System

Abstract

An object of a system according to an embodiment is to provide support for efficiently clearing a game that has not been cleared by a player.SOLUTION: A system according to an embodiment includes a game information acquisition unit, a strategy analysis unit, an advice providing unit, an assist operation unit, and an auto play unit. The game information acquisition unit acquires information on a game that the player wishes to clear. The strategy analysis section analyzes a strategy method based on the game information acquired by the game information acquisition section. The advice providing unit provides advice based on the strategy method analyzed by the strategy analysis unit. The assist operation unit assists an operation of a player. The auto-play unit automatically plays a game.SELECTED DRAWING: Figure 1

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 provide sufficient support for players to efficiently complete games that they have not yet completed, and there is room for improvement.

[0005] The system according to the embodiment aims to provide support for players to efficiently complete games that they have not yet completed. [Means for solving the problem]

[0006] The system according to the embodiment comprises a game information acquisition unit, a strategy analysis unit, an advice providing unit, an assist operation unit, and an autoplay unit. The game information acquisition unit acquires information about the game that the player wants to complete. The strategy analysis unit analyzes a strategy based on the game information acquired by the game information acquisition unit. The advice providing unit provides advice based on the strategy analyzed by the strategy analysis unit. The assist operation unit assists the player's operations. The autoplay unit plays the game automatically. [Effects of the Invention]

[0007] The system according to the embodiment can provide support for players to efficiently complete games that they have not yet completed. [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) A game support system according to an embodiment of the present invention uses a generation AI to provide complete support to players who have purchased but not played, or played but not completed, many games, all the way to completion. This allows the game support system to provide support for game completion, strategy advice, assistive operations, and even fully automatic play, according to the player's wishes.

[0029] A game support system according to an embodiment includes a game information acquisition unit, a strategy analysis unit, an advice providing unit, an assist operation unit, and an autoplay unit. The game information acquisition unit acquires information about a game that a player wants to complete. For example, the game information acquisition unit acquires the title and progress of the game entered by the player. The game information acquisition unit can also acquire related information from a game database. The strategy analysis unit analyzes a strategy based on the game information acquired by the game information acquisition unit. For example, the strategy analysis unit analyzes a strategy for a game using a generation AI. The strategy analysis unit can also suggest an optimal strategy based on the progress of the game. The advice providing unit provides advice based on the strategy analyzed by the strategy analysis unit. For example, the advice providing unit provides advice to the player using a text message or audio guide using a generation AI. The advice providing unit can also provide a video tutorial. The assist operation unit assists the player's operation. For example, the assist operation unit supports the player's operation using a generation AI. The assist operation unit can also automate certain operations. The autoplay unit automatically plays the game. For example, the autoplay unit can use a generation AI to play the game completely automatically. The autoplay unit can also autoplay only specific stages at the player's request. This allows the game support system according to the embodiment to provide support for clearing the game at the player's request.

[0030] The strategy analysis unit can analyze a player's past play data and propose strategies optimized for the player's play style. For example, the generation AI collects the player's past play data and analyzes the player's operation patterns and strategies. For example, in an RPG game, the strategy analysis unit identifies the player's preferred play style based on the player's past play data and proposes strategies based on that. Furthermore, the strategy analysis unit can identify a player's preferred play style based on the player's past play data and propose strategies tailored to that style. For example, in an action game, the generation AI can propose strategies that utilize the player's preferred attack patterns. Furthermore, the generation AI can analyze a player's past play data, identify the player's weaknesses, and propose strategies that focus on supporting those weaknesses. For example, in a shooting game, the generation AI can propose countermeasures for enemy placements and attack patterns that the player finds difficult. This allows the provision of strategies optimized for the player's play style.

[0031] The strategy analysis unit can optimize the timing of using in-game items or skills, supporting efficient game completion. For example, the strategy analysis unit allows the generation AI to analyze the effects of all in-game items and skills and suggest the optimal timing for their use. For example, in an RPG game, the strategy analysis unit optimizes the timing of using recovery items and strengthening skills during boss battles. The strategy analysis unit also allows the generation AI to adjust the timing of item and skill use in real time according to the player's progress. For example, in an action game, the strategy analysis unit suggests the use of defensive skills and evasive items to match the enemy's attack patterns. The strategy analysis unit also allows the generation AI to learn the effects of all in-game items and skills and suggest the optimal timing for their use to suit the player's play style. For example, in a shooting game, the strategy analysis unit suggests the use of items that are effective against specific enemies or stages. This supports efficient game completion.

[0032] The strategy analysis unit can collect strategy data from other players and propose the strategy with the highest success rate. For example, the generation AI collects strategy data from other players and analyzes the strategy with the highest success rate. For example, in an RPG game, the strategy analysis unit proposes effective skill and item combinations used by other players. The strategy analysis unit also proposes the strategy with the highest success rate based on the strategy data from other players. For example, in an action game, the strategy analysis unit refers to the strategies for stages that other players have cleared. The strategy analysis unit also proposes the optimal strategy based on the player's progress. For example, in a shooting game, the strategy analysis unit proposes effective weapons and tactics used by other players. This makes it possible to provide the strategy with the highest success rate based on the strategy data from other players.

