system

Abstract

The system according to the embodiment aims to efficiently select, evaluate, and recommend games created by a generation AI. [Solution] A system according to an embodiment includes a display unit, a play unit, an evaluation unit, an addition unit, and a recommendation unit. The display unit displays games created by the generation AI while scrolling through them. The play unit selects and plays a game displayed by the display unit. The evaluation unit evaluates the game played by the play unit. The addition unit adds the game evaluated by the evaluation unit to a favorites list. The recommendation unit recommends new games based on the games added by the addition unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately address the process of efficiently selecting, evaluating, and recommending games created by generative AI, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently select, evaluate, and recommend games created by a generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a display unit, a play unit, an evaluation unit, an addition unit, and a recommendation unit. The display unit displays games created by the generation AI while scrolling through them. The play unit selects and plays a game displayed by the display unit. The evaluation unit evaluates the game played by the play unit. The addition unit adds the game evaluated by the evaluation unit to a favorites list. The recommendation unit recommends new games based on the games added by the addition unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select, evaluate, and recommend games created by a generation AI. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An app according to an embodiment of the present invention allows users to easily enjoy indie games created by a generation AI in a short time by scrolling through them. This app displays multiple indie games created by the generation AI while scrolling through them, allowing users to quickly play and rate a game they select, add it to a favorites list, and provides a recommendation function based on user preferences. This allows the app to not only easily enjoy indie games created by the generation AI, but also provides a more fulfilling gaming experience through user-to-user rating sharing and recommendation functions. For example, a user can scroll through multiple indie games created by the generation AI while playing them. The user can quickly play a game and add their favorite game to a favorites list. The user can also share their game ratings with other users using a like function. Furthermore, the app is equipped with a recommendation function that recommends new games based on the user's play history and like trends. This makes it easier for users to find games that suit their preferences.

[0029] A game app according to an embodiment includes a display unit, a play unit, an evaluation unit, an addition unit, and a recommendation unit. The display unit displays games created by the generation AI while scrolling. For example, the display unit displays multiple indie games created by the generation AI while scrolling. The play unit plays a game selected by a user. For example, the play unit plays a game selected by a user in a short period of time. The evaluation unit evaluates a game played by a user. For example, the evaluation unit evaluates a game played by a user by pressing a "like" button. The addition unit adds a game evaluated by the evaluation unit to a favorites list. For example, the addition unit adds a game that the user likes to the favorites list. The recommendation unit recommends a new game based on the game added by the addition unit. For example, the recommendation unit recommends a new game based on the user's play history and tendency to like. As a result, the game app according to an embodiment allows a user to play multiple games created by the generation AI in a short period of time while scrolling through them, and has a favorites list creation function and recommendation function based on the tendency.

[0030] The display unit can scroll through and display multiple indie games created by the generation AI. The display unit, for example, scrolls through and displays multiple indie games created by the generation AI. Indie games include, for example, games created by individuals or small development teams. Specific scrolling methods and criteria include, for example, scrolling speed and display order. This allows the user to play multiple indie games created by the generation AI while scrolling through them.

[0031] The play unit can play a game selected by the user. The play unit, for example, plays the game selected by the user in a short time. Specific time ranges and play methods for short time play include, for example, an upper limit on play time and play modes. This allows the user to play the game in a short time.

[0032] The evaluation unit can evaluate a game played by a user by pressing a "Like" button. The evaluation unit can evaluate a game played by a user by pressing a "Like" button, for example. Specific functions and operation methods of the "Like" button include, for example, the position of the button and the action taken when pressed. This allows the user to evaluate a game played by a user.

[0033] The adding unit can add games that the user likes to the favorites list. For example, the adding unit adds games that the user likes to the favorites list. Specific criteria and selection methods for favorite games include, for example, evaluation scores and number of plays. This allows the user to add games that they like to the favorites list.

[0034] The recommendation unit can recommend new games based on the user's play history and tendency to like. The recommendation unit recommends new games based on, for example, the user's play history and tendency to like. Specific content and storage method of the play history include, for example, play time and number of plays. Specific analysis method and criteria for tendency to like include, for example, frequency of likes and targets of likes. This makes it possible to recommend new games based on the user's play history and tendency to like.

[0035] The display unit can analyze the user's past play history and prioritize displaying appropriate games. The display unit can, for example, analyze the genre of games the user has played in the past and prioritize displaying games of the same genre. The display unit can also prioritize displaying new games by developers of games that the user has previously given high ratings to. The display unit can also prioritize displaying games similar to games that the user has previously played for a long time. Specific details of the past play history and the method of saving it include, for example, play time and number of plays. This makes it possible to display optimal games based on the user's past play history.

