Content providing method and information processing system

Abstract

Expanding the scope of use of generative AI. [Solution] A content provision method including a learning step in which a processor trains a learning model using content related to a specific person and licensed by a rights holder, and a provision step in which the processor causes the trained learning model to generate content that reflects the characteristics of the specific person, and provides the generated content to users using a specified service.

Description

[Technical Field]

[0001] The present invention relates to a content providing method and an information processing system. [Background technology]

[0002] Various services using generation AI are known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7592908 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a need to expand the scope of use of generative AI.

[0005] The present invention aims to broaden the scope of applications of generative AI. [Means for solving the problem]

[0006] According to one embodiment shown in the present disclosure, a learning step in which the processor trains the learning model using content related to a specific person and licensed by a rights holder; a providing step in which the processor causes the trained learning model to generate content that reflects the characteristics of the specific person, and provides the generated content to a user who uses a predetermined service. A content providing method is provided. [Effects of the Invention]

[0007] According to the present invention, it is possible to broaden the scope of applications of generation AI. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating a schematic configuration of an information processing system. [Figure 2] FIG. 2 is a block diagram showing a functional configuration of the information processing system. [Figure 3] FIG. 10 is a diagram showing an example of a screen on which a user can select a person for which generated content is to be generated. [Figure 4] FIG. 10 is a diagram showing an example of a screen on which a user can specify the details of content to be generated. [Figure 5] FIG. 1 is a diagram illustrating an example of an information processing system in which a plurality of businesses are involved. [Figure 6] 10 is a flowchart illustrating an example of a process related to content generation. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] [First embodiment] <System hardware configuration> As shown in FIG. 1, an information processing system 1 of this embodiment includes a plurality of terminal devices 10 and a server 20.

[0011] The terminal device 10 and the server 20 are connected via a network 2. The network 2 may be configured by, for example, the Internet, a mobile communication system (e.g., 3G, 4G, 5G, LTE (Long Term Evolution), etc.), Wi-Fi (Wireless Fidelity), Bluetooth (registered trademark), other communication lines, or a combination of these. Furthermore, the connection between the terminal device 10 and the server 20 may be wired or wireless.

[0012] The server 20 (in other words, a computer, an information processing device) may be, for example, a general-purpose computer such as a workstation or a personal computer. The server 20 includes a processor 21, a memory 22, a storage 23, a communication IF (interface) 24, and an input / output IF 25. These components of the server 20 are connected to each other by a communication bus.

[0013] The processor 21 controls the overall operation of the server 20. The processor 21 may include a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), etc. The processor 21 reads a program from the storage 23 and loads it into the memory 22. The processor 21 executes the loaded program.

[0014] The memory 22 is a main storage device. The memory 22 is configured by storage devices such as a ROM (Read Only Memory) and a RAM (Random Access Memory). The memory 22 temporarily stores programs and various data that the processor 21 reads from the storage 23, thereby providing a working area for the processor 21. The memory 22 also temporarily stores various data that the processor 21 generates while operating according to the programs.

[0015] In this embodiment, the program may be a program for realizing a game on the terminal device 10. The various data include, for example, data related to the game, such as user information and game information, as well as instructions and notifications transmitted and received between the terminal device 10 and the server 20.

[0016] The storage 23 is an auxiliary storage device. The storage 23 is configured by a storage device such as a flash memory or an HDD (Hard Disk Drive). Various types of data are stored in the storage 23.

[0017] The communication IF 24 controls transmission and reception of various data between the server 20 and the terminal device 10 and the like via the network.

[0018] The input / output IF 25 is an interface through which the server 20 receives input of data and also an interface through which the server 20 outputs data. The input / output IF 25 may include, for example, an input unit which is an information input device such as a mouse or a keyboard, and a display unit which is a device that displays and outputs images.

[0019] The terminal device 10 (in other words, a computer, an information processing device) may be, for example, a smartphone, a feature phone, a PDA (Personal Digital Assistant), a tablet computer, a personal computer, a wearable terminal, or a game device. The terminal device 10 may be a mobile terminal. The terminal device 10 may be a portable terminal that a user uses when playing a game.

[0020] The terminal device 10 includes a processor 11, a memory 12, a storage 13, a communication IF 14, an input / output IF 15, an input unit 17, and a display unit 18. These components included in the terminal device 10 are connected to each other by a communication bus.

[0021] The processor 11 controls the overall operation of the terminal device 10. The processor 11 may include a CPU, an MPU, a GPU, etc. The processor 11 reads a program from the storage 13 and loads it into the memory 12. The processor 11 executes the loaded program.

[0022] The memory 12 is a main storage device. The memory 12 is configured by storage devices such as a ROM and a RAM. The memory 12 provides a working area for the processor 11 by temporarily storing the programs and various data that the processor 11 reads from the storage 13. The memory 12 also temporarily stores various data that the processor 11 generates while operating according to the programs.

[0023] The storage 13 is an auxiliary storage device. The storage 13 is configured by a storage device such as a flash memory or a HDD. The storage 13 stores various data related to the game.

[0024] The communication IF 14 controls transmission and reception of various data between the terminal device 10 and the server 20 etc. via the network.

[0025] The input / output IF 15 is an interface through which the terminal device 10 receives input of data and also an interface through which the terminal device 10 outputs data. The input / output IF 15 may input and output data via, for example, a USB (Universal Serial Bus) or the like. The input / output IF 15 may include an input unit 17, a display unit 18, or the like.

[0026] The input unit 17 accepts input from a user. The input unit 17 may be, for example, a pointing device such as a touchpad. The display unit 18 displays images. The display unit 18 may be, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The terminal device 10 includes, for example, a touch screen 16 which is an electronic component that combines the input unit 17 and the display unit 18.

[0027] The input unit 17 has a function of detecting a position input on the input surface by a user operation (for example, a touch operation, a tap operation, a slide operation, a swipe operation, a flick operation, etc.) and transmitting information indicating the detected position as an input signal. The touch panel serving as the input unit 17 may be of a capacitive type or a resistive type, or may be of another type.

[0028] The input unit 17 may be, for example, a keyboard, various physical buttons, various sensors (for example, an acceleration sensor, an angular velocity sensor, a magnetic sensor, a GPS sensor, a motion sensor, a gaze sensor, a bioelectric potential sensor, a fingerprint sensor, a breath sensor, a pressure sensor, or an image sensor), an operation stick, a camera, or a microphone. The display unit 18 may be, for example, a projector.

[0029] <System Functional Configuration> 2 is a block diagram showing the functional configuration of the server 20 and the terminal device 10. Each of the server 20 and the terminal device 10 may include functional configurations (not shown) required to function as a general computer and functional configurations required to implement known functions in a game.

