System

Abstract

An object of a system according to an embodiment is to efficiently collect and appropriately provide game mastery information.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The receiving unit receives information. The analysis unit analyzes the information received by the reception unit. The providing unit provides strategy information based on the information obtained by the analysis unit.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 techniques have not been able to efficiently collect and appropriately provide game strategy information, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect game strategy information and provide it appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, and a providing unit. The receiving unit receives information. The analysis unit analyzes the information received by the receiving unit. The providing unit provides strategy information based on the information obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect game strategy information and provide it appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention utilizes a generation AI to provide information for game progress. The information provision system inputs information about the game a user is playing, and the generation AI analyzes the information to generate and provide information useful for game progress to the user. This mechanism allows the user to obtain specific game strategy information for game progress. For example, the information provision system inputs detailed information about the game the user is playing, such as the title of the game, the user's current progress, and the problem areas. For example, the user inputs information such as "I can't beat the boss at level 10 in game X." This information is input to the generation AI. The information provision system then analyzes the input information using the generation AI. The generation AI generates a strategy for the problem area based on a game database and past game strategy information. For example, the generation AI generates specific game strategy information such as "To beat the boss at level 10, use a specific item." The information provision system then provides the generated game strategy information to the user. For example, the game strategy information generated by the generation AI is displayed on the user's smartphone or PC. Based on this information, the user can progress through the game advantageously. This allows the information provision system to obtain specific game strategy information for game progress advantageously. For example, detailed strategy information such as how to obtain a specific item or the weaknesses of a boss can be obtained. In addition, because the generation AI proposes new strategies based on past strategy information, the user can always obtain the latest strategy information. This allows the information provision system to obtain specific strategy information that will allow the user to progress through the game with an advantage. For example, detailed strategy information such as how to obtain a specific item or the weaknesses of a boss can be obtained. In addition, because the generation AI proposes new strategies based on past strategy information, the user can always obtain the latest strategy information.

[0029] An information provision system according to an embodiment includes a receiving unit, an analysis unit, and a providing unit. The receiving unit receives information about a game being played by a user. The information about the game being played by a user includes, for example, the game title, current progress, and difficulty points, but is not limited to these examples. The receiving unit receives information in, for example, text format. The receiving unit can also receive information in audio format. The receiving unit can also receive information in image format. For example, the receiving unit analyzes text information entered by a user to determine the progress of the game. The audio information is converted into text using audio recognition technology. The image information is analyzed using image recognition technology. The analysis unit analyzes the information received by the receiving unit using a generation AI. The analysis is performed using, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to these examples. For example, the analysis unit generates a strategy for a difficulty point based on a game database and past strategy information. The analysis unit extracts related information from the game database using, for example, data mining technology. The analysis unit can also analyze past strategy information using statistical analysis technology. The analysis unit can also use a machine learning algorithm to generate strategies tailored to the user's play style. The providing unit provides strategy information based on the information obtained by the analysis unit. The provision can be, for example, in real time, but is not limited to this example. For example, the providing unit displays the strategy information generated by the generation AI on the user's smartphone or PC. The providing unit can also provide customized information tailored to the user's progress. The providing unit can also provide real-time advice. For example, the providing unit monitors the progress of the game being played by the user and provides strategy information at appropriate times. This allows the information providing system according to the embodiment to obtain specific strategy information to help the user advance through the game. For example, detailed strategy information, such as how to obtain specific items or the weaknesses of bosses, can be obtained. Furthermore, since the generation AI suggests new strategies based on past strategy information, the user can always obtain the latest strategy information.

[0030] The reception unit can accept information in text, audio, or image format. The reception unit accepts information in, for example, text format. Examples of text formats include, but are not limited to, text files and chat messages. The reception unit can also accept information in audio format. Examples of audio formats include, but are not limited to, audio files and real-time audio input. The reception unit can also accept information in image format. Examples of image formats include, but are not limited to, image files and screenshots. This allows the user to input information in various formats. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input an audio file to a generation AI and have the generation AI convert the audio data into text data.

[0031] The analysis unit can analyze information including official guidebooks or user community information. The analysis unit, for example, analyzes information from official guidebooks. Official guidebooks include, for example, but are not limited to, walkthrough guides published by game manufacturers. The analysis unit can also analyze information from user communities. User community information includes, for example, but is not limited to, online forums and social networking groups. This allows for analysis based on a variety of information sources, thereby providing more accurate walkthrough information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from official guidebooks into a generation AI and cause the generation AI to generate walkthrough information.

