can be selected in association with a setting parameter (e.g., gender) are candidates for setting values ​​that represent visual characteristics of an avatar (object). In other words, a setting parameter represents a group (type) of setting values ​​of the same type.

[0044] However, specific examples of the setting parameters are not limited to the above examples. Moreover, for the above setting parameters, the selectable setting value candidates are not limited to the above examples. Furthermore, the setting parameters are not limited to those that set the visual characteristics of the avatar. For example, a voice tone parameter is an example of a setting parameter that sets the auditory characteristics of the avatar. Examples of setting values ​​for the voice tone parameter include soprano, mezzo-soprano, and alto.

[0045] Note that the avatar itself as a visual feature, items worn by the avatar as a visual feature (e.g., clothes, accessories, etc.), and the voice data of the avatar as an auditory feature may be acquired through payment, gacha, etc. For example, it may be possible to acquire an avatar that looks different from the default avatar, items worn by characters appearing in manga or anime, voice data of real voice actors, etc.

[0046] Then, object generating means 210 generates avatar data (image data, 3D model data) according to a plurality of setting values ​​(e.g., gender=female, age=child, face shape=round face, hairstyle=pigtails, skin color=fair, clothing=maid, color=white) selected by the user through selection areas A2 to A5, and displays an avatar image represented by the generated avatar data in avatar display area A1. Furthermore, object generating means 210 finalizes the generated avatar data in response to selection of the [Enter] icon.

[0047] The selection means 220 selects setting values ​​to be input to the trained model 16b from among a plurality of setting values ​​selected through the avatar generation screen. The selection means 220 selects setting values ​​to be input to the trained model 16b, for example, using the priority table shown in FIG. 7(A) and the setting value histograms shown in FIG. 7(B) and FIG. 7(C). In this embodiment, it is assumed that four setting values ​​are selected from among seven setting values. However, the number of setting values ​​to be selected is not limited to the above example.

[0048] The priority table shown in FIG. 7(A) holds an initial priority for each setting parameter. The initial priority indicates a predetermined priority for a plurality of setting parameters. The initial priority indicates that the smaller the numerical value, the higher the priority. The priority table is set, for example, by an administrator of the game server 10, and downloaded in advance to the user terminal 20A. That is, the selection means 220 selects fixed setting values ​​for gender, age, hairstyle, and skin color only according to the priority table shown in FIG. 7(A).

[0049] The setting value histograms shown in Fig. 7(B) and Fig. 7(C) are obtained by tallying up setting values ​​previously selected by other users for each setting parameter (e.g., hairstyle, clothing) in the online space provided by the game server 10. The vertical axis in Fig. 7(B) and Fig. 7(C) may represent the number of times selected (frequency) or the selection rate (%). These setting value histograms are tallied, for example, by the game server 10. The selection means 220 receives the setting value histograms from the game server 10 via the communication IF 25. Note that Fig. 7 only shows the setting value histograms for hairstyle parameters and clothing parameters, but setting value histograms for other setting parameters may also be referenced.

[0050] The selection means 220 then changes the priority of the setting parameters based on the setting value histogram acquired from the game server 10. As an example, as shown in Fig. 7(B), the selection means 220 does not change the priority of the hairstyle parameter because "pigtails" is the second most frequently selected hairstyle parameter. As another example, as shown in Fig. 7(C), the selection means 220 changes the priority of the clothing parameter to "1" (the highest) because "maid" is the least frequently selected clothing parameter.

[0051] As a result, in the examples of FIG. 6 and FIG. 7, the selection means 220 selects gender=female, age=child, clothing=maid, and skin color=fair as the setting values ​​to be input to the trained model 16b. In this way, the selection means 220 may preferentially select a setting value that has been selected less frequently by other users among a plurality of setting values. However, the method in which the selection means 220 selects a setting value is not limited to this. As another example, the selection means 220 may randomly select a setting value to be input to the trained model 16b from a plurality of setting values. As yet another example, the selection means 220 may preferentially input a setting value indicating an avatar, item, or voice acquired by paying a fee to the trained model 16b over other setting values.