[0033] The strategy analysis unit can automatically discover hidden elements or tricks in a game and provide them to the player. For example, the generation AI can automatically discover hidden elements and tricks in a game and provide them to the player. For example, in an RPG game, the strategy analysis unit identifies the locations of hidden items and hidden bosses and notifies the player. The strategy analysis unit also analyzes hidden elements and tricks in a game, and the generation AI provides them to the player. For example, in an action game, the strategy analysis unit discovers hidden stages and events that occur under specific conditions. The strategy analysis unit also allows the generation AI to automatically discover hidden elements and tricks in a game and provide them according to the player's progress. For example, in a shooting game, the strategy analysis unit identifies hidden weapons and enemies that appear under specific conditions. This allows hidden elements and tricks in a game to be automatically discovered and provided to the player.

[0034] The advice providing unit can analyze the player's operations in real time and instantly provide optimal strategy advice. For example, in an action game, the generation AI analyzes the player's operations in real time and instantly provides optimal strategy advice. For example, in an action game, the generation AI detects the player's operational errors and suggests appropriate ways to avoid them. The advice providing unit also analyzes the player's operation data in real time and the generation AI instantly provides optimal strategy advice. For example, in a shooting game, the generation AI provides advice to improve the player's shooting accuracy. The advice providing unit also builds a system in which the generation AI analyzes the player's operations in real time and instantly provides optimal strategy advice. For example, in an RPG game, the generation AI analyzes the player's operations during battle and suggests the optimal timing to use skills and items. This makes it possible to provide optimal strategy advice in real time.

[0035] The advice providing unit can learn the behavior patterns of enemy characters in the game and propose optimal countermeasures. For example, the generation AI of the advice providing unit learns the behavior patterns of enemy characters in the game and proposes optimal countermeasures. For example, in an action game, the generation AI analyzes the attack pattern of a specific boss and proposes evasion methods and attack timing. The advice providing unit also learns the behavior patterns of enemy characters, and the generation AI proposes optimal countermeasures to the player. For example, in a shooting game, the generation AI provides countermeasures for the enemy's appearance position and attack pattern. The advice providing unit also learns the behavior patterns of enemy characters in the game and proposes optimal countermeasures tailored to the player's progress. For example, in an RPG game, the generation AI suggests the use of skills and items that are effective against specific enemies. This makes it possible to provide optimal countermeasures based on the enemy character's behavior patterns.

[0036] The advice providing unit can analyze other players' walkthrough videos and extract and provide the most effective strategy. For example, the generation AI in the advice providing unit analyzes other players' walkthrough videos and extracts and provides the most effective strategy. For example, in an action game, the generation AI suggests strategies for boss battles that other players have successfully completed. The advice providing unit also extracts and provides the most effective strategy based on other players' walkthrough videos. For example, in a shooting game, the generation AI suggests effective tactics and weapon usage methods used by other players. The advice providing unit also analyzes other players' walkthrough videos and provides the optimal strategy tailored to the player's progress. For example, in an RPG game, the generation AI suggests effective skills and item usage methods used by other players. This allows the generation AI to provide the most effective strategy based on other players' walkthrough videos.

[0037] The advice providing unit can provide strategy advice based on in-game environmental elements. For example, the generation AI provides strategy advice taking into account in-game environmental elements (terrain, weather, etc.). For example, in an action game, the generation AI proposes the optimal strategy for specific terrain and weather conditions. The advice providing unit also analyzes in-game environmental elements, and the generation AI provides the player with the optimal strategy advice. For example, in a shooting game, the generation AI proposes effective tactics for specific terrain and weather conditions. The advice providing unit also considers in-game environmental elements and provides the optimal strategy advice tailored to the player's progress. For example, in an RPG game, the generation AI proposes effective ways to use skills and items for specific terrain and weather conditions. This makes it possible to provide strategy advice that takes into account in-game environmental elements.

[0038] The assist operation unit can learn the player's operation patterns and perform assist operations in a way that complements the player's operations. For example, the assist operation unit uses a generation AI to learn the player's operation patterns and perform assist operations in a way that complements the player's operations. For example, in a shooting game, the assist operation unit provides assist operations to improve the player's shooting accuracy. The assist operation unit also learns the player's operation data and the generation AI performs assist operations in a way that complements the player's operations. For example, in an action game, the assist operation unit assists the player's jumps and evasion operations. The assist operation unit also builds a system in which the generation AI learns the player's operation patterns and performs assist operations in a way that complements the player's operations. For example, in an RPG game, the assist operation unit assists the player's operations during battle. This makes it possible to perform assist operations in a way that complements the player's operations.

[0039] The assist operation unit can provide optimal assist operations for specific events or missions in a game. For example, the assist operation unit allows a generating AI to provide optimal assist operations for specific events or missions in a game. For example, in a shooting game, the assist operation unit assists operations in specific boss battles or missions. The assist operation unit also analyzes specific events or missions in a game, and the generating AI provides optimal assist operations to the player. For example, in an action game, the assist operation unit assists operations in specific stages or events. The assist operation unit also allows a generating AI to provide optimal assist operations for specific events or missions in a game, tailored to the player's progress. For example, in an RPG game, the assist operation unit assists operations in specific quests or boss battles. This makes it possible to provide optimal assist operations for specific events or missions.