[0036] The display unit can filter games based on the user's current interests and concerns. For example, the display unit can display related games based on keywords recently searched by the user. The display unit can also display related games based on the genre of games recently played by the user. The display unit can also display related games based on the characteristics of games recently "liked" by the user. Specific methods and criteria for identifying current interests and concerns include, for example, survey results and behavioral history. This allows games to be filtered based on the user's current interests and concerns.

[0037] The display unit can select an appropriate display format based on the user's device information. For example, if the user is using a smartphone, the display unit can display the game in a vertical scrolling format. If the user is using a tablet, the display unit can also display the game in a horizontal scrolling format. If the user is using a PC, the display unit can also display the game in a grid format. Specific content and acquisition method of the device information include, for example, the device type and OS version. This allows the optimal display format to be selected based on the user's device information.

[0038] The play unit can analyze the user's past play history and suggest an optimal game play method. For example, the play unit can suggest play methods that the user has used successfully in the past. The play unit can also suggest play methods that the user has used unsuccessfully in the past to avoid. The play unit can also preferentially suggest play methods that the user has given high ratings to in the past. Specific details of the past play history and the method of saving it include, for example, play time and number of plays. This makes it possible to suggest an optimal game play method based on the user's past play history.

[0039] The play unit can provide a game tutorial based on the user's current skill level. For example, if the user is a beginner, the play unit can provide a tutorial that explains basic operation methods. If the user is an intermediate player, the play unit can provide a tutorial that explains advanced operation methods. If the user is an advanced player, the play unit can provide a tutorial that explains specialized operation methods. Specific methods and criteria for identifying the current skill level include, for example, play history and score. This makes it possible to provide a tutorial that suits the user's skill level.

[0040] The play unit can select the optimal operation method based on the user's device information. For example, if the user is using a smartphone, the play unit prioritizes touch operation. Also, if the user is using a tablet, the play unit can use both touch operation and pen operation. Also, if the user is using a PC, the play unit can prioritize keyboard and mouse operation. Specific content and acquisition method of device information include, for example, the device type and OS version. This allows the optimal operation method to be selected based on the user's device information.

[0041] The evaluation unit can analyze the user's past rating history and suggest an optimal rating method. For example, the evaluation unit can suggest a rating method for a game that the user has previously rated highly. The evaluation unit can also suggest a rating method to avoid for a game that the user has previously rated poorly. The evaluation unit can also customize the rating method based on feedback provided by the user in the past. Specific content and storage method of the past rating history include, for example, rating scores and rating comments. This makes it possible to suggest an optimal rating method based on the user's past rating history.

[0042] The evaluation unit can filter the ratings based on the user's current interests and concerns. For example, the evaluation unit evaluates games in a genre that the user has recently taken an interest in. The evaluation unit can also evaluate games that the user has recently played. The evaluation unit can also evaluate games that the user has recently "liked." Specific methods and criteria for identifying the user's current interests and concerns include, for example, survey results and behavioral history. This allows the ratings to be filtered based on the user's current interests and concerns.

[0043] The evaluation unit can select the optimal evaluation format based on the user's device information. For example, if the user is using a smartphone, the evaluation unit performs the evaluation by touch operation. Furthermore, if the user is using a tablet, the evaluation unit can also perform the evaluation by touch operation and pen operation. Furthermore, if the user is using a PC, the evaluation unit can also perform the evaluation by keyboard and mouse operation. Specific content and acquisition method of the device information include, for example, the device type and OS version. This makes it possible to select the optimal evaluation format based on the user's device information.

[0044] The adding unit can analyze the user's past addition history and suggest an optimal addition method. For example, the adding unit can analyze the genre of games the user has added in the past and suggest adding games of the same genre. The adding unit can also suggest adding new games from developers of games that the user has previously given high ratings to. The adding unit can also suggest adding games similar to games that the user has previously played for a long time. Specific content and storage methods of the past addition history include, for example, the date and time of addition and the number of additions. This makes it possible to suggest an optimal addition method based on the user's past addition history.

[0045] The adding unit can perform additional filtering based on the user's current interests and concerns. For example, the adding unit can add related games based on keywords recently searched by the user. The adding unit can also add related games based on genres of games recently played by the user. The adding unit can also add related games based on characteristics of games recently "liked" by the user. Specific methods and criteria for identifying current interests and concerns include, for example, survey results and behavioral history. This makes it possible to add games based on the user's current interests and concerns.

[0046] The adding unit can select the optimal adding format based on the user's device information. For example, if the user is using a smartphone, the adding unit can add the game in a vertical scrolling format. If the user is using a tablet, the adding unit can also add the game in a horizontal scrolling format. If the user is using a PC, the adding unit can also add the game in a grid format. Specific content and acquisition method of the device information include, for example, the device type and OS version. This makes it possible to select the optimal adding format based on the user's device information.