[0052] The profile generating means 230 inputs the setting values ​​selected by the selection means 220 (e.g., gender=female, age=child, clothing=maid, skin color=fair) into the trained model 16b to generate a profile of the avatar. The profile generating means 230 may further input the name of the avatar determined (input) by the user (e.g., name=Linda) into the trained model 16b. The profile generating means 230 may further input the range of the number of characters in the profile (e.g., 70 to 100 characters) into the trained model 16b.

[0053] For example, when having AI 16 capable of natural language processing generate a profile, the profile generation means 230 sends the following text-format input data to the AI ​​server 15 via the communication IF 25: "Please generate a profile of Linda, a woman, child, maid, fair-skinned, in 70 to 100 characters."

[0054] The AI ​​server 15 inputs input data acquired from the user terminal 20A to the input layer L1, processes the data in the intermediate layer L2, and outputs a text-format profile from the output layer L3. The AI ​​server 15 also transmits the generated profile to the user terminal 20A. The profile generation means 230 then receives the profile (output data) shown in FIG. 8(A) from the AI ​​server 15 via the communication IF 25.

[0055] A profile is text-format data that introduces an avatar. A profile may include any text information related to an avatar, such as the avatar's preferences, what the avatar wants to do in the online space, messages to other users, a backstory (e.g., hometown, educational background, work history), and family composition. A profile is also text that is imagined from multiple setting values ​​entered as input data. However, a profile is not limited to text format, and may also be in image format, audio format, or a combination of these. Furthermore, a profile does not need to be true and may be fiction.

[0056] The automatic editing means 240 edits the profile generated by the profile generating means 230. As one example, the automatic editing means 240 may delete predetermined keywords from the profile generated by the profile generating means. As another example, the automatic editing means 240 may replace predetermined keywords in the profile generated by the profile generating means with other words. Examples of keywords include sexual, violent, and discriminatory expressions, setting values ​​included in the input data, and specific personal information (current address, phone number), etc.

[0057] In the example of FIG. 8(B), the setting value "fair-skinned maid" is deleted, and the sexual expression "sexy" is replaced with "various." Whether to delete the keyword or replace it with another word may be determined in advance, or the AI ​​16 may determine this. Furthermore, the automatic editing means 240 may skip the execution of editing if the predetermined keyword is not included in the profile.

[0058] As shown in FIG. 8(C), the learning means 250 inputs learning data, which uses "Please create a profile of Linda, who is female, child, maid, and fair-skinned, in 70 to 100 characters," and the profile edited by the automatic editing means 240 as correct answer data, into the trained model 16b for further learning. That is, the learning means 250 transmits the learning data to the AI ​​server 15 via the communication IF 25. The AI ​​server 15 uses the learning data received from the user terminal 20A to cause the trained model 16b to execute a learning process.

[0059] The publishing means 260 publishes in the online space the profile generated by the profile generating means 230 or edited by the automatic editing means 240 in association with the avatar generated by the object generating means 210. More specifically, the publishing means 260 associates the avatar data generated by the profile generating means 230 with the profile data generated by the profile generating means 230 or the automatic editing means 240, and transmits them to the game server 10 via the communication IF 25.

[0060] In response to requests from the user terminals 20B and 20C, the game server 10 transmits the avatar data and profile data received from the user terminal 20A to the user terminals 20B and 20C. This allows the users of the user terminals 20B and 20C to perceive the avatar (hereinafter referred to as "avatar A") and profile (hereinafter referred to as "profile A") generated by the user terminal 20A through the monitor 31 and the speaker 36.

[0061] As an example, the users of the user terminals 20B and 20C visually recognize avatar A that comes into view of their own avatars B and C in the online space. The users of the user terminals 20B and 20C also view profile A by selecting avatar A that comes into view. Furthermore, the users of the user terminals 20B and 20C view avatar A and profile A through a user selection screen or a user detail screen, which will be described later with reference to FIG. 13.