[0040] The assist operation unit can analyze the operation data of other players and provide the most effective assist operation. For example, in a shooting game, the generating AI of the assist operation unit analyzes the operation data of other players and provides the most effective assist operation. For example, in a shooting game, the operating patterns that other players have successfully performed are used as a reference. Furthermore, the generating AI of the assist operation unit provides the most effective assist operation based on the operation data of other players. For example, in an action game, it suggests effective operation methods used by other players. Furthermore, in a role-playing game, the generating AI of the assist operation unit analyzes the operation data of other players and provides the optimal assist operation tailored to the player's progress. For example, in an RPG game, it suggests effective ways to use skills and items used by other players. This makes it possible to provide the most effective assist operation based on the operation data of other players.

[0041] The assist operation unit automates the operation of specific characters or items in the game, reducing the burden on the player. For example, the assist operation unit automates the operation of specific characters or items in the game using a generation AI, reducing the burden on the player. For example, in a shooting game, the assist operation unit automates the use of specific weapons and items. The assist operation unit also analyzes the operation of specific characters and items in the game, allowing the generation AI to provide the player with the optimal automated operation. For example, in an action game, the assist operation unit automates the operation of specific characters and items in the game using a generation AI, providing the optimal automated operation tailored to the player's progress. For example, in an RPG game, the assist operation unit automates the use of specific skills and items. This automates the operation of specific characters and items, reducing the burden on the player.

[0042] The autoplay unit can analyze the in-game scenario and select the most efficient play method to perform autoplay. For example, the autoplay unit uses a generation AI to analyze all in-game scenarios and select the most efficient play method to perform fully automatic play. For example, in an RPG game, the autoplay unit selects the optimal quest order and timing for using skills. The autoplay unit also analyzes the in-game scenario and uses a generation AI to select the optimal play method for the player to perform fully automatic play. For example, in an action game, the autoplay unit selects the optimal order for clearing stages and timing for using items. The autoplay unit also analyzes all in-game scenarios and uses a generation AI to select the optimal play method based on the player's progress to perform fully automatic play. For example, in a shooting game, the autoplay unit selects the optimal order for defeating enemies and timing for using weapons. This allows the most efficient play method to be selected to perform fully automatic play.

[0043] The autoplay unit can learn the player's past play style and provide autoplay based on that. For example, the generation AI of the autoplay unit learns the player's past play style and provides autoplay based on that. For example, in an RPG game, autoplay is performed that reflects the player's preferred skills and item usage. The autoplay unit also learns the player's past play style and the generation AI provides autoplay that is optimal for the player. For example, in an action game, autoplay is performed that reflects the player's preferred operation patterns. The autoplay unit also learns the player's past play style and provides autoplay that is optimal according to the player's progress. For example, in a shooting game, autoplay is performed that reflects the player's preferred weapons and tactics. This makes it possible to provide autoplay based on the player's past play style.

[0044] The autoplay unit can collect autoplay data of other players and provide an autoplay method with the highest success rate. For example, in an RPG game, the autoplay unit suggests the order in which other players successfully completed quests and the timing of skill use. Furthermore, based on the autoplay data of other players, the autoplay unit can provide the autoplay method with the highest success rate. For example, in an action game, the autoplay unit suggests the order in which other players successfully completed stages and the timing of item use. Furthermore, the autoplay unit can collect autoplay data of other players and provide the optimal autoplay method tailored to the player's progress. For example, in a shooting game, the autoplay unit suggests the order in which other players successfully defeated enemies and the timing of weapon use. This allows the autoplay unit to provide the autoplay method with the highest success rate based on the autoplay data of other players.

[0045] The autoplay unit can provide autoplay that automatically clears specific events or missions in a game. For example, the autoplay unit provides autoplay in which a generation AI automatically clears specific events or missions in a game. For example, in an RPG game, it automatically clears specific quests or boss battles. The autoplay unit also analyzes specific events or missions in a game, and the generation AI provides the player with the optimal autoplay method. For example, in an action game, it automatically clears specific stages or events. The autoplay unit also provides the optimal autoplay method that matches the player's progress by automatically clearing specific events or missions in a game. For example, in a shooting game, it automatically clears specific enemies or missions. This makes it possible to provide autoplay that automatically clears specific events or missions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The game support system can also be equipped with a health management unit that monitors the player's health. The health management unit monitors the player's heart rate and stress level in real time and suggests appropriate breaks. For example, it can detect fatigue caused by playing for an extended period of time and send a notification urging the player to take a break. The health management unit can also manage playing time and suggest appropriate exercises based on the player's health data. This allows the player to enjoy the game while maintaining their health.

[0048] The game support system can further include a learning support unit to enhance the player's learning effect. The learning support unit provides in-game knowledge and skills as learning materials. For example, in a historical game, it provides explanations of actual historical backgrounds and events. The learning support unit can also track the player's learning progress and provide appropriate feedback. This allows players to learn while having fun playing the game.

[0049] The game support system may further include a creative support unit to stimulate the player's creativity. The creative support unit provides tools for the player to create original content within the game. For example, it may support character customization and stage design. The creative support unit may also provide a platform for collecting the player's creative ideas and sharing them with other players. This may bring out the player's creativity and increase the enjoyment of the game.