[0047] The recommendation unit can analyze the user's past play history and recommend the most suitable game. For example, the recommendation unit can analyze the genre of games the user has played in the past and recommend games of the same genre. The recommendation unit can also recommend new works by developers of games that the user has previously given high ratings to. The recommendation unit can also recommend games similar to games that the user has previously played for a long time. Specific details of the past play history and the method of saving it include, for example, play time and number of plays. This makes it possible to recommend the most suitable game based on the user's past play history.

[0048] The recommendation unit can filter recommendations based on the user's current interests and concerns. For example, the recommendation unit can recommend related games based on keywords recently searched by the user. The recommendation unit can also recommend related games based on the genre of games recently played by the user. The recommendation unit can also recommend related games based on the characteristics of games recently "liked" by the user. Specific methods and criteria for identifying the user's current interests and concerns include, for example, survey results and behavioral history. This makes it possible to recommend games based on the user's current interests and concerns.

[0049] The recommendation unit can select the optimal recommendation format based on the user's device information. For example, if the user is using a smartphone, the recommendation unit can recommend games in a vertical scrolling format. If the user is using a tablet, the recommendation unit can also recommend games in a horizontal scrolling format. If the user is using a PC, the recommendation unit can also recommend games in a grid format. Specific content and acquisition method of the device information include, for example, the device type and OS version. This allows the optimal recommendation format to be selected based on the user's device information.

[0050] The recommendation unit can prioritize recommending highly relevant games based on the user's geographical location information. For example, if the user is in a specific area, the recommendation unit can prioritize recommending games related to that area. Furthermore, if the user is traveling, the recommendation unit can prioritize recommending games related to the travel destination. Furthermore, if the user is participating in a specific event, the recommendation unit can prioritize recommending games related to the event. Specific content and acquisition methods of the geographical location information include, for example, GPS data and IP address. This makes it possible to recommend highly relevant games based on the user's geographical location information.

[0051] The recommendation unit can analyze the user's social media activity and recommend related games. For example, the recommendation unit can recommend games related to posts that the user has "liked" on social media. The recommendation unit can also recommend games recommended by accounts the user follows on social media. The recommendation unit can also recommend games related to content the user has shared on social media. Specific details and analysis methods of social media activity include, for example, the content of posts and the number of likes. This makes it possible to recommend related games based on the user's social media activity.

[0052] The recommendation unit can customize the recommendation method by reflecting the user's past feedback. For example, the recommendation unit can adjust the order of recommended games based on the user's past feedback. The recommendation unit can also adjust the category of recommended games based on the user's past feedback. The recommendation unit can also customize the description and screenshots of recommended games based on the user's past feedback. Specific content and storage method of the past feedback include, for example, the type and content of the feedback. This allows the recommendation method to be customized based on the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The display unit can adjust the theme color of the game based on the user's visual preferences. For example, if the user prefers bright colors, the display unit can change the game background and interface to bright colors. Alternatively, if the user prefers dark colors, the display unit can change the game background and interface to dark colors. Furthermore, if the user prefers a specific color, the display unit can apply a theme based on that color. This allows customization according to the user's visual preferences.

[0055] The play unit can analyze the user's past play style and suggest the optimal game mode. For example, if the user has previously preferred action games, the play unit can suggest an action mode. If the user has previously preferred puzzle games, the play unit can suggest a puzzle mode. Furthermore, if the user prefers cooperative play, the play unit can suggest a multiplayer mode. This makes it possible to provide the optimal game mode according to the user's play style.

[0056] The adding unit can analyze the user's past addition history and suggest the optimal timing for adding games. For example, if the user previously added games during a specific time period, the adding unit can suggest a new game for that time period. Also, if the user previously added games on weekends, the adding unit can suggest a new game for the weekend. Furthermore, if the user previously added games during a specific event period, the adding unit can suggest a new game for that event period. In this way, the optimal timing for adding games can be suggested based on the user's past addition history.

[0057] The display unit can adjust the type of games to display based on the remaining battery power of the user's device. For example, when the remaining battery power is low, the display unit can prioritize games that can be played in a short time. When the remaining battery power is sufficient, the display unit can also display games that can be played for a long time. Furthermore, when the remaining battery power is moderate, the display unit can display games that require a moderate playing time. This makes it possible to display games according to the remaining battery power of the user's device.

[0058] The evaluation unit can analyze the user's past evaluation history and propose optimal evaluation criteria. For example, if the user has preferred detailed evaluations in the past, the evaluation unit can propose detailed evaluation criteria. Also, if the user has preferred concise evaluations, the evaluation unit can propose concise evaluation criteria. Furthermore, if the user has preferred visual evaluations, the evaluation unit can propose graphical evaluation criteria. In this way, optimal evaluation criteria can be proposed based on the user's past evaluation history.