[0062] Next, an example of a process for generating an avatar and a profile in user terminal 20B will be described with reference to Figs. 9 to 11. Fig. 9 is another example of an avatar generation screen. Fig. 10 is another example of the setting parameter priorities (A) and setting value histograms (B) and (C). Fig. 11 is another example of profiles (A), (B) and learning data (C). Note that a detailed description of commonalities with the examples of Figs. 6 to 8 will be omitted, and differences will be mainly described. The examples of Figs. 9 to 11 differ from the examples of Figs. 6 to 8 in the combination of setting values ​​selected by the user through the avatar generation screen.

[0063] More specifically, it is assumed that the user of user terminal 20B has selected the following settings: gender=male, age=adult, face shape=round face, hairstyle=mohawk, skin color=dark, clothing=kamikaze uniform, and color=white. Object generating means 210 generates avatar data in accordance with these settings, and displays an avatar image represented by the generated avatar data in avatar display area A1.

[0064] 10(A) and 10(B), since the number of selections of "mohawk" in the hairstyle parameters and "kamikaze uniform" in the clothing parameters are the least, the selection means 220 changes the priority of the hairstyle parameters and clothing parameters to "1." Then, in the example of Fig. 9 and Fig. 10, the selection means 220 selects gender = male, age = adult, hairstyle = mohawk, and clothing = kamikaze uniform as the setting values ​​to be input to the trained model 16b.

[0065] The profile generating means 230 inputs input data in text format, "Please generate a profile of Taro, male, adult, with a mohawk and a special attack uniform, in 70 to 100 characters," to the trained model 16b to generate the profile shown in FIG. 11(A). The automatic editing means 240 deletes the violent expression "fighting" from the profile shown in FIG. 11(A) and replaces the violent expression "street fight" with "sports." The role of the automatic editing means 240 may be assigned to the trained model 16b. That is, the trained model 16b may pre-learn predetermined keywords (in other words, keywords that are not suitable for use in a profile) and generate a profile that does not include the keywords.

[0066] As shown in Figure 11 (C), the learning means 250 takes as input data "Please create a profile of Taro, male, adult, with a mohawk and a kamikaze uniform, in 70 to 100 characters." and inputs the learning data, which has the profile edited by the automatic editing means 240 as correct answer data, into the trained model 16b for further training.

[0067] The publishing means 260 publishes in the online space the profile (hereinafter referred to as "profile B") generated by the profile generating means 230 or edited by the automatic editing means 240, in association with the avatar (hereinafter referred to as "avatar B") generated by the object generating means 210.

[0068] 12 shows an example of a profile editing screen (A) and learning data (B). The user editing means 270 edits the profile generated by the profile generating means 230 or edited by the automatic editing means 240 according to an instruction from the user of the user terminal 20A. More specifically, the user editing means 270 displays the profile editing screen shown in FIG. 12(A) on the monitor 31.

[0069] The profile editing screen includes a profile display area A6, a software keyboard A7, and a [OK] icon. The profile display area A6 is an area for displaying a profile generated by the profile generating means 230 or edited by the automatic editing means 240. The software keyboard A7 is an area for receiving a user's operation to instruct editing (adding, deleting, or correcting characters) of the profile displayed in the profile display area A6.

[0070] The user editing means 270 edits the profile displayed in the profile display area A6 according to the user's operation (instruction) via the software keyboard A7. Then, the user editing means 270 finalizes the editing of the profile in response to the selection of the [OK] icon. Note that, when the user inputs a predetermined keyword (in other words, a keyword that is not suitable for use in the profile), the user editing means 270 may not reflect the keyword in the profile.

[0071] Then, the publishing means 260 publishes the profile edited by the user editing means 270 in association with the avatar. Also, as shown in Fig. 12(B), the learning means 250 inputs learning data including the input data generated by the profile generating means 230 and the profile (correct answer data) edited by the user editing means 270 to the trained model 16b for further learning.

[0072] Next, a process in which a user of the user terminal 20C browses avatars A and B and profiles A and B will be described with reference to Fig. 13. Here, a process in which a user selects a user to play cooperatively in a cooperative online game will be described as an example. Fig. 13 shows examples of a user selection screen (A) and a user details screen (B).