[0050] The game support system may further include a training section for supporting the improvement of a player's skills. The training section provides a training program according to the player's skill level. For example, in a shooting game, it may provide a practice mode to improve shooting accuracy. The training section may also track the player's progress and provide appropriate feedback. This allows the player's skills to be improved efficiently.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The game information acquisition unit acquires information about the game that the player wants to complete. For example, it acquires the game title and progress status entered by the player. The game information acquisition unit can also acquire related information from the game database. Step 2: The strategy analysis unit analyzes the strategy based on the game information acquired by the game information acquisition unit. For example, it analyzes the strategy using a generation AI and proposes the optimal strategy depending on the progress of the game. Step 3: The advice provider provides advice based on the strategy analyzed by the strategy analysis unit. For example, the generator AI can provide advice to the player via text messages, audio guides, or video tutorials. Step 4: The assist operation unit assists the player with their operations. For example, it can use generative AI to assist the player's operations and automate certain operations. Step 5: The autoplay section plays the game automatically. For example, the game can be played completely automatically using a generative AI, or it can autoplay only specific stages according to the player's wishes.

[0053] (Example 2) A game support system according to an embodiment of the present invention uses a generation AI to provide complete support to players who have purchased but not played, or played but not completed, many games, all the way to completion. This allows the game support system to provide support for game completion, strategy advice, assistive operations, and even fully automatic play, according to the player's wishes.

[0054] A game support system according to an embodiment includes a game information acquisition unit, a strategy analysis unit, an advice providing unit, an assist operation unit, and an autoplay unit. The game information acquisition unit acquires information about a game that a player wants to complete. For example, the game information acquisition unit acquires the title and progress of the game entered by the player. The game information acquisition unit can also acquire related information from a game database. The strategy analysis unit analyzes a strategy based on the game information acquired by the game information acquisition unit. For example, the strategy analysis unit analyzes a strategy for a game using a generation AI. The strategy analysis unit can also suggest an optimal strategy based on the progress of the game. The advice providing unit provides advice based on the strategy analyzed by the strategy analysis unit. For example, the advice providing unit provides advice to the player using a text message or audio guide using a generation AI. The advice providing unit can also provide a video tutorial. The assist operation unit assists the player's operation. For example, the assist operation unit supports the player's operation using a generation AI. The assist operation unit can also automate certain operations. The autoplay unit automatically plays the game. For example, the autoplay unit can use a generation AI to play the game completely automatically. The autoplay unit can also autoplay only specific stages at the player's request. This allows the game support system according to the embodiment to provide support for clearing the game at the player's request.

[0055] The strategy analysis unit can analyze a player's past play data and propose strategies optimized for the player's play style. For example, the generation AI collects the player's past play data and analyzes the player's operation patterns and strategies. For example, in an RPG game, the strategy analysis unit identifies the player's preferred play style based on the player's past play data and proposes strategies based on that. Furthermore, the strategy analysis unit can identify a player's preferred play style based on the player's past play data and propose strategies tailored to that style. For example, in an action game, the generation AI can propose strategies that utilize the player's preferred attack patterns. Furthermore, the generation AI can analyze a player's past play data, identify the player's weaknesses, and propose strategies that focus on supporting those weaknesses. For example, in a shooting game, the generation AI can propose countermeasures for enemy placements and attack patterns that the player finds difficult. This allows the provision of strategies optimized for the player's play style.

[0056] The strategy analysis unit can optimize the timing of using in-game items or skills, supporting efficient game completion. For example, the strategy analysis unit allows the generation AI to analyze the effects of all in-game items and skills and suggest the optimal timing for their use. For example, in an RPG game, the strategy analysis unit optimizes the timing of using recovery items and strengthening skills during boss battles. The strategy analysis unit also allows the generation AI to adjust the timing of item and skill use in real time according to the player's progress. For example, in an action game, the strategy analysis unit suggests the use of defensive skills and evasive items to match the enemy's attack patterns. The strategy analysis unit also allows the generation AI to learn the effects of all in-game items and skills and suggest the optimal timing for their use to suit the player's play style. For example, in a shooting game, the strategy analysis unit suggests the use of items that are effective against specific enemies or stages. This supports efficient game completion.

[0057] The strategy analysis unit can use the emotion estimation function to identify situations in which the player feels stressed and provide focused support on strategies for overcoming those situations. For example, the strategy analysis unit can use the emotion estimation function to identify situations in which the player feels stressed in real time and suggest strategies for overcoming those situations. For example, in an RPG game, the strategy analysis unit can provide strategies for difficult boss battles or complex dungeons. The strategy analysis unit can also analyze the player's emotion data to identify situations in which the player feels stressed and provide focused support on strategies for overcoming those situations. For example, in an action game, the strategy analysis unit can suggest strategies for overcoming stages or enemies that the player finds difficult. The strategy analysis unit can also use the emotion estimation function to identify situations in which the player feels stressed and customize strategies for those situations. For example, in a shooting game, the strategy analysis unit can provide countermeasures for enemy attack patterns that the player finds difficult. This allows focused support on strategies for overcoming those situations in which the player feels stressed.

[0058] The strategy analysis unit can collect strategy data from other players and propose the strategy with the highest success rate. For example, the generation AI collects strategy data from other players and analyzes the strategy with the highest success rate. For example, in an RPG game, the strategy analysis unit proposes effective skill and item combinations used by other players. The strategy analysis unit also proposes the strategy with the highest success rate based on the strategy data from other players. For example, in an action game, the strategy analysis unit refers to the strategies for stages that other players have cleared. The strategy analysis unit also proposes the optimal strategy based on the player's progress. For example, in a shooting game, the strategy analysis unit proposes effective weapons and tactics used by other players. This makes it possible to provide the strategy with the highest success rate based on the strategy data from other players.