[0059] The recommendation unit can analyze the user's past play history and suggest the optimal game play time. For example, if the user has previously preferred games that can be played in a short time, the recommendation unit can suggest games that can be played in a short time. If the user has previously preferred games that can be played for a long time, the recommendation unit can also suggest games that can be played for a long time. Furthermore, if the user has previously preferred games that can be played for a medium amount of time, the recommendation unit can also suggest games that have a moderate play time. In this way, the optimal play time can be suggested based on the user's past play history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The display unit scrolls and displays the game created by the generation AI. For example, the display unit scrolls and displays a plurality of indie games created by the generation AI. Step 2: The play unit plays the game selected by the user. For example, the play unit plays the game selected by the user for a short period of time. Step 3: The rating unit rates the game played by the user. For example, the rating unit rates the game played by the user by pressing a "Like" button. Step 4: The adding unit adds the games evaluated by the evaluating unit to the favorites list. For example, the adding unit adds games that the user likes to the favorites list. Step 5: The recommendation unit recommends new games based on the games added by the adding unit. For example, the recommendation unit recommends new games based on the user's play history and likes.

[0062] (Example 2) An app according to an embodiment of the present invention allows users to easily enjoy indie games created by a generation AI in a short time by scrolling through them. This app displays multiple indie games created by the generation AI while scrolling through them, allowing users to quickly play and rate a game they select, add it to a favorites list, and provides a recommendation function based on user preferences. This allows the app to not only easily enjoy indie games created by the generation AI, but also provides a more fulfilling gaming experience through user-to-user rating sharing and recommendation functions. For example, a user can scroll through multiple indie games created by the generation AI while playing them. The user can quickly play a game and add their favorite game to a favorites list. The user can also share their game ratings with other users using a like function. Furthermore, the app is equipped with a recommendation function that recommends new games based on the user's play history and like trends. This makes it easier for users to find games that suit their preferences.

[0063] A game app according to an embodiment includes a display unit, a play unit, an evaluation unit, an addition unit, and a recommendation unit. The display unit displays games created by the generation AI while scrolling. For example, the display unit displays multiple indie games created by the generation AI while scrolling. The play unit plays a game selected by a user. For example, the play unit plays a game selected by a user in a short period of time. The evaluation unit evaluates a game played by a user. For example, the evaluation unit evaluates a game played by a user by pressing a "like" button. The addition unit adds a game evaluated by the evaluation unit to a favorites list. For example, the addition unit adds a game that the user likes to the favorites list. The recommendation unit recommends a new game based on the game added by the addition unit. For example, the recommendation unit recommends a new game based on the user's play history and tendency to like. As a result, the game app according to an embodiment allows a user to play multiple games created by the generation AI in a short period of time while scrolling through them, and has a favorites list creation function and recommendation function based on the tendency.

[0064] The display unit can scroll through and display multiple indie games created by the generation AI. The display unit, for example, scrolls through and displays multiple indie games created by the generation AI. Indie games include, for example, games created by individuals or small development teams. Specific scrolling methods and criteria include, for example, scrolling speed and display order. This allows the user to play multiple indie games created by the generation AI while scrolling through them.

[0065] The play unit can play a game selected by the user. The play unit, for example, plays the game selected by the user in a short time. Specific time ranges and play methods for short time play include, for example, an upper limit on play time and play modes. This allows the user to play the game in a short time.

[0066] The evaluation unit can evaluate a game played by a user by pressing a "Like" button. The evaluation unit can evaluate a game played by a user by pressing a "Like" button, for example. Specific functions and operation methods of the "Like" button include, for example, the position of the button and the action taken when pressed. This allows the user to evaluate a game played by a user.

[0067] The adding unit can add games that the user likes to the favorites list. For example, the adding unit adds games that the user likes to the favorites list. Specific criteria and selection methods for favorite games include, for example, evaluation scores and number of plays. This allows the user to add games that they like to the favorites list.

[0068] The recommendation unit can recommend new games based on the user's play history and tendency to like. The recommendation unit recommends new games based on, for example, the user's play history and tendency to like. Specific content and storage method of the play history include, for example, play time and number of plays. Specific analysis method and criteria for tendency to like include, for example, frequency of likes and targets of likes. This makes it possible to recommend new games based on the user's play history and tendency to like.