[0030] In this embodiment, the server 20 identifies each user and the terminal device 10 using a user account that is registered in advance. The method of registering the account is not particularly limited. For example, the terminal device 10 or another device such as a personal computer may transmit information required for registering a user account to the server 20 based on a user operation, and the server 20 may create and store an account for each user based on the received information.

[0031] The server 20 has a function of communicating with each terminal device 10 and providing predetermined services to users via the terminal device 10. The server 20 functions as a control unit 210 and a storage unit 220 through cooperation of a processor 21, a memory 22, a storage 23, a communication IF 24, an input / output IF 25, and the like.

[0032] The storage unit 220 stores various data used by the control unit 210. The various data includes, for example, programs and data referenced when the control unit 210 executes the programs.

[0033] The control unit 210 controls various processes related to the service (e.g., a game) according to this embodiment by executing a program stored in the storage unit 220. The control unit 210 includes, for example, a learning unit 211 and a generation unit 212. The control unit 210 also transmits and receives various data to and from the terminal device 10. In response to a request from the terminal device 10, the control unit 210 executes arithmetic processing described in the program, thereby providing the terminal device 10 with a predetermined service (e.g., a game).

[0034] The terminal device 10 has, for example, a function as an input device that accepts input operations from a user, and a function as an output device that outputs images and sounds related to a predetermined service.

[0035] The terminal device 10 functions as a control unit 110 and a storage unit 120 through cooperation of the processor 11, memory 12, storage 13, communication IF 14, input / output IF 15, and the like.

[0036] The storage unit 120 stores various data used by the control unit 110. The various data includes, for example, programs and data that the control unit 110 refers to when executing the programs.

[0037] The control unit 110 executes a program stored in the storage unit 120 to control various processes related to a service (e.g., a game) provided to a user on the terminal device 10. The control unit 110 has, for example, an operation reception unit 111, a game control unit 113, and a display control unit 114. The control unit 110 also transmits and receives various data to and from the server 20. The control unit 110 performs various processes related to the provision of the service (in other words, the progress of the game) while communicating with the server 20. In other words, the control unit 110 provides a predetermined service to the user by executing arithmetic processing described in the program.

[0038] The operation reception unit 111 receives an operation (hereinafter also referred to as an "input operation") input by the user via the input unit 17. Specifically, when an input operation is performed on the input unit 17, the operation reception unit 111 detects the coordinates of the input position and the type of input operation. Examples of the types of input operations include various operations performed with fingers, etc., such as a touch operation, a tap operation, a slide operation, a swipe operation, a flick operation, a pinch-in operation, and a pinch-out operation. The input operation is not limited to an operation of physically contacting the input unit 17 (for example, the touch screen 16) but may also include a non-contact operation. Note that an operation of ending a previously performed input operation, such as a touch-off operation of ending contact with the touch screen 16, can also be considered as one form of input operation.

[0039] Here, the operation reception unit 111 can also receive input operations performed using an operation device connected via the input / output IF 15 in the same way as input operations to the input unit 17 .

[0040] The game control unit 113 executes various processes related to the progress of the game. The game control unit 113 identifies the user's instruction content based on the user's input operation detected by the operation reception unit 111. The game control unit 113 also executes various determination processes related to the progress of the game based on the identified instruction content, etc. The game control unit 113 also progresses the game while communicating with the server 20 based on the results of the determination processes, etc. The game control unit 113 also instructs the display control unit 114 to display an image corresponding to the progress of the game on the display unit 18.

[0041] The display control unit 114 displays images related to the game on the display unit 18. The display control unit 114 displays various images stored in the storage unit 220 or the storage unit 120, various images generated by the generation unit 212, and the like on the display unit 18. Specifically, the display control unit 114 generates an image to be displayed on the display unit 18 using the various images, and displays the image on the display unit 18. The various images include images of objects (in other words, game media) within the game (in other words, within the virtual space) (for example, images of characters such as a player character, items, and backgrounds), and images related to a UI (User Interface) required for various game operations, such as icons, buttons, and menus indicating various parameters.

[0042] Note that the functions of the terminal device 10 and the server 20 illustrated in FIG. 2 are merely examples. Each of the terminal device 10 and the server 20 may have at least some of the functions of the other device. In other words, the server 20 may have some or all of the functional blocks of the terminal device 10 in this embodiment, or the terminal device 10 may have some or all of the functional blocks of the server 20. Furthermore, each device, such as the terminal device 10 and the server 20, does not have to be realized by an integrated device, but may be realized, for example, by multiple devices connected via a network or the like. Furthermore, the information processing system 1 may be configured, for example, by only the terminal device 10 or the server 20. In other words, the information processing system 1 does not have to be realized by multiple devices connected via a network.

[0043] <Processing according to this embodiment> Next, the processing according to this embodiment will be described. In this embodiment, the processor 11 of the terminal device 10 or the processor 21 of the server 20 executes a program stored in the information processing system 1 to perform each of the processing described below. However, at least a portion of the processing performed by the processor 11, which is described below, may be executed by a processor other than the processor 11. Furthermore, at least a portion of the processing performed by the processor 21, which is described below, may be executed by a processor other than the processor 21. In other words, the computer that executes the program in this embodiment may be either the terminal device 10 or the server 20, or may be realized by a combination of multiple devices. Furthermore, the program appearing in this embodiment may be provided in a state recorded on a non-transitory computer-readable recording medium.

[0044] (Learning model) The learning unit 211 inputs content related to a specific person (hereinafter referred to as "learning content") into the learning model, and generates a learning model that has been trained to be able to generate content that reflects the characteristics of the specific person (hereinafter referred to as "generated content"). In other words, the learning unit 211 generates a learning model (specifically, a trained learning model) that can generate generated content that reflects the characteristics of the specific person by learning using the learning content related to the specific person (in other words, based on the learning content related to the specific person). The trained learning model generated by the learning unit 211 is stored in the storage unit 220. Note that the specific person is a real person, but may be a living person or a deceased person.

[0045] The learning content may be, for example, content in which a specific person is performing a specific performance. Specifically, the learning content may include content in which a specific singer is singing, content in which a specific dancer is dancing, content in which a specific actor is acting, content in which a specific athlete is competing, etc.

[0046] The learning content may also be, for example, content related to a conversation between a specific person. Specifically, the learning content may be a video of a specific person talking, audio data of a specific person talking, or text data of a conversation between a specific person.

[0047] Furthermore, the study content may be, for example, content created by a specific person. Specifically, the study content may be a picture drawn by a specific person, or a manga, anime, game, drama, movie, novel, script, music, or video created by a specific person. In other words, the study content may be the work of a specific person (in other words, an artist).

[0048] The data format of the study content may be image data (including still images and moving images), text data, audio data, or the like.