[0059] The strategy analysis unit can automatically discover hidden elements or tricks in a game and provide them to the player. For example, the generation AI can automatically discover hidden elements and tricks in a game and provide them to the player. For example, in an RPG game, the strategy analysis unit identifies the locations of hidden items and hidden bosses and notifies the player. The strategy analysis unit also analyzes hidden elements and tricks in a game, and the generation AI provides them to the player. For example, in an action game, the strategy analysis unit discovers hidden stages and events that occur under specific conditions. The strategy analysis unit also allows the generation AI to automatically discover hidden elements and tricks in a game and provide them according to the player's progress. For example, in a shooting game, the strategy analysis unit identifies hidden weapons and enemies that appear under specific conditions. This allows hidden elements and tricks in a game to be automatically discovered and provided to the player.

[0060] The strategy analysis unit can use the emotion estimation function to identify scenes that the player is enjoying and propose a strategy that emphasizes those scenes. For example, the strategy analysis unit uses the emotion estimation function to identify scenes that the player is enjoying and propose a strategy that emphasizes those scenes. For example, in an RPG game, the strategy analysis unit provides a strategy related to the story or characters that the player is enjoying. The strategy analysis unit also analyzes the player's emotion data to identify scenes that the player is enjoying and proposes a strategy that emphasizes those scenes. For example, in an action game, the strategy analysis unit provides a strategy related to a stage or boss battle that the player is enjoying. The strategy analysis unit also uses the emotion estimation function to identify scenes that the player is enjoying and customize a strategy that emphasizes those scenes. For example, in a shooting game, the strategy analysis unit provides a strategy related to the enemy placement or attack pattern that the player is enjoying. This makes it possible to provide a strategy that emphasizes scenes that the player is enjoying.

[0061] The advice providing unit can analyze the player's operations in real time and instantly provide optimal strategy advice. For example, in an action game, the generation AI analyzes the player's operations in real time and instantly provides optimal strategy advice. For example, in an action game, the generation AI detects the player's operational errors and suggests appropriate ways to avoid them. The advice providing unit also analyzes the player's operation data in real time and the generation AI instantly provides optimal strategy advice. For example, in a shooting game, the generation AI provides advice to improve the player's shooting accuracy. The advice providing unit also builds a system in which the generation AI analyzes the player's operations in real time and instantly provides optimal strategy advice. For example, in an RPG game, the generation AI analyzes the player's operations during battle and suggests the optimal timing to use skills and items. This makes it possible to provide optimal strategy advice in real time.

[0062] The advice providing unit can learn the behavior patterns of enemy characters in the game and propose optimal countermeasures. For example, the generation AI of the advice providing unit learns the behavior patterns of enemy characters in the game and proposes optimal countermeasures. For example, in an action game, the generation AI analyzes the attack pattern of a specific boss and proposes evasion methods and attack timing. The advice providing unit also learns the behavior patterns of enemy characters, and the generation AI proposes optimal countermeasures to the player. For example, in a shooting game, the generation AI provides countermeasures for the enemy's appearance position and attack pattern. The advice providing unit also learns the behavior patterns of enemy characters in the game and proposes optimal countermeasures tailored to the player's progress. For example, in an RPG game, the generation AI suggests the use of skills and items that are effective against specific enemies. This makes it possible to provide optimal countermeasures based on the enemy character's behavior patterns.

[0063] The advice providing unit can use the emotion estimation function to identify situations in which the player finds difficulty and provide specific advice for overcoming the situation. The advice providing unit can, for example, use the emotion estimation function to identify situations in which the player finds difficulty and provide specific advice for overcoming the situation. For example, in an action game, the advice providing unit can suggest strategies for overcoming a boss battle in which the player is struggling. The advice providing unit can also analyze the player's emotion data to identify situations in which the player finds difficulty and provide specific advice for overcoming the situation. For example, in a shooting game, the advice providing unit can suggest strategies for overcoming a stage or enemy that the player has difficulty with. The advice providing unit can also use the emotion estimation function to identify situations in which the player finds difficulty and customize specific advice for overcoming the situation. For example, in an RPG game, the advice providing unit can suggest strategies for overcoming a dungeon or boss battle in which the player is struggling. This makes it possible to provide specific advice for overcoming a situation in which the player finds difficulty.

[0064] The advice providing unit can analyze other players' walkthrough videos and extract and provide the most effective strategy. For example, the generation AI in the advice providing unit analyzes other players' walkthrough videos and extracts and provides the most effective strategy. For example, in an action game, the generation AI suggests strategies for boss battles that other players have successfully completed. The advice providing unit also extracts and provides the most effective strategy based on other players' walkthrough videos. For example, in a shooting game, the generation AI suggests effective tactics and weapon usage methods used by other players. The advice providing unit also analyzes other players' walkthrough videos and provides the optimal strategy tailored to the player's progress. For example, in an RPG game, the generation AI suggests effective skills and item usage methods used by other players. This allows the generation AI to provide the most effective strategy based on other players' walkthrough videos.