[0069] The display unit can estimate the user's emotions and adjust the order of games to be displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the display unit can prioritize displaying games that are relaxing. Furthermore, if the user is excited, the display unit can prioritize displaying action games. Furthermore, if the user is tired, the display unit can prioritize displaying games that can be enjoyed in a short amount of time. Specific methods and technologies for estimating emotions include, for example, facial expression recognition and voice analysis. This makes it possible to adjust the order of games to be displayed according to the user's emotions.

[0070] The display unit can analyze the user's past play history and prioritize displaying appropriate games. The display unit can, for example, analyze the genre of games the user has played in the past and prioritize displaying games of the same genre. The display unit can also prioritize displaying new games by developers of games that the user has previously given high ratings to. The display unit can also prioritize displaying games similar to games that the user has previously played for a long time. Specific details of the past play history and the method of saving it include, for example, play time and number of plays. This makes it possible to display optimal games based on the user's past play history.

[0071] The display unit can filter games based on the user's current interests and concerns. For example, the display unit can display related games based on keywords recently searched by the user. The display unit can also display related games based on the genre of games recently played by the user. The display unit can also display related games based on the characteristics of games recently "liked" by the user. Specific methods and criteria for identifying current interests and concerns include, for example, survey results and behavioral history. This allows games to be filtered based on the user's current interests and concerns.

[0072] The display unit can select an appropriate display format based on the user's device information. For example, if the user is using a smartphone, the display unit can display the game in a vertical scrolling format. If the user is using a tablet, the display unit can also display the game in a horizontal scrolling format. If the user is using a PC, the display unit can also display the game in a grid format. Specific content and acquisition method of the device information include, for example, the device type and OS version. This allows the optimal display format to be selected based on the user's device information.

[0073] The play unit can estimate the user's emotions and adjust the difficulty of the game based on the estimated user's emotions. For example, the play unit can lower the difficulty of the game if the user is feeling stressed. Alternatively, the play unit can keep the difficulty of the game the same if the user is relaxed. Alternatively, the play unit can increase the difficulty of the game if the user is excited. Specific methods and technologies for estimating emotions include, for example, facial expression recognition and voice analysis. This allows the difficulty of the game to be adjusted according to the user's emotions.

[0074] The play unit can analyze the user's past play history and suggest an optimal game play method. For example, the play unit can suggest play methods that the user has used successfully in the past. The play unit can also suggest play methods that the user has used unsuccessfully in the past to avoid. The play unit can also preferentially suggest play methods that the user has given high ratings to in the past. Specific details of the past play history and the method of saving it include, for example, play time and number of plays. This makes it possible to suggest an optimal game play method based on the user's past play history.

[0075] The play unit can provide a game tutorial based on the user's current skill level. For example, if the user is a beginner, the play unit can provide a tutorial that explains basic operation methods. If the user is an intermediate player, the play unit can provide a tutorial that explains advanced operation methods. If the user is an advanced player, the play unit can provide a tutorial that explains specialized operation methods. Specific methods and criteria for identifying the current skill level include, for example, play history and score. This makes it possible to provide a tutorial that suits the user's skill level.

[0076] The play unit can select the optimal operation method based on the user's device information. For example, if the user is using a smartphone, the play unit prioritizes touch operation. Also, if the user is using a tablet, the play unit can use both touch operation and pen operation. Also, if the user is using a PC, the play unit can prioritize keyboard and mouse operation. Specific content and acquisition method of device information include, for example, the device type and OS version. This allows the optimal operation method to be selected based on the user's device information.

[0077] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria based on the estimated user's emotion. For example, the evaluation unit can provide detailed evaluation criteria when the user is relaxed. The evaluation unit can also provide concise evaluation criteria when the user is in a hurry. The evaluation unit can also provide visually stimulating evaluation criteria when the user is excited. Specific methods and technologies for estimating emotion include, for example, facial expression recognition and voice analysis. This allows the evaluation criteria to be adjusted according to the user's emotion.

[0078] The evaluation unit can analyze the user's past rating history and suggest an optimal rating method. For example, the evaluation unit can suggest a rating method for a game that the user has previously rated highly. The evaluation unit can also suggest a rating method to avoid for a game that the user has previously rated poorly. The evaluation unit can also customize the rating method based on feedback provided by the user in the past. Specific content and storage method of the past rating history include, for example, rating scores and rating comments. This makes it possible to suggest an optimal rating method based on the user's past rating history.

[0079] The evaluation unit can filter the ratings based on the user's current interests and concerns. For example, the evaluation unit evaluates games in a genre that the user has recently taken an interest in. The evaluation unit can also evaluate games that the user has recently played. The evaluation unit can also evaluate games that the user has recently "liked." Specific methods and criteria for identifying the user's current interests and concerns include, for example, survey results and behavioral history. This allows the ratings to be filtered based on the user's current interests and concerns.