[0073] The user terminal 20C displays a user selection screen shown in Fig. 13(A) on the monitor 31. The user selection screen displays a list of users who can play cooperatively (e.g., users who are logged in to an online game). The user selection screen displays, for example, an avatar, name, level, and a [details] icon for each user who can play cooperatively. That is, the user terminal 20C receives the avatars, names, and levels of users who can play cooperatively from the game server 10, and displays them in a list on the user selection screen.

[0074] In response to the selection of the [Details] icon corresponding to the name Linda, for example, the user terminal 20C displays the user details screen shown in Fig. 13(B) on the monitor 31. The user details screen is a screen that displays detailed information of the user selected through the user selection screen. The user details screen includes, for example, the name, level, avatar image, profile, rating, and [Request for Cooperation] icon of the selected user.

[0075] The evaluation items on the user details screen include a [GOOD] icon, a [BAD] icon, the number of high ratings in the past when the [GOOD] icon was selected (91), and the number of low ratings in the past when the [BAD] icon was selected (9). The [GOOD] icon corresponds to a high rating by a user who viewed the profile. The [BAD] icon corresponds to a low rating by a user who viewed the profile. The number of high ratings and the number of low ratings are cumulative values ​​tallied by the game server 10. When the [GOOD] icon or the [BAD] icon is selected, the user terminal 20C notifies the game server 10 of the selected icon. The game server 10 updates the tallied results of the number of high ratings and the number of low ratings, and notifies the user terminal 20A of the updated number of high ratings and the number of low ratings.

[0076] The feedback means 280 receives the updated number of likes and dislikes from the game server 10 through the communication IF 25. The number of likes and dislikes is an example of an evaluation of the profile. Then, the feedback means 280 feeds back the evaluation of the profile to the trained model 16b. The feedback means 280 transmits, for example, the number of likes as a score for the profile to the AI ​​server 15 through the communication IF 25. The trained model 16b relearns or additionally learns using the number of likes fed back from the feedback means 280. That is, the trained model 16b becomes more likely to generate a profile that is close to a profile with a large number of likes and less likely to generate a profile that is close to a profile with a small number of likes.

[0077] The timing and conditions for the feedback means 280 to feed back the evaluation are, for example, as follows. As one example, the feedback means 280 executes feedback when a predetermined time (e.g., 10 hours) has elapsed since the profile was made public. As another example, the feedback means 280 executes feedback on the condition that the number of likes, the number of dislikes, or the total number of likes and dislikes has reached a predetermined number (e.g., 100 times). However, the specific method of evaluation of a profile by other users is not limited to the above example.

[0078] [Effects of the embodiment] According to the above embodiment, a profile of an avatar is generated in the trained model 16b using a plurality of setting values ​​selected when generating an avatar as input data. This makes it possible to publish a unique profile generated according to an avatar in association with the avatar without increasing the workload of the user.

[0079] Furthermore, according to the above embodiment, a unique profile can be generated by selecting a setting value to be input to the trained model 16b from among a plurality of setting values. In particular, a more unique profile can be generated by preferentially inputting a setting value that is selected less frequently by other users. However, when all setting values ​​are input to the trained model 16b, the selection means 220 can be omitted.

[0080] According to the above embodiment, the profile generated by the trained model 16b can be edited to prevent a profile including undesirable expressions from being published in the online space. However, if the trained model 16b is trained to exclude undesirable expressions, the automatic editing means 240 can be omitted.

[0081] Furthermore, according to the above embodiment, by enabling the user to edit the profile, the user's preferred profile can be published in the online space. Also, compared to having the user create the profile from scratch, the user's workload can be reduced. However, the user editing means 270 can be omitted.

[0082] Furthermore, according to the above embodiment, the profile edited by the automatic editing means 240 and the user editing means 270 can be retrained as correct answer data in the trained model 16b to generate a profile that is more unique, does not include undesirable expressions, and matches the user's preferences. However, when using a general-purpose AI 16 that does not disclose the input interface for training data, the training means 250 can be omitted.