[0049] As described above, learning is performed by inputting study content into the learning model. Here, inputting the study content into the learning model may involve directly inputting the study content into the learning model, or may involve converting the study content into a form understandable by the learning model and then inputting the converted study content into the learning model. This conversion may also be performed using a predetermined generation AI or the like. Specifically, the learning unit 211 may input the study content into a predetermined generation AI to extract features of the study content, and input the extracted features into the learning model to perform learning based on the study content. In other words, learning of the learning model is performed based on the study content, as long as the features of a specific person appearing in the study content are reflected in the generated content.

[0050] The generated content may be, for example, content in which a person related to the learning content used to train the learning model performs a specific performance. Specifically, the generated content may be content in which a person related to the learning content used to train sings, dances, performs, plays a sport (in other words, competes), etc.

[0051] The generated content may also be, for example, content in which a person related to the learning content used to train the learning model gives a talk.

[0052] Furthermore, the generated content may be, for example, content that reflects the style of a person associated with the learning content used to train the learning model. In other words, it may be content that imitates the learning content used to train the learning model. Specifically, the generated content may be a picture, manga, anime, game, drama, movie, novel, script, music, video, or the like that reflects the style of a person associated with the learning content used for training. In other words, for example, if the learning content is a picture drawn by a specific person, the generated content may be a picture that reflects the style of the specific person.

[0053] Note that generated content, such as content in which a person related to the learning content performs a specific performance, content in which that person talks, or content that reflects the style of that person, is generated by the learning model and can be said to imitate the performance, talk, or work of that person, or can be said to be a pseudo (or virtual) realization of the performance, talk, or work of that person. Furthermore, such content can also be said to be a pseudo (or virtual) performance or talk of that person. Here, the pseudo (or virtual) person can be the person in the game (or an image of the person) generated by the generation unit 212 and displayed on the display unit 18.

[0054] The data format of the generated content may be image data (including still images and moving images), text data, audio data, or the like.

[0055] (Permission of the Rights Holder) The learning unit 211 performs learning of the learning model using training content authorized by a rights holder. Here, a rights holder is a person who has certain rights to training content related to a specific person, and may include, for example, a copyright holder, a person who holds moral rights, a person who holds neighboring rights, a person who holds portrait rights, or a person who holds publicity rights. A rights holder can also be said to have the right to grant permission to use specific content as training content. A rights holder can also be said to have the right to grant permission to use content that reflects the characteristics of a specific person, generated by a trained learning model, in a specific service (in other words, to provide it to users). Note that a rights holder may be a corporation or a natural person.

[0056] The rights holder may be an organization to which a person related to the study content belongs, or the person himself / herself.

[0057] For example, if the learning content is content related to a specific celebrity (e.g., a video of an idol singing or talking), the parties involved in the production of the content (including the specific celebrity) hold the copyright and other rights. The rights holder may be anyone involved in the production of the content. For this type of content, the rights to use the content may be held by, for example, the agency to which the specific celebrity belongs, and the rights holder may be the agency.

[0058] Furthermore, for example, if the educational content is created by a specific person, that person and the organization to which that person belongs (for example, if the specific person is a manga artist, this includes the publishing company with which that person has a contract) hold the copyright and other rights. The rights holder can be any of these parties.

[0059] The rights holder may also be a person who has a predetermined relationship with the person related to the study content.

[0060] For example, in the case of educational content relating to a specific person, that person may be deceased. In such cases, the rights to the educational content may be held by the person's surviving family members or a specific corporation. The rights holder may be any of these parties.

[0061] The control unit 210 stores information indicating that permission from the rights holder has been obtained in the storage unit 220. After performing learning using the learning content or using generated content using a learning model generated by the learning, the control unit 210 can disclose (e.g., display on a predetermined terminal device) based on the information that permission from the rights holder has been obtained for learning using the learning content or use of the generated content. Note that, when using content as learning content (in other words, learning using the content), the learning unit 211 may permit use of the content as learning content and perform learning using the content, on the condition that information indicating permission from the rights holder has been stored for the content. Furthermore, when generating generated content using a trained learning model, the generation unit 212 may permit generation of the generated content and generate the generated content using the learning model, on the condition that information indicating permission from the rights holder has been stored for the generation of the generated content.

[0062] Note that there may be multiple rights holders for one piece of content. Also, multiple pieces of content related to a specific person may be used as study content, and the multiple pieces of content may have the same rights holder or different rights holders for each piece of content.

[0063] (Providing generated content) The generation unit 212 generates generated content using the trained learning model at a predetermined opportunity and provides the generated content to the user. For example, the control unit 110 of the terminal device 10 requests the generation unit 212 to generate generated content. The generation unit 212 generates the generated content based on the request and transmits it to the terminal device 10. Then, the control unit 110 of the terminal device 10 provides the transmitted generated content to the user.

[0064] The provision of the generated content may be, for example, displaying an image (still image or video) generated as the generated content on the display unit 18 of the terminal device 10. The provision of the generated content may also be outputting audio generated as the generated content to an audio output unit (e.g., a speaker) of the terminal device 10. The audio may be a voice sung by a person related to the learning content used to train the learning model, a piece of music played by the person, a voice spoken by the person, or an audio reflecting the person's style (e.g., a piece of music reflecting the style of a particular composer representing the person). In other words, the term "audio" in this specification is not limited to a human voice.

[0065] The learning model can also be called a generative AI. The learning model may be, for example, an image generation AI (in other words, an image generation model), an audio generation AI (in other words, an audio generation model), or an LLM (Large Language Model). Furthermore, multiple learning models may be used to generate one generated content. For example, when generating video content including audio, the generation unit 212 may generate the images and audio of the video content using separate learning models, and then combine the generated images and audio to generate the video content. In other words, the learning unit 211 may use video content including audio as training content to train a learning model for generating images and a learning model for generating audio.

[0066] (Learning models for multiple individuals) The learning unit 211 generates, for each of a plurality of persons, a learning model trained to generate generated content that reflects the characteristics of each person, based on the learning content related to each person. That is, the learning unit 211 executes the following operations: generating a learning model trained to generate generated content that reflects the characteristics of a first person, based on the learning content related to the first person; and generating a learning model trained to generate generated content that reflects the characteristics of a second person, based on the learning content related to the second person. In this way, the learning unit 211 is able to generate learning models trained to generate generated content that reflects the characteristics of each of the first to nth persons (n ​​is a natural number equal to or greater than 2), based on the learning content related to each of the first to nth persons.

[0067] Note that a learning model may be generated for only one person and used to generate generated content. In other words, it is not essential for the information processing system 1 according to this embodiment to be able to generate generated content relating to multiple people.

[0068] Note that a different learning model may be generated for each person, or one learning model may be generated for multiple people. That is, a learning model trained to generate generated content reflecting the characteristics of a first person (in other words, a learning model related to the first person) and a learning model trained to generate generated content reflecting the characteristics of a second person (in other words, a learning model related to the second person) may be the same learning model or different learning models. In other words, trained learning models may be generated separately for each person and stored in storage unit 220.