[0065] The advice providing unit can provide strategy advice based on in-game environmental elements. For example, the generation AI provides strategy advice taking into account in-game environmental elements (terrain, weather, etc.). For example, in an action game, the generation AI proposes the optimal strategy for specific terrain and weather conditions. The advice providing unit also analyzes in-game environmental elements, and the generation AI provides the player with the optimal strategy advice. For example, in a shooting game, the generation AI proposes effective tactics for specific terrain and weather conditions. The advice providing unit also considers in-game environmental elements and provides the optimal strategy advice tailored to the player's progress. For example, in an RPG game, the generation AI proposes effective ways to use skills and items for specific terrain and weather conditions. This makes it possible to provide strategy advice that takes into account in-game environmental elements.

[0066] The advice providing unit can use the emotion estimation function to identify scenes that excite the player and strengthen the strategy advice for those scenes. The advice providing unit, for example, uses the emotion estimation function to identify scenes that excite the player and strengthen the strategy advice for those scenes. For example, in an action game, the advice providing unit provides strategies for boss battles or specific stages that excite the player. The advice providing unit also analyzes the player's emotion data to identify exciting scenes and strengthen the strategy advice for those scenes. For example, in a shooting game, the advice providing unit provides strategies for enemy appearances or events that excite the player. The advice providing unit also uses the emotion estimation function to identify scenes that excite the player and customize the strategy advice for those scenes. For example, in an RPG game, the advice providing unit provides strategies related to story developments or characters that excite the player. This makes it possible to strengthen the strategy advice for scenes that excite the player.

[0067] The assist operation unit can learn the player's operation patterns and perform assist operations in a way that complements the player's operations. For example, the assist operation unit uses a generation AI to learn the player's operation patterns and perform assist operations in a way that complements the player's operations. For example, in a shooting game, the assist operation unit provides assist operations to improve the player's shooting accuracy. The assist operation unit also learns the player's operation data and the generation AI performs assist operations in a way that complements the player's operations. For example, in an action game, the assist operation unit assists the player's jumps and evasion operations. The assist operation unit also builds a system in which the generation AI learns the player's operation patterns and performs assist operations in a way that complements the player's operations. For example, in an RPG game, the assist operation unit assists the player's operations during battle. This makes it possible to perform assist operations in a way that complements the player's operations.

[0068] The assist operation unit can provide optimal assist operations for specific events or missions in a game. For example, the assist operation unit allows a generating AI to provide optimal assist operations for specific events or missions in a game. For example, in a shooting game, the assist operation unit assists operations in specific boss battles or missions. The assist operation unit also analyzes specific events or missions in a game, and the generating AI provides optimal assist operations to the player. For example, in an action game, the assist operation unit assists operations in specific stages or events. The assist operation unit also allows a generating AI to provide optimal assist operations for specific events or missions in a game, tailored to the player's progress. For example, in an RPG game, the assist operation unit assists operations in specific quests or boss battles. This makes it possible to provide optimal assist operations for specific events or missions.

[0069] The assist operation unit can use the emotion estimation function to identify situations that make the player tense and strengthen the assist operation in those situations. The assist operation unit, for example, uses the emotion estimation function to identify situations that make the player tense and strengthen the assist operation in those situations. For example, in a shooting game, the assist operation unit assists with operations during boss battles or specific stages that make the player tense. The assist operation unit also analyzes the player's emotion data to identify situations that make the player tense and strengthens the assist operation in those situations. For example, in an action game, the assist operation unit assists with operations during enemy appearances or events that make the player tense. The assist operation unit also uses the emotion estimation function to identify situations that make the player tense and customize the assist operation in those situations. For example, in an RPG game, the assist operation unit assists with operations related to story developments or characters that make the player tense. This makes it possible to strengthen the assist operation in situations that make the player tense.

[0070] The assist operation unit can analyze the operation data of other players and provide the most effective assist operation. For example, in a shooting game, the generating AI of the assist operation unit analyzes the operation data of other players and provides the most effective assist operation. For example, in a shooting game, the operating patterns that other players have successfully performed are used as a reference. Furthermore, the generating AI of the assist operation unit provides the most effective assist operation based on the operation data of other players. For example, in an action game, it suggests effective operation methods used by other players. Furthermore, in a role-playing game, the generating AI of the assist operation unit analyzes the operation data of other players and provides the optimal assist operation tailored to the player's progress. For example, in an RPG game, it suggests effective ways to use skills and items used by other players. This makes it possible to provide the most effective assist operation based on the operation data of other players.

[0071] The assist operation unit automates the operation of specific characters or items in the game, reducing the burden on the player. For example, the assist operation unit automates the operation of specific characters or items in the game using a generation AI, reducing the burden on the player. For example, in a shooting game, the assist operation unit automates the use of specific weapons and items. The assist operation unit also analyzes the operation of specific characters and items in the game, allowing the generation AI to provide the player with the optimal automated operation. For example, in an action game, the assist operation unit automates the operation of specific characters and items in the game using a generation AI, providing the optimal automated operation tailored to the player's progress. For example, in an RPG game, the assist operation unit automates the use of specific skills and items. This automates the operation of specific characters and items, reducing the burden on the player.

[0072] The assist operation unit can use the emotion estimation function to identify scenes that the player is enjoying and minimize the assist operation in those scenes. The assist operation unit, for example, uses the emotion estimation function to identify scenes that the player is enjoying and minimize the assist operation in those scenes. For example, in a shooting game, the assist operation unit minimizes operations in stages that the player is enjoying or when enemies appear. The assist operation unit also analyzes the player's emotion data to identify scenes that the player is enjoying and minimizes the assist operation in those scenes. For example, in an action game, the assist operation unit minimizes operations in events or boss battles that the player is enjoying. The assist operation unit also uses the emotion estimation function to identify scenes that the player is enjoying and customize the assist operation for those scenes. For example, in an RPG game, the assist operation unit minimizes operations related to story developments or characters that the player is enjoying. This makes it possible to minimize the assist operation in scenes that the player is enjoying.