[0083] Furthermore, according to the above embodiment, the evaluations of other users who have viewed the profile published in the online space are collected and fed back to the trained model 16b, thereby generating a profile that is more suitable for the online space. However, when using a general-purpose AI 16 that does not publish a feedback input interface, the feedback means 280 can be omitted.

[0084] [Other embodiments] Note that specific examples of objects are not limited to avatars, and may include characters such as animals, monsters, and robots, equipment (e.g., weapons, armor, accessories), vehicles (e.g., automobiles, ships, aircraft, and spaceships), and buildings (e.g., houses and castles). Specific examples of setting parameters vary depending on the type of object. For example, setting parameters corresponding to a house object include the shape of the roof, the material of the walls, the shape of the windows, and the layout of the room.

[0085] Furthermore, the program according to the present invention is not limited to a single program, but may be a collection of multiple programs. Furthermore, the program according to the present invention is not limited to being executed by a single device, but may be executed by multiple devices in a shared manner. Furthermore, the division of roles among the game server 10, the AI ​​server 15, and the user terminal 20 is not limited to the above example. That is, a part of the processing of the game server 10 may be executed by the AI ​​server 15 or the user terminal 20, a part of the processing of the AI ​​server 15 may be executed by the game server 10 or the user terminal 20, or a part of the processing of the user terminal 20 may be executed by the game server 10 or the AI ​​server 15.

[0086] Furthermore, a part or all of the means realized by the program can be realized by hardware such as an integrated circuit. Furthermore, the program may be provided by being recorded on a non-transitory recording medium that can be read by a computer. Examples of the recording medium include a hard disk, an SD card, a DVD, and a server on the Internet. [Explanation of symbols]

[0087] 1...system, 2...communication network, 10...game server, 15...AI server, 16...learning model, 16a...learning model, 16b...trained model, 17...neural network, 20...user terminal, 21...processor, 22...memory, 23...storage, 23P...terminal program, 25...communication interface, 26...computer, 29...communication bus, 31...monitor, 33...camera, 34...camera, 35...microphone, 36...speaker, 41...motion sensor, 42...operation device, 210...object generation means, 220...selection means, 230...profile generation means, 240...automatic editing means, 250...learning means, 260...publication means, 270...user editing means, 280...feedback means

Claims

1. Computer, an object generating means for generating an object to be placed in an online space in accordance with a plurality of setting values ​​selected by a user for setting visual characteristics of the object; A profile generating means for inputting a plurality of the setting values ​​into a trained model that has been trained in advance to generate a profile of the object; a program that functions as publishing means for associating the profile generated by the profile generating means with the object generated by the object generating means and publishing the profile in the online space;

2. The program according to claim 1, causing the computer to function as an automatic editing means for editing the profile generated by the profile generating means; The publishing means publishes the profile edited by the automatic editing means.

3. The program according to claim 2, The automatic editing means deletes predetermined keywords from the profile generated by the profile generation means.

4. The program according to claim 2, A program that causes the computer to function as a learning means for inputting learning data, in which a plurality of the setting values ​​are used as input data and the profile edited by the automatic editing means is used as correct answer data, into the trained model for further training.

5. The program according to claim 1, A program that causes the computer to function as a selection means for selecting the setting value to be input to the trained model from among the multiple setting values.

6. The program according to claim 5, The selection means is a program for preferentially selecting, from among the plurality of setting values, the setting value that has been selected less frequently by other users.

7. The program according to claim 1, The computer, a user editing means for editing the profile generated by the profile generating means in accordance with an instruction from a user; The program functions as a learning means for inputting learning data, which has a plurality of the setting values ​​as input data and the profile edited by the user editing means as correct answer data, into the trained model for further training.

8. The program according to claim 1, A program that causes the computer to function as a feedback means for obtaining evaluations of the profile from other users who have viewed the profile published by the publishing means and providing feedback to the trained model.

9. an object generating means for generating an object to be placed in an online space in accordance with a plurality of setting values ​​selected by a user for setting visual characteristics of the object; A profile generating means for inputting a plurality of the setting values ​​into a trained model that has been trained in advance to generate a profile of the object; a publishing means for publishing the profile generated by the profile generating means in the online space in association with the object generated by the object generating means.

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