[0069] Furthermore, when generating a learning model by performing learning based on learning content for each of multiple people, the rights holder of the learning content may be different for each person, or may be the same for multiple people. For example, when generating a learning model by performing learning based on learning content for each of multiple people belonging to a specific entertainment agency, the rights holder of the learning content may be the same regardless of the people.

[0070] (Example 1) Next, a specific example of a service using the configuration according to this embodiment will be described.

[0071] The learning unit 211 generates a learning model capable of generating generated content that reflects the characteristics of a specific entertainer by learning using training content related to the specific entertainer. Specifically, the learning unit 211 generates a learning model capable of generating generated content that reflects the characteristics of each of the first to nth entertainers (n is a natural number of 2 or more) by learning using training content related to each of the first to nth entertainers. The training content related to each of the first to nth entertainers is used for learning with permission from the rights holder.

[0072] The learning content for each entertainer includes at least one of content in which the entertainer performs a specific performance and content in which the entertainer talks. By using content in which a specific entertainer performs a specific performance (e.g., a video of the specific entertainer singing) as learning content, a learning model capable of generating content in which the specific entertainer performs the specific performance (e.g., a video of the specific entertainer singing) can be generated. Furthermore, by using content in which a specific entertainer talks (e.g., a video of the specific entertainer speaking) as learning content, a learning model capable of generating content in which the specific entertainer talks (e.g., a video of the specific entertainer speaking) can be generated. Note that the type of content used as learning content and the type of generated content generated by a learning model trained using the learning content do not necessarily have to match. Furthermore, content in which a specific entertainer talks (e.g., a video of the specific entertainer speaking) can be used as learning content to cause a trained learning model to generate content in which the specific entertainer performs a specific performance (e.g., a video of the specific entertainer singing). Furthermore, the data formats of the learning content and the generated content may be different. That is, for example, it is possible to generate a learning model that uses video data as learning content and generates generated content made up of audio data that does not include image data.

[0073] Generator 212 uses a learning model that has been trained to be able to generate generated content that reflects the characteristics of a specific entertainer, to generate generated content that reflects the characteristics of the specific entertainer, and provides the generated content to the user. Specifically, generator 212 uses a learning model that is able to generate generated content that reflects the characteristics of each entertainer for each of the first to nth entertainers, to generate generated content that reflects the characteristics of each entertainer, and provides the generated content to the user.

[0074] That is, for example, the generation unit 212 generates content in which the first entertainer performs a specific performance using a learning model trained using learning content related to the first entertainer. Alternatively or additionally, the generation unit 212 generates content in which the first entertainer talks using a learning model trained using learning content related to the first entertainer.

[0075] Furthermore, for example, the generation unit 212 generates content in which the second entertainer performs a specific performance using a learning model trained using learning content related to the second entertainer. Alternatively or additionally, the generation unit 212 generates content in which the second entertainer talks using a learning model trained using learning content related to the second entertainer.

[0076] When the generating unit 212 provides the generated content, the generated content is output in the user's terminal device 10. For example, if the generated content is video (in other words, an image) (specifically, video of a specific entertainer performing or talking, etc.), the output of the generated content may be the display of the video as the generated content on the display unit 18. Also, if the generated content is audio (specifically, audio of a specific entertainer performing or talking, etc.), the output of the generated content may be the output of the audio as the generated content from the audio output unit. Note that when the generated content is video, the output of the generated content may include the output of audio from the audio output unit.

[0077] That is, on the user's terminal device 10, generated content that reflects the characteristics of each of the first to nth entertainers can be enjoyed.

[0078] The type of generated content to be generated may vary depending on the characteristics of the person related to the generated content. For example, if the first entertainer is a singer, the generation unit 212 may use a learning model to generate content sung by the first entertainer as the generated content. In other words, the learning unit 211 may train a learning model that generates generated content related to the first entertainer as a learning model that generates content sung by the first entertainer (for example, using content in which the first entertainer sings). Furthermore, for example, if the second entertainer is a comedian, the generation unit 212 may use a learning model to generate content in which the second entertainer talks as the generated content. In other words, the learning unit 211 may train a learning model that generates generated content related to the second entertainer as a learning model that generates content in which the second entertainer talks (for example, using content related to the second entertainer talking).

[0079] The generation unit 212 may determine, based on a user operation, for which person generated content to generate and provide. Then, the generation unit 212 may generate generated content for the determined person using a learning model and provide it to the user. FIG. 3 shows a person selection screen 500 as an example of a screen related to this operation, which is displayed on the display unit 18 of the user's terminal device 10.

[0080] The person selection screen 500 accepts a user operation to select (in other words, instruct) for which person generated content is to be generated. The person selection screen 500 displays a list of people for whom generated content can be generated (in other words, selectable candidates 501). The generation unit 212 generates generated content related to a specific person selected by the user from the people listed on the person selection screen 500, using a learning model trained using learning content related to the specific person, and provides the generated content to the user. This allows the user to select a specific person (e.g., a favorite celebrity) from multiple people and enjoy the content of the selected person.

[0081] Furthermore, the generation unit 212 may determine the content of the generated content to be generated for a specific person based on a user operation. Then, the generation unit 212 may generate the generated content of the determined content using a learning model and provide it to the user. FIG. 4 shows a content selection screen 520 as an example of a screen related to the operation, which is displayed on the display unit 18 of the user's terminal device 10.

[0082] The content selection screen 520 receives a user's operation to instruct (in other words, select) the content of the generated content to be generated (specifically, generated content related to a specific person). The content selection screen 520 displays a plurality of selectable candidates 521 for the content of the generated content to be generated. The generation unit 212 generates generated content with content corresponding to a candidate selected by the user from the plurality of candidates displayed on the content selection screen 520 using a learning model trained using learning content related to the specific person, and provides the generated content to the user. This allows the user to enjoy content corresponding to the content selected by the user as content related to the specific person.

[0083] Here, the operation to instruct the content of the generated content related to a specific person may instruct the type of action to be taken by the specific person (in other words, the type of generated content to be generated). In other words, the generation unit 212 may determine the type of action to be taken by the specific person based on the user's operation, and generate generated content in which the specific person takes the action instructed by the user. Specifically, the operation may instruct, for example, whether to have the specific person sing or talk. Furthermore, when having the specific person sing, it may be possible to instruct whether to have the specific person sing an existing song (in other words, a real song), whether to create a new song, or which song to have the specific person sing. In other words, it may be possible to instruct the specific content of the specific action to be taken by the specific person.

[0084] Furthermore, the operation instructing the content of the generated content related to a specific person may instruct a time at which content of the specific person should be generated. In other words, the generation unit 212 may determine a time at which content of the specific person should be generated based on a user operation. Specifically, for example, when generating generated content in which a specific person sings, the operation may instruct an age at which content of the specific person should be generated. Furthermore, for example, when generating generated content in which a specific person sings, the operation may instruct an event at which content of the specific person should be generated. Specifically, if the specific person has previously performed multiple live music events, instructing to generate content of the specific person at a specific live event may enable the generation of generated content that reflects the characteristics of the specific person at the specific live event (e.g., clothing, singing style, etc.).