[0073] The autoplay unit can analyze the in-game scenario and select the most efficient play method to perform autoplay. For example, the autoplay unit uses a generation AI to analyze all in-game scenarios and select the most efficient play method to perform fully automatic play. For example, in an RPG game, the autoplay unit selects the optimal quest order and timing for using skills. The autoplay unit also analyzes the in-game scenario and uses a generation AI to select the optimal play method for the player to perform fully automatic play. For example, in an action game, the autoplay unit selects the optimal order for clearing stages and timing for using items. The autoplay unit also analyzes all in-game scenarios and uses a generation AI to select the optimal play method based on the player's progress to perform fully automatic play. For example, in a shooting game, the autoplay unit selects the optimal order for defeating enemies and timing for using weapons. This allows the most efficient play method to be selected to perform fully automatic play.

[0074] The autoplay unit can learn the player's past play style and provide autoplay based on that. For example, the generation AI of the autoplay unit learns the player's past play style and provides autoplay based on that. For example, in an RPG game, autoplay is performed that reflects the player's preferred skills and item usage. The autoplay unit also learns the player's past play style and the generation AI provides autoplay that is optimal for the player. For example, in an action game, autoplay is performed that reflects the player's preferred operation patterns. The autoplay unit also learns the player's past play style and provides autoplay that is optimal according to the player's progress. For example, in a shooting game, autoplay is performed that reflects the player's preferred weapons and tactics. This makes it possible to provide autoplay based on the player's past play style.

[0075] The autoplay unit can use the emotion estimation function to perform autoplay that prioritizes the playback of scenes that the player particularly enjoys. The autoplay unit, for example, uses the emotion estimation function to perform autoplay that prioritizes the playback of scenes that the player particularly enjoys. For example, in an RPG game, it prioritizes the playback of scenes featuring stories or characters that the player enjoys. The autoplay unit also analyzes the player's emotion data and performs autoplay that prioritizes the playback of scenes that the player particularly enjoys. For example, in an action game, it prioritizes the playback of boss battles or specific stages that the player enjoys. The autoplay unit also uses the emotion estimation function to identify scenes that the player particularly enjoys and customizes autoplay that prioritizes the playback of those scenes. For example, in a shooting game, it prioritizes the playback of enemy appearances or events that the player enjoys. This allows autoplay that prioritizes the playback of scenes that the player particularly enjoys.

[0076] The autoplay unit can collect autoplay data of other players and provide an autoplay method with the highest success rate. For example, in an RPG game, the autoplay unit suggests the order in which other players successfully completed quests and the timing of skill use. Furthermore, based on the autoplay data of other players, the autoplay unit can provide the autoplay method with the highest success rate. For example, in an action game, the autoplay unit suggests the order in which other players successfully completed stages and the timing of item use. Furthermore, the autoplay unit can collect autoplay data of other players and provide the optimal autoplay method tailored to the player's progress. For example, in a shooting game, the autoplay unit suggests the order in which other players successfully defeated enemies and the timing of weapon use. This allows the autoplay unit to provide the autoplay method with the highest success rate based on the autoplay data of other players.

[0077] The autoplay unit can provide autoplay that automatically clears specific events or missions in a game. For example, the autoplay unit provides autoplay in which a generation AI automatically clears specific events or missions in a game. For example, in an RPG game, it automatically clears specific quests or boss battles. The autoplay unit also analyzes specific events or missions in a game, and the generation AI provides the player with the optimal autoplay method. For example, in an action game, it automatically clears specific stages or events. The autoplay unit also provides the optimal autoplay method that matches the player's progress by automatically clearing specific events or missions in a game. For example, in a shooting game, it automatically clears specific enemies or missions. This makes it possible to provide autoplay that automatically clears specific events or missions.

[0078] The autoplay unit can use the emotion estimation function to identify scenes that excite the player and perform autoplay that emphasizes those scenes. The autoplay unit, for example, uses the emotion estimation function to identify scenes that excite the player and perform autoplay that emphasizes those scenes. For example, in an RPG game, the autoplay unit emphasizes story or character scenes that excite the player. The autoplay unit also analyzes the player's emotion data to identify exciting scenes and perform autoplay that emphasizes those scenes. For example, in an action game, the autoplay unit emphasizes boss battles or specific stages that excite the player. The autoplay unit also uses the emotion estimation function to identify scenes that excite the player and customize autoplay that emphasizes those scenes. For example, in a shooting game, the autoplay unit emphasizes enemy appearances or events that excite the player. This allows autoplay that emphasizes scenes that excite the player.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The game support system can also be equipped with a health management unit that monitors the player's health. The health management unit monitors the player's heart rate and stress level in real time and suggests appropriate breaks. For example, it can detect fatigue caused by playing for an extended period of time and send a notification urging the player to take a break. The health management unit can also manage playing time and suggest appropriate exercises based on the player's health data. This allows the player to enjoy the game while maintaining their health.