[0080] The evaluation unit can select the optimal evaluation format based on the user's device information. For example, if the user is using a smartphone, the evaluation unit performs the evaluation by touch operation. Furthermore, if the user is using a tablet, the evaluation unit can also perform the evaluation by touch operation and pen operation. Furthermore, if the user is using a PC, the evaluation unit can also perform the evaluation by keyboard and mouse operation. Specific content and acquisition method of the device information include, for example, the device type and OS version. This makes it possible to select the optimal evaluation format based on the user's device information.

[0081] The adding unit can estimate the user's emotions and adjust the order of the favorites list based on the estimated user emotions. For example, if the user is relaxed, the adding unit can display relaxing games at the top of the favorites list. Furthermore, if the user is excited, the adding unit can display action games at the top of the favorites list. Furthermore, if the user is tired, the adding unit can display games that can be enjoyed in a short amount of time at the top of the favorites list. Specific methods and technologies for estimating emotions include, for example, facial expression recognition and voice analysis. This makes it possible to adjust the order of the favorites list according to the user's emotions.

[0082] The adding unit can analyze the user's past addition history and suggest an optimal addition method. For example, the adding unit can analyze the genre of games the user has added in the past and suggest adding games of the same genre. The adding unit can also suggest adding new games from developers of games that the user has previously given high ratings to. The adding unit can also suggest adding games similar to games that the user has previously played for a long time. Specific content and storage methods of the past addition history include, for example, the date and time of addition and the number of additions. This makes it possible to suggest an optimal addition method based on the user's past addition history.

[0083] The adding unit can perform additional filtering based on the user's current interests and concerns. For example, the adding unit can add related games based on keywords recently searched by the user. The adding unit can also add related games based on genres of games recently played by the user. The adding unit can also add related games based on characteristics of games recently "liked" by the user. Specific methods and criteria for identifying current interests and concerns include, for example, survey results and behavioral history. This makes it possible to add games based on the user's current interests and concerns.

[0084] The adding unit can select the optimal adding format based on the user's device information. For example, if the user is using a smartphone, the adding unit can add the game in a vertical scrolling format. If the user is using a tablet, the adding unit can also add the game in a horizontal scrolling format. If the user is using a PC, the adding unit can also add the game in a grid format. Specific content and acquisition method of the device information include, for example, the device type and OS version. This makes it possible to select the optimal adding format based on the user's device information.

[0085] The recommendation unit can estimate the user's emotions and adjust the order of games to be recommended based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can preferentially recommend relaxing games. Furthermore, if the user is excited, the recommendation unit can preferentially recommend action games. Furthermore, if the user is tired, the recommendation unit can preferentially recommend games that can be enjoyed in a short amount of time. Specific methods and technologies for estimating emotions include, for example, facial expression recognition and voice analysis. This makes it possible to adjust the order of games to be recommended according to the user's emotions.

[0086] The recommendation unit can analyze the user's past play history and recommend the most suitable game. For example, the recommendation unit can analyze the genre of games the user has played in the past and recommend games of the same genre. The recommendation unit can also recommend new works by developers of games that the user has previously given high ratings to. The recommendation unit can also recommend games similar to games that the user has previously played for a long time. Specific details of the past play history and the method of saving it include, for example, play time and number of plays. This makes it possible to recommend the most suitable game based on the user's past play history.

[0087] The recommendation unit can filter recommendations based on the user's current interests and concerns. For example, the recommendation unit can recommend related games based on keywords recently searched by the user. The recommendation unit can also recommend related games based on the genre of games recently played by the user. The recommendation unit can also recommend related games based on the characteristics of games recently "liked" by the user. Specific methods and criteria for identifying the user's current interests and concerns include, for example, survey results and behavioral history. This makes it possible to recommend games based on the user's current interests and concerns.

[0088] The recommendation unit can select the optimal recommendation format based on the user's device information. For example, if the user is using a smartphone, the recommendation unit can recommend games in a vertical scrolling format. If the user is using a tablet, the recommendation unit can also recommend games in a horizontal scrolling format. If the user is using a PC, the recommendation unit can also recommend games in a grid format. Specific content and acquisition method of the device information include, for example, the device type and OS version. This allows the optimal recommendation format to be selected based on the user's device information.

[0089] The recommendation unit can estimate the user's emotions and adjust the category of games to be recommended based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can preferentially recommend puzzle games and simulation games. Also, if the user is excited, the recommendation unit can preferentially recommend action games and shooting games. Also, if the user is tired, the recommendation unit can preferentially recommend casual games and mini games. Specific methods and technologies for estimating emotions include, for example, facial expression recognition and voice analysis. This makes it possible to adjust the category of games to be recommended depending on the user's emotions.