[0085] Note that the generation of generated content in response to a user's instruction regarding a person or content can be achieved by changing input information (e.g., a prompt) input to a learning model when generating generated content in response to the user's instruction. Specifically, for example, a prompt or the like can be used to instruct the learning model regarding which person to generate generated content for, what kind of behavior the person will perform, or when to generate the content, thereby causing the learning model to generate generated content in response to the user's instruction. Furthermore, the generation of generated content in response to a user's instruction regarding a person or content can be achieved by changing the learning model used in response to the instruction. For example, when separate learning models are prepared for generating generated content related to a first person and for generating generated content related to a second person, the generated content related to the person in response to the user's instruction can be generated by changing the learning model used in response to the user's instruction. Furthermore, when separate learning models are prepared for generating generated content related to a specific person performing a specific performance and for generating generated content related to the specific person's talk, the used learning model can be changed in response to the user's instruction to generate generated content in which the specific person performs an action in response to the user's instruction.

[0086] Note that the operation of instructing the content to be generated may be executable during interactive communication with a person related to the generated content (specifically, a virtual image of the person (in other words, a virtual person)). In other words, the generated content may involve interactive communication between the user and the person related to the generated content (for example, communication in a game).

[0087] For example, the generation unit 212 may first generate a first generated content related to a specific person using the learning model and provide it to the user. Then, the generation unit 212 may generate a second generated content (specifically, the second generated content related to the specific person) according to the user's reaction to the first generated content using the learning model and provide it to the user.

[0088] Specifically, for example, the first generated content may be content in which a specific person asks the user what they want to do (e.g., content asking, "What do you want to do?"), as exemplified in FIG. 4. The user may operate a response to the first generated content, such as "I want to talk" or "I want you to sing," and the operation may be accepted as an operation instructing the content of the generated content. Then, based on the user's operation to respond "I want to talk," the generation unit 212 may use a learning model to generate second generated content in which the specific person speaks to the user, and provide the second generated content to the user. Furthermore, based on the user's operation to respond "I want you to sing," the generation unit 212 may use a learning model to generate second generated content in which the specific person sings, and provide the second generated content to the user. In the example shown in FIG. 4, the operation to respond "I want you to sing" (in other words, an operation to instruct singing) includes an operation to instruct singing a specific existing song and an operation to instruct singing a new song. Then, based on the user's operation to instruct singing an existing specific song, the generation unit 212 generates second generated content in which the specific person sings the specific song using the learning model and provides it to the user. Also, based on the user's operation to instruct creating and singing a new song, the generation unit 212 generates a new song that reflects the musicality of the specific person using the learning model as second generated content and provides it to the user.

[0089] Alternatively, for example, the first generated content may be content in which a specific person speaks to the user. Furthermore, for example, the second generated content may be content in which the specific person speaks lines in response to the user's response (in other words, lines) to the first generated content. That is, the generation unit 212 may generate lines of the specific person based on lines of the user input by the user and provide them to the user. Note that the lines of the user may be lines that are presented as in a dating simulation game, from which the user can select a plurality of candidates, and the user may be able to select from the presented candidates, or lines that the user may think up and input at will.

[0090] Alternatively, for example, the first generated content may be content in which a specific person performs a specific performance. Furthermore, for example, the second generated content may reflect the user's reaction during the specific performance. Specifically, if the first generated content is content in which a specific person sings, the generation unit 212 may use a learning model to generate generated content in which the specific person responds to a user's operation (in other words, calling the name or waving the live goods, etc.) based on the user's operation of calling the name of the specific person or waving a live goods (e.g., a penlight, towel, fan, etc.), and provide the generated content to the user as the second generated content. Note that the operation of waving the live goods may be an operation of waving the live goods as a virtual object in a virtual space (in other words, within a game), or may be an operation of waving the live goods in real space (e.g., an operation detected via a camera of the terminal device 10, etc.).

[0091] The user's reaction (in other words, an operation related to the reaction) may be an operation performed by the user using his / her hands, such as an operation on the touch screen 16 (for example, a selection from options displayed on the display unit 18, or an input of arbitrary text), or may be an operation performed by the user's gesture (in other words, an operation detected via a sensor capable of detecting the user's movements, such as an image sensor or a motion sensor), or may be an operation performed by the user's voice (in other words, an operation of inputting voice via a microphone or the like provided on the terminal device 10).

[0092] According to this example, it is possible to provide a service (in other words, a game) that allows users to enjoy content related to a specific celebrity that is generated by a generation AI. It is also possible to provide a service that allows users to select a specific celebrity from multiple celebrities and enjoy content related to the selected celebrity that is generated by a generation AI. It is also possible to provide a service that allows users to have celebrities perform actions that they desire. It is also possible to provide a service that allows users to engage in simulated communication with celebrities.

[0093] Even when the learning model generates generated content related to the same behavior, the generated generated content may change depending on the time. For example, when the learning model generates content in which a specific singer sings a specific song by that singer, the intonation may change depending on the time, and in some cases the singer may make a mistake, such as mispronouncing the lyrics. This makes it possible to provide users with a different kind of enjoyment than when enjoying pre-recorded audio or video. In other words, it is possible to provide users with a unique experience that is different for each user.

[0094] (Example 2) Next, another specific example of a service using the configuration according to this embodiment will be described.

[0095] The learning unit 211 generates a learning model capable of generating generated content that reflects the characteristics of a specific artist by learning using training content related to the specific artist. Specifically, the learning unit 211 generates a learning model capable of generating generated content that reflects the characteristics of each of the first to nth artists (n is a natural number equal to or greater than 2) by learning using training content related to each of the first to nth artists. The training content related to each of the first to nth artists is used for learning with permission from the respective rights holders.

[0096] The training content for each artist includes works by that artist. By using works by a specific artist as training content, a training model can be generated that can generate content that reflects the style of the specific artist (in other words, that imitates the style of the specific artist). For example, by using pictures by a specific artist as training content, a training model can be generated that can generate pictures that reflect the style of the specific artist. Furthermore, by using songs by a specific artist as training content, a training model can be generated that can generate songs that reflect the musicality of the specific artist. Furthermore, by using manga by a specific artist as training content, a training model can be generated that can generate manga that reflects the style of the specific artist. Specifically, it is possible to generate a training model that can generate a sequel to a specific manga when a specific manga artist is no longer able to continue the work, or a training model that can generate a different ending when a user dislikes the ending of a specific manga artist's work.