[0081] The game support system can further include a learning support unit to enhance the player's learning effect. The learning support unit provides in-game knowledge and skills as learning materials. For example, in a historical game, it provides explanations of actual historical backgrounds and events. The learning support unit can also track the player's learning progress and provide appropriate feedback. This allows players to learn while having fun playing the game.

[0082] The game support system can further include a communication support unit to improve the sociability of players. The communication support unit provides a function to promote interaction between players. For example, it can match players with common interests and suggest cooperative or competitive play. The communication support unit can also analyze players' emotions and suggest appropriate communication methods. This allows players to deepen their interactions with each other and make new friends through games.

[0083] The game support system may further include a creative support unit to stimulate the player's creativity. The creative support unit provides tools for the player to create original content within the game. For example, it may support character customization and stage design. The creative support unit may also provide a platform for collecting the player's creative ideas and sharing them with other players. This may bring out the player's creativity and increase the enjoyment of the game.

[0084] The game support system may further include a motivation management unit that estimates the player's emotions and maintains the player's motivation. The motivation management unit analyzes the player's emotional data and provides appropriate rewards and challenges. For example, it may set goals that give the player a sense of accomplishment and provide special rewards when achieved. The motivation management unit may also adjust the difficulty of the game according to the player's emotions. This allows the player to maintain their motivation and enjoy the game for a long period of time.

[0085] The game support system may further include a feedback collection unit that collects player feedback and helps improve the game. The feedback collection unit collects player impressions and opinions while playing in real time and provides them to the game developer. For example, it collects evaluations of specific stages or characters and suggests areas for improvement. The feedback collection unit may also analyze player emotional data and evaluate the player's satisfaction with the game. This makes it possible to provide a better game that reflects player opinions.

[0086] The game support system may further include a training section for supporting the improvement of a player's skills. The training section provides a training program according to the player's skill level. For example, in a shooting game, it may provide a practice mode to improve shooting accuracy. The training section may also track the player's progress and provide appropriate feedback. This allows the player's skills to be improved efficiently.

[0087] The game support system can further include a relaxation unit that estimates the player's emotions and reduces the player's stress. The relaxation unit analyzes the player's emotional data and suggests appropriate relaxation methods. For example, if the player is feeling stressed, the relaxation unit can provide relaxing music or images. The relaxation unit can also temporarily lower the difficulty of the game depending on the player's emotions. This reduces the player's stress and allows them to enjoy the game in a relaxed state.

[0088] The game support system may further include a concentration improvement unit that estimates the player's emotions and improves the player's concentration. The concentration improvement unit analyzes the player's emotional data and suggests an appropriate method for improving concentration. For example, if the player lacks concentration, the concentration improvement unit may provide a mini-game or training to improve concentration. The concentration improvement unit may also adjust the speed of the game according to the player's emotions. This improves the player's concentration and allows the player to progress through the game more effectively.

[0089] The game support system may further include a customization unit that estimates the player's emotions and provides a customized game experience according to the player's emotions. The customization unit analyzes the player's emotional data and suggests game settings that match the player's preferences. For example, if the player wants to relax, the customization unit may lower the game's difficulty or provide relaxing music. The customization unit may also customize the game's visuals and sounds according to the player's emotions. This makes it possible to provide a customized game experience according to the player's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The game information acquisition unit acquires information about the game that the player wants to complete. For example, it acquires the game title and progress status entered by the player. The game information acquisition unit can also acquire related information from the game database. Step 2: The strategy analysis unit analyzes the strategy based on the game information acquired by the game information acquisition unit. For example, it analyzes the strategy using a generation AI and proposes the optimal strategy depending on the progress of the game. Step 3: The advice provider provides advice based on the strategy analyzed by the strategy analysis unit. For example, the generator AI can provide advice to the player via text messages, audio guides, or video tutorials. Step 4: The assist operation unit assists the player with their operations. For example, it can use generative AI to assist the player's operations and automate certain operations. Step 5: The autoplay section plays the game automatically. For example, the game can be played completely automatically using a generative AI, or it can autoplay only specific stages according to the player's wishes.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 AI 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[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 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.

[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. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[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 headset type terminal 314, 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. 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 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 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.

[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 AI 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 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[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 robot 414, 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 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 processing similar to that of the specific 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 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.

[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 AI 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 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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, in order to avoid confusion and to 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.

[0158] 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. [Explanation of symbols]

[0159] 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 system that uses a generating AI to provide support for clearing a game according to the player's wishes, a game information acquisition unit that acquires information about a game that a player wants to complete; a strategy analysis unit that analyzes a strategy based on the game information acquired by the game information acquisition unit; an advice providing unit that provides advice based on the strategy analyzed by the strategy analysis unit; an assist operation unit that assists the player's operation; an autoplay unit that automatically plays the game; A system characterized by:

2. The strategy analysis unit Analyzing the player's past play data and proposing the strategy optimized for the player's play style The system of claim 1 .

3. The strategy analysis unit Optimize the timing of using items or skills in the game to support efficient game completion The system of claim 1 .

4. The strategy analysis unit Identify situations where the player feels stressed and provide focused support for the strategy in those situations. The system of claim 1 .

5. The strategy analysis unit Collecting strategy data from other players and proposing the strategy with the highest success rate The system of claim 1 .

6. The strategy analysis unit Automatically discovering the hidden elements or tricks in the game and providing them to the player The system of claim 1 .

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