[0090] The recommendation unit can prioritize recommending highly relevant games based on the user's geographical location information. For example, if the user is in a specific area, the recommendation unit can prioritize recommending games related to that area. Furthermore, if the user is traveling, the recommendation unit can prioritize recommending games related to the travel destination. Furthermore, if the user is participating in a specific event, the recommendation unit can prioritize recommending games related to the event. Specific content and acquisition methods of the geographical location information include, for example, GPS data and IP address. This makes it possible to recommend highly relevant games based on the user's geographical location information.

[0091] The recommendation unit can analyze the user's social media activity and recommend related games. For example, the recommendation unit can recommend games related to posts that the user has "liked" on social media. The recommendation unit can also recommend games recommended by accounts the user follows on social media. The recommendation unit can also recommend games related to content the user has shared on social media. Specific details and analysis methods of social media activity include, for example, the content of posts and the number of likes. This makes it possible to recommend related games based on the user's social media activity.

[0092] The recommendation unit can customize the recommendation method by reflecting the user's past feedback. For example, the recommendation unit can adjust the order of recommended games based on the user's past feedback. The recommendation unit can also adjust the category of recommended games based on the user's past feedback. The recommendation unit can also customize the description and screenshots of recommended games based on the user's past feedback. Specific content and storage method of the past feedback include, for example, the type and content of the feedback. This allows the recommendation method to be customized based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the display unit, play unit, evaluation unit, adding unit, and recommendation unit, described above, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the display unit is realized by the display 40A of the smart device 14 and scrolls through the games created by the generation AI. The play unit is realized by the control unit 46A of the smart device 14 and plays the game selected by the user in a short period of time. The evaluation unit is realized by the control unit 46A of the smart device 14 and allows the user to rate the game they have played by pressing a "Like" button. The adding unit is realized by the control unit 46A of the smart device 14 and adds the rated game to a favorites list. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends new games based on the user's play history and tendency to like. === Hard Collateral 1-2 === Each of the multiple elements, including the display unit, play unit, evaluation unit, adding unit, and recommendation unit, described above, is realized by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the display unit is realized by the display of the smart glasses 214 and scrolls through the games created by the generation AI. The play unit is realized by the control unit 46A of the smart glasses 214 and plays the game selected by the user in a short period of time. The evaluation unit is realized by the control unit 46A of the smart glasses 214 and allows the user to rate the game they played by pressing a "Like" button. The adding unit is realized by the control unit 46A of the smart glasses 214 and adds the rated game to a favorites list. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends new games based on the user's play history and tendency to like. === Hard Collateral 1-3 === Each of the multiple elements including the above-described display unit, play unit, evaluation unit, adding unit, and recommendation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the display unit is realized by the display 343 of the headset-type terminal 314 and displays the game created by the generation AI while scrolling. The play unit is realized by the control unit 46A of the headset-type terminal 314 and plays the game selected by the user in a short period of time. The evaluation unit is realized by the control unit 46A of the headset-type terminal 314 and allows the user to evaluate the game they played by pressing a "Like" button. The adding unit is realized by the control unit 46A of the headset-type terminal 314 and adds the rated game to a favorites list. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends new games based on the user's play history and tendency to like. === Hard Collateral 1-4 === Each of the multiple elements including the display unit, play unit, evaluation unit, adding unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the display unit is realized by the display of the robot 414 and displays the game created by the generation AI while scrolling. The play unit is realized by the control unit 46A of the robot 414 and plays the game selected by the user in a short period of time. The evaluation unit is realized by the control unit 46A of the robot 414 and allows the user to evaluate the game they played by pressing a "Like" button. The adding unit is realized by the control unit 46A of the robot 414 and adds the evaluated game to a favorites list. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends new games based on the user's play history and tendency to like.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The display unit can adjust the theme color of the game based on the user's visual preferences. For example, if the user prefers bright colors, the display unit can change the game background and interface to bright colors. Alternatively, if the user prefers dark colors, the display unit can change the game background and interface to dark colors. Furthermore, if the user prefers a specific color, the display unit can apply a theme based on that color. This allows customization according to the user's visual preferences.

[0095] The play unit can analyze the user's past play style and suggest the optimal game mode. For example, if the user has previously preferred action games, the play unit can suggest an action mode. If the user has previously preferred puzzle games, the play unit can suggest a puzzle mode. Furthermore, if the user prefers cooperative play, the play unit can suggest a multiplayer mode. This makes it possible to provide the optimal game mode according to the user's play style.

[0096] The evaluation unit can estimate the user's emotions and customize the evaluation feedback based on the estimated user's emotions. For example, if the user has positive emotions, the evaluation unit can provide detailed feedback. If the user has negative emotions, the evaluation unit can provide brief feedback. Furthermore, if the user has neutral emotions, the evaluation unit can provide standard feedback. In this way, evaluation feedback according to the user's emotions can be provided.