[0097] The generation unit 212 uses a learning model that has been trained to be able to generate generated content that reflects the characteristics of a specific artist, to generate generated content that reflects the characteristics of the specific artist, and provides the generated content to the user. Specifically, the generation unit 212 uses a learning model that is able to generate generated content that reflects the characteristics of each artist for each of the first to nth artists, to generate generated content that reflects the characteristics of each artist, and provides the generated content to the user.

[0098] That is, for example, the generation unit 212 generates generated content that reflects the style of the first artist, using a learning model that has been trained using the work of the first artist.

[0099] Furthermore, for example, the generation unit 212 generates generated content that reflects the style of the second artist, using a learning model that has been trained using the work of the second artist.

[0100] When the generating unit 212 provides the generated content, the generated content is output in the terminal device 10 of the user.

[0101] That is, on the user's terminal device 10, it is possible to enjoy generated content that reflects the style of each artist, for each of the first to nth artists.

[0102] The type of generated content may be varied depending on the characteristics of the person associated with the generated content. For example, if the first artist is an artist, the generation unit 212 may use a learning model to generate, as the generated content, a picture (in other words, an image) that reflects the style of the first artist. In other words, the learning unit 211 may train the learning model that generates the generated content related to the first artist as a learning model that generates a picture that reflects the style of the first artist (for example, using a picture drawn by the first artist). Furthermore, for example, if the second artist is a composer, the generation unit 212 may use the learning model to generate, as the generated content, a song that reflects the style of the second artist. In other words, the learning unit 211 may train the learning model that generates the generated content related to the second artist as a learning model that generates a song that reflects the musicality of the second artist (for example, using a song by the second artist).

[0103] The generation unit 212 may determine which person's generated content to generate and provide based on a user operation. Then, the generation unit 212 may generate the generated content related to the determined person using a learning model and provide it to the user. The user's operation for determining the person may be the same as that exemplified in Specific Example 1, for example.

[0104] Furthermore, the generation unit 212 may determine the content of generated content related to a specific person based on a user operation. Then, the generation unit 212 may generate generated content of the determined content using a learning model and provide it to the user. The user operation for determining the content may be the same as that exemplified in specific example 2, for example.

[0105] In this example, the operation of instructing the content of the generated content related to a specific person may be an operation of instructing the image of the content to be generated, an operation of instructing the theme of the content to be generated, an operation of instructing the characteristics of the content to be generated, or the like. Specifically, the operation instructs the image (in other words, the theme or characteristics) of the generated content to be generated, such as "cool" or "cute," and the generation unit 212 may generate content according to the instructed image using a learning model. In other words, the generation unit 212 may instruct the learning model to generate content according to the instructed image (in other words, input information according to the instructed image into the learning model), and generate the generated content.

[0106] Furthermore, an operation to instruct the content of generated content related to a specific person may instruct a time at which content of the specific person should be generated. In other words, the generation unit 212 may determine a time at which content of the specific person should be generated based on a user operation. Specifically, for example, when generating generated content that reflects the style of a specific person, the operation may instruct an age at which content that reflects the style of the specific person should be generated. For example, when generating generated content that reflects the style of a specific person, the operation may instruct a style of a work that reflects the style of the work to be generated. In other words, by instructing which of multiple works created by the specific person the style of the work to be reflected in the content of the specific person to be generated, generated content that reflects the characteristics (in other words, the style) of the specific person related to the specified work may be generated.

[0107] This example makes it possible to provide a service (in other words, a game) that allows users to enjoy content related to a specific artist and generated by a generation AI. It also makes it possible to provide a service that allows users to select a specific artist from multiple artists and enjoy content related to the selected artist and generated by a generation AI.

[0108] (Trigger for generating generated content) The trigger for generating generated content is not particularly limited, but a specific example is shown below.

[0109] For example, in the above-mentioned specific examples 1 and 2, a user may be able to have the generation unit 212 generate generated content at any timing. That is, for example, the service (in other words, a game) according to specific example 1 may be a service that allows a user to have the generation AI generate content related to a specific entertainer at any timing of their choice, so that the user can enjoy it. Specifically, a service may be provided that allows a user to have the generation AI generate generated content in which a specific entertainer sings a song at any timing when the user wants to hear the song of the specific entertainer, so that the user can listen to the content.

[0110] Furthermore, in the above-described specific examples 1 and 2, the generation unit 212 may generate generated content based on the user satisfying a predetermined condition in the game. In other words, the generation unit 212 may generate generated content based on the user satisfying a predetermined condition related to the progress of the game. For example, the generation unit 212 may generate generated content based on the user completing a predetermined event (e.g., a so-called quest) in the game or achieving a specific result in a predetermined event (e.g., a so-called mini-game). In other words, the generation unit 212 may generate generated content based on the user satisfying a predetermined condition in the game and grant the generated generated content to the user as a reward. Here, when granting generated content as a reward, the generated content itself may be granted as the reward, or game media or the like using the generated content may be granted as the reward. That is, for example, a video or the like may be granted to the user as generated content. Alternatively, an image of a predetermined game media (e.g., a character, an item, or the like) may be generated as generated content based on the user satisfying a predetermined condition in the game, and the game media of the image may be granted to the user. For example, the control unit 210 progresses the game based on the user's operation and determines whether the user has satisfied a predetermined condition for the granting of a reward. Then, based on the user's satisfaction of the predetermined condition, the control unit 210 may cause the learning model to generate generated content and grant it to the user as a reward. Note that the granting of generated content is not limited to granting it as a reward. For example, the generation unit 212 may generate generated content and grant it to the user based on the user's payment of a fee for the generation of generated content in a purchase event at an in-game shop as a predetermined event (for example, based on an operation to purchase the generated content or an operation to purchase game media using the generated content).

[0111] In other words, the control unit 210 may generate an image of a predetermined game medium based on the user's satisfaction of a predetermined condition for the grant of the predetermined game medium, and grant the predetermined game medium using the generated image. That is, for example, the configuration according to this embodiment may be applied to a game in which a user can acquire game medium (e.g., a character used by the user, equipment for the character, etc.) whose appearance is determined using a generation AI and which the user can use in the game. Furthermore, when a user satisfies a predetermined condition in the game, the appearance of the game medium to be granted to the user may be determined using a generation AI, and the game medium with the determined appearance may be granted to the user. Furthermore, by allowing the user to select an artist's style to reflect in the appearance (i.e., an image) of the granted game medium, it may be possible to acquire game medium that uses the style of a user's favorite artist.

[0112] (Example 3) Regarding the generation of generated content using the learning model of this embodiment, the operator that generates the generated content (in other words, the operator that manages the learning model) and the operator that operates the service provided by the application used by the user (for example, the game played by the user) can be different operators.

[0113] For example, a first business operator, with the permission of the rights holder, trains a learning model using training content to generate a trained learning model, and then, based on a contract with a second business operator, the first business operator permits the second business operator to use the trained learning model.

[0114] 5, the functions of the above-mentioned server 20 can be divided into a server of the first business operator and a server of the second business operator. For example, it is conceivable that the server managed by the first business operator (hereinafter referred to as the "first business operator server 20") is the above-mentioned server 20, and a server that controls the applications used by users (for example, the function of providing games) among the functions of the above-mentioned server 20 is provided separately as a server managed by the second business operator (hereinafter referred to as the "second business operator server 30").

[0115] The hardware configuration of the second operator server 30 can be the same as that of the above-described server 20. The second operator server 30 functions as a control unit 310 and a storage unit 320 through cooperation of a processor, memory, storage, communication IF, input / output IF, etc. The control unit 310 and the storage unit 320 can be configured to perform at least some of the functions of the above-described control unit 210 and storage unit 220.

[0116] The control unit 310 of the second business operator server 30 provides a predetermined service (for example, a game) to the user via the terminal device 10. That is, for example, under the control of the control unit 310, a person selection screen 500 and a content selection screen 520 exemplified in Fig. 4 and Fig. 5 are displayed on the user's terminal device 10. Also, under the control of the control unit 310, for example, the game progresses based on the user's operation on the terminal device 10.

[0117] The control unit 310 decides to generate generated content based on a user's operation related to the predetermined service. For example, the control unit 310 may decide to generate generated content based on the user satisfying a predetermined condition in a game. When the control unit 310 decides to generate generated content, it requests the first business operator server 20 to generate the generated content. The generation unit 212 of the first business operator server 20 generates the generated content based on the request and transmits it to the second business operator server 30. The second business operator server 30 provides the transmitted generated content to the user. In other words, the generation unit 212 provides the generated generated content to the user via the second business operator server 30.

[0118] Here, for example, the storage unit 220 of the first business operator server 20 stores information about a second business operator that has signed a contract with the first business operator for the use of the learning model. The control unit 210 of the first business operator server 20 checks whether or not a contract exists based on the information. If a request for the generation of generated content is received from the second business operator server 30 of the contracted second business operator, the control unit 210 generates the generated content based on the request. This allows the second business operator to have the first business operator generate the generated content and use it for its own services. While the use of the learning model (in other words, the contract) may require the second business operator to pay a predetermined fee to the first business operator, the payment method and the form of the contract are not particularly limited. For example, the second business operator may pay the first business operator a fee depending on the number of times the learning model is used (e.g., each time generated content is generated), or the contract between the second business operator and the first business operator may be in the form of a subscription or the like.

[0119] An example of service provision by a second business operator is shown below. For example, the second business operator may be the operator of a game played by a user. An application (i.e., software) for the game is installed on the terminal device 10, and the user plays the game via the application. The control unit 310 of the second business operator server 30 controls the progress of the game based on the user's operation. The control unit 310 also requests the first business operator server 20 to generate generated content based on the user's satisfaction of a predetermined condition in the game. The generation unit 212 of the first business operator server 20 generates generated content based on the request and transmits it to the second business operator server 30. The control unit 310 of the second business operator server 30 provides the transmitted generated content to the user in the game. For example, the control unit 310 displays an image as the generated content on the game play screen (i.e., the display unit 18) displayed on the terminal device 10, or outputs sound as the generated content to the sound output unit of the terminal device 10 during the game. In other words, the control unit 310 provides the user with a game using the generated generated content. For example, the control unit 310 may provide the user with game media that uses an image as generated content.

[0120] Here, the number of second providers is not limited to one, and multiple second providers may exist. In other words, as illustrated in FIG. 5, multiple servers associated with different second providers may exist as the second provider server 30. For example, assume that second provider A, one of the multiple second providers, operates a first game, and second provider B, another of the multiple second providers, operates a second game. In this case, the control unit 310 of the server 30 of second provider A may request the first provider server 20 to generate content related to the first game based on a user's fulfillment of a predetermined condition in the first game. The control unit 310 of the server 30 of second provider A may then use the generated content in the first game. Furthermore, the control unit 310 of the server 30 of second provider B may request the first provider server 20 to generate content related to the second game based on a user's fulfillment of a predetermined condition in the second game. The control unit 310 of the server 30 of second provider B may then use the generated content in the second game.

[0121] With this configuration, a second business providing a service to a user can use a generation AI to generate content related to content to which the rights holder holds the rights, and use the content within the service, without having to obtain permission from the rights holder itself.

[0122] (Processing related to content generation) Next, an example of processing related to content generation will be described with reference to FIG.

[0123] First, the learning unit 211 trains the learning model using learning content related to a specific person and licensed by the rights holder (step S101). This generates a learning model capable of generating generated content that reflects the characteristics of the specific person. The trained learning model is stored in the storage unit 220.

[0124] The control unit 110 of the terminal device 10 accepts a predetermined operation by a user who is using a predetermined service (for example, playing a game) (step S102). The predetermined operation may be an operation related to the progress of the game. Specifically, the predetermined operation may be an operation to select for which person generated content is to be generated on the person selection screen 500, or an operation to instruct the content of the generated content to be generated on the content selection screen 520. The predetermined operation may also be an operation to progress a quest (in other words, a mission) in the game, or a purchase operation in a shop in the game.

[0125] The generation unit 212 generates generated content that reflects the characteristics of a specific person, based on a predetermined operation by a user who is using a predetermined service, using the learning model that has been trained in step S101 (step S103).

[0126] Next, the generating unit 212 provides the generated content to the user. Specifically, the generating unit 212 transmits the generated content to the terminal device 10 of the user (step S104).

[0127] Next, the control unit 110 of the terminal device 10 outputs the received generated content (step S105).

[0128] According to the configuration of this embodiment, it is possible to connect a rights holder who has rights to content related to a specific person but is unable to monetize it, and a user who is interested in the content related to the specific person but whose enjoyment of the content related to the specific person is restricted due to the existence of the rights holder, so that both parties benefit. Note that it is assumed that the specific person and the user are different persons, but this does not prevent them from being the same person.

[0129] The present invention is not limited to the above-described embodiments and can be modified and implemented in various ways without departing from the spirit and scope of the present invention. Furthermore, the technology described herein may be applied to services other than games (in other words, applications). Furthermore, the technology described herein may also be used in the development process of services such as game production. Within the scope of the present invention, the components may be freely combined, any component may be modified, any component may be replaced, any component may be omitted, or other components may be added. Furthermore, the process flow described herein is merely an example, and the order and configuration of each process may be different. Furthermore, some processes described herein may not exist. In other words, the process flow, specific determination processes, etc. may differ from those exemplified herein.