[0097] The adding unit can analyze the user's past addition history and suggest the optimal timing for adding games. For example, if the user previously added games during a specific time period, the adding unit can suggest a new game for that time period. Also, if the user previously added games on weekends, the adding unit can suggest a new game for the weekend. Furthermore, if the user previously added games during a specific event period, the adding unit can suggest a new game for that event period. In this way, the optimal timing for adding games can be suggested based on the user's past addition history.

[0098] The recommendation unit can estimate the user's emotions and adjust the description of the recommended game based on the estimated user's emotions. For example, if the user is relaxed, the description can be concise and relaxed in tone. If the user is excited, the description can be made more exciting. Furthermore, if the user is tired, the description can be shortened to make it easier to read. This makes it possible to provide a description that matches the user's emotions.

[0099] The display unit can adjust the type of games to display based on the remaining battery power of the user's device. For example, when the remaining battery power is low, the display unit can prioritize games that can be played in a short time. When the remaining battery power is sufficient, the display unit can also display games that can be played for a long time. Furthermore, when the remaining battery power is moderate, the display unit can display games that require a moderate playing time. This makes it possible to display games according to the remaining battery power of the user's device.

[0100] The play unit can estimate the user's emotions and adjust the in-game music and sound effects based on the estimated user's emotions. For example, if the user is relaxed, it can play soft music. If the user is excited, it can play fast-paced music. Furthermore, if the user is tired, it can lower the volume. This makes it possible to adjust the music and sound effects according to the user's emotions.

[0101] The evaluation unit can analyze the user's past evaluation history and propose optimal evaluation criteria. For example, if the user has preferred detailed evaluations in the past, the evaluation unit can propose detailed evaluation criteria. Also, if the user has preferred concise evaluations, the evaluation unit can propose concise evaluation criteria. Furthermore, if the user has preferred visual evaluations, the evaluation unit can propose graphical evaluation criteria. In this way, optimal evaluation criteria can be proposed based on the user's past evaluation history.

[0102] The adding unit can estimate the user's emotion and adjust the display format of the favorites list based on the estimated user's emotion. For example, if the user is relaxed, the favorites list can be displayed in a list format. If the user is excited, the favorites list can be displayed in a grid format. If the user is tired, the favorites list can be displayed in a simple list format. This makes it possible to provide a favorites list display format that corresponds to the user's emotion.

[0103] The recommendation unit can analyze the user's past play history and suggest the optimal game play time. For example, if the user has previously preferred games that can be played in a short time, the recommendation unit can suggest games that can be played in a short time. If the user has previously preferred games that can be played for a long time, the recommendation unit can also suggest games that can be played for a long time. Furthermore, if the user has previously preferred games that can be played for a medium amount of time, the recommendation unit can also suggest games that have a moderate play time. In this way, the optimal play time can be suggested based on the user's past play history.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The display unit scrolls and displays the game created by the generation AI. For example, the display unit scrolls and displays a plurality of indie games created by the generation AI. Step 2: The play unit plays the game selected by the user. For example, the play unit plays the game selected by the user for a short period of time. Step 3: The rating unit rates the game played by the user. For example, the rating unit rates the game played by the user by pressing a "Like" button. Step 4: The adding unit adds the games evaluated by the evaluating unit to the favorites list. For example, the adding unit adds games that the user likes to the favorites list. Step 5: The recommendation unit recommends new games based on the games added by the adding unit. For example, the recommendation unit recommends new games based on the user's play history and likes.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a display unit that scrolls and displays the game created by the generation AI; a play unit for selecting and playing a game displayed by the display unit; an evaluation unit that evaluates the game played by the playing unit; an adding unit that adds the game evaluated by the evaluation unit to a favorites list; a recommendation unit that recommends new games based on the games added by the adding unit.

2. The display unit Scroll through multiple indie games created by the AI 2. The system of claim 1.

3. The play section includes: Play the game selected by the user 2. The system of claim 1.

4. The adding unit Adding games that users like to a favorites list 2. The system of claim 1.

5. The recommendation unit Recommend new games based on your play history and likes 2. The system of claim 1.

6. The display unit Estimate the user's emotions and adjust the order of games to be displayed based on the estimated user emotions.

2. The system of claim 1.

7. The display unit Analyze the user's past playing history and prioritize the most appropriate games.

2. The system of claim 1.

8. The display unit Filtering games based on the user's current interests 2. The system of claim 1.

9. The display unit Select the appropriate display format based on the user's device information 2. The system of claim 1.

Images