[0130] <Additional Notes> The configuration of this embodiment may be used to broaden the scope of use of generative AI. Furthermore, the configuration of this embodiment may be used to improve the interest of services. Furthermore, the configuration of this embodiment may be used to prepare content and provide services in a non-traditional manner. The matters described in the above embodiments may also be written as follows:

[0131] (Appendix 1) A learning step in which a processor (e.g., processor 21) causes a learning model to learn using content related to a specific person and licensed by a rights holder; A providing step in which a processor (for example, processor 21) causes the trained learning model to generate content that reflects the characteristics of the specific person, and provides the generated content to a user who uses a predetermined service. How content is provided. This configuration allows users to legally and safely enjoy content generated by a learning model that reflects the characteristics of a specific person. Furthermore, rights holders of content related to a specific person can authorize the use of the content and then have the learning model generate content related to their rights and deliver it to users, allowing rights holders to earn revenue based on their rights. This configuration broadens the scope of use of generative AI.

[0132] (Appendix 2) the content related to the specific person used for learning in the learning step is content in which the specific person is performing a specific performance, The content that reflects the characteristics of the specific person and that is generated by the trained learning model in the providing step is content in which the specific person performs the specific performance. 1. A content provision method as set forth in Appendix 1. With this configuration, it is possible to simulate the performance of a specific person with the permission of the rights holder and provide it to the user.

[0133] (Appendix 3) The content related to the specific person used for learning in the learning step is content related to a talk of the specific person, The content that reflects the characteristics of the specific person and that is generated by the learned learning model in the providing step is content in which the specific person talks. 1. A content provision method as set forth in Appendix 1. With this configuration, it is possible to simulate a conversation between a specific person with the permission of the rights holder and provide it to the user.

[0134] (Appendix 4) the content related to the specific person used for learning in the learning step is a work by the specific person; The content that reflects the characteristics of the specific person that is generated by the trained learning model in the providing step is content that reflects the style of the specific person. 1. A content provision method as set forth in Appendix 1. With this configuration, it becomes possible to generate content that reflects the style of a particular person under the permission of the rights holder and provide it to users.

[0135] (Appendix 5) In the providing step, the processor determines, based on an operation by the user, which of a plurality of people to generate generated content for, and causes the learning model to generate content that reflects the characteristics of the determined person, and provides the content to the user. A content providing method according to any one of appendices 1 to 4. According to this configuration, the user can select which person's generated content he or she wishes to receive, thereby increasing the interest of the service.

[0136] (Appendix 6) In the providing step, the processor determines the content that reflects the characteristics of the specific person to be generated by the learning model based on an operation by the user, causes the learning model to generate the determined content that reflects the characteristics of the specific person, and provides the content to the user. A content providing method according to any one of appendices 1 to 4. With this configuration, the user can specify the content that will be generated by the learning model and provided to them, reflecting the characteristics of a specific person, thereby increasing the interest of the service.

[0137] (Appendix 7) In the providing step, the processor causes the learning model to generate content that reflects the characteristics of the specific person based on the user satisfying a predetermined condition in a predetermined game as the predetermined service, and provides the content to the user. A content providing method according to any one of appendices 1 to 4. With this configuration, it is possible to provide a user playing a specified game with content that reflects the characteristics of a specific person based on the user fulfilling certain conditions within the game, and that is generated by a learning model, thereby increasing the interest of the game.

[0138] (Appendix 8) the content related to the specific person used for learning in the learning step is a work by the specific person; In the providing step, the processor causes the learning model to generate content that reflects the style of the specific person based on the user satisfying a predetermined condition in a predetermined game as the predetermined service, and provides the user with game media using the generated content. 1. A content provision method as set forth in Appendix 1. With this configuration, a user playing a specific game can be given game media that uses content that reflects the style of a specific person based on the user fulfilling certain conditions within the game, thereby increasing the interest of the game.

[0139] (Appendix 9) a storage means for storing a learning model that has been trained using content related to a specific person and licensed by a rights holder; and a providing means for causing the trained learning model to generate content that reflects the characteristics of the specific person, and for providing the generated content to a user who uses a predetermined service. Information processing system. According to this configuration, the same effects as those of the method described in Supplementary Note 1 can be achieved.

[0140] It should be noted that each configuration (in other words, the means for solving the problem) described in the appendix can be appropriately diverted to an apparatus, system, program, method, medium, etc. [Explanation of symbols]

[0141] 1 Information processing system, 10 Terminal device, 11 Processor, 12 Memory, 13 Storage, 14 Communication IF, 15 Input / output IF, 17 Input unit, 18 Display unit, 20 Server, 21 Processor, 22 Memory, 23 Storage, 24 Communication IF, 25 Input / output IF, 110 Control unit, 111 Operation acceptance unit, 113 Game control unit, 114 Display control unit, 120 Memory unit, 210 Control unit, 211 Learning unit, 212 Generation unit, 220 Memory unit

Claims

1. a learning step in which the processor trains the learning model using content related to a specific person and licensed by a rights holder; a providing step in which a processor causes the trained learning model to generate content that reflects the characteristics of the specific person, and provides the generated content to a user who uses a predetermined service; In the providing step, the processor determines, based on the user's operation, when content that reflects the characteristics of the specific person should be generated by the learning model, and generates content according to this determination by the learning model and provides it to the user. How content is provided.

2. the content related to the specific person used for learning in the learning step is content in which the specific person is performing a specific performance, The content that reflects the characteristics of the specific person and that is generated by the trained learning model in the providing step is content in which the specific person performs the specific performance. The content providing method according to claim 1 .

3. The content related to the specific person used for learning in the learning step is content related to a talk of the specific person, The content that reflects the characteristics of the specific person and that is generated by the learned learning model in the providing step is content in which the specific person talks. The content providing method according to claim 1 .

4. the content related to the specific person used for learning in the learning step is a work by the specific person; The content that reflects the characteristics of the specific person that is generated by the trained learning model in the providing step is content that reflects the style of the specific person. The content providing method according to claim 1 .

5. In the providing step, the processor determines, based on an operation by the user, which of a plurality of people to generate generated content for, and causes the learning model to generate content that reflects the characteristics of the determined person, and provides the content to the user. The content providing method according to any one of claims 1 to 4.

6. In the providing step, the processor causes the learning model to generate content that reflects the characteristics of the specific person based on the user satisfying a predetermined condition in a predetermined game as the predetermined service, and provides the content to the user. The content providing method according to any one of claims 1 to 4.

7. the content related to the specific person used for learning in the learning step is a work by the specific person; In the providing step, the processor causes the learning model to generate content that reflects the style of the specific person based on the user satisfying a predetermined condition in a predetermined game as the predetermined service, and provides the user with game media using the generated content. The content providing method according to claim 1 .

8. a storage means for storing a learning model that has been trained using content related to a specific person and licensed by a rights holder; a provision means for causing the trained learning model to generate content that reflects the characteristics of the specific person, and providing the generated content to a user who uses a predetermined service; The providing means determines, based on an operation by the user, when content that reflects the characteristics of the specific person should be generated by the learning model, and generates content according to this determination by the learning model and provides the content to the user. Information processing system.

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