[0032] The providing unit can provide advice in real time. For example, the providing unit provides advice in real time. Real-time advice includes, but is not limited to, updates every second and real-time feedback. For example, the providing unit monitors the progress of a game being played by a user and provides strategy information at an appropriate timing. The providing unit can also use a generation AI to provide advice in real time. For example, the providing unit inputs the user's progress into the generation AI and causes the generation AI to execute real-time advice. This allows the user to obtain strategy information in real time.

[0033] The providing unit can provide customized information according to the user's progress. The providing unit provides, for example, customized information according to the user's progress. Customized information includes, for example, personalized information based on the user's progress, but is not limited to such examples. For example, the providing unit monitors the progress of a game being played by the user and provides strategy information at an appropriate time. The providing unit can also use a generation AI to provide customized information according to the user's progress. For example, the providing unit can input the user's progress to the generation AI and cause the generation AI to execute the customized information. This makes it possible to provide appropriate strategy information according to the user's progress.

[0034] The information providing system includes a monitoring unit that monitors the user's progress. The monitoring unit monitors the user's progress in real time, for example. Real-time monitoring includes, but is not limited to, real-time data collection, log analysis, and the like. For example, the monitoring unit grasps the progress of a game being played by the user in real time and provides information at an appropriate timing. The monitoring unit can also use a generation AI to monitor the user's progress. For example, the monitoring unit can input the user's progress into the generation AI and cause the generation AI to perform real-time monitoring. This allows the user's progress to be grasped in real time and appropriate information to be provided.

[0035] The information provision system includes a feedback unit that collects user feedback. The feedback unit collects, for example, user feedback. Examples of collected feedback include, but are not limited to, questionnaires, user reviews, and usage logs. For example, the feedback unit improves the system based on the feedback provided by the user. The feedback unit can also use a generation AI to collect user feedback. For example, the feedback unit can input user feedback into the generation AI and have the generation AI analyze the feedback. This makes it possible to improve the system based on user feedback.

[0036] The reception unit can analyze the user's past game play history and select the optimal reception method. The reception unit, for example, analyzes the user's past game play history and selects the optimal reception method. The past game play history includes, but is not limited to, in-game logs, play time, and achievement status. For example, the reception unit preferentially suggests input methods (text, voice, image) that the user has frequently used in the past. The reception unit can also customize the reception method for a specific game title based on the user's past play history. The reception unit can also analyze the user's past play time periods and suggest the optimal reception time. This makes it possible to provide the optimal reception method based on the user's past play history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past play history data into a generation AI and have the generation AI select the optimal reception method.

[0037] The reception unit may perform filtering based on the user's current game progress and areas of interest when receiving information. For example, the reception unit may perform filtering based on the user's current game progress and areas of interest when receiving information. Examples of filtering include, but are not limited to, filtering based on progress and areas of interest. For example, the reception unit may receive only relevant information based on the progress of the game the user is currently playing. The reception unit may also filter information based on the user's areas of interest (e.g., specific characters or items). The reception unit may also grasp the user's current game progress in real time and receive appropriate information. This allows the reception of information according to the user's current situation and interests. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's progress data to a generation AI and cause the generation AI to perform filtering.

[0038] The reception unit can select the optimal reception means depending on the user's input method when receiving information. For example, the reception unit selects the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving information. Optimal reception means include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit accepts the information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the information using natural language processing technology. Furthermore, if the user selects image input, the reception unit can analyze the information using image recognition technology. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0039] The reception unit may prioritize receiving highly relevant information based on the user's geographical location information when receiving information. For example, the reception unit may prioritize receiving highly relevant information taking the user's geographical location information into consideration when receiving information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit may prioritize receiving game information related to that area. The reception unit may also prioritize receiving information about nearby game events and stores based on the user's location information. The reception unit may also prioritize receiving area-specific strategy information based on the user's location information. This allows for providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's location information data into a generation AI and cause the generation AI to select highly relevant information.

[0040] The reception unit may analyze the user's social media activity and receive related information when receiving information. For example, the reception unit may analyze the user's social media activity and receive related information when receiving information. Social media activity may include, but is not limited to, post content, the number of likes, and the number of comments. For example, the reception unit may receive related strategy information based on game information shared by the user on social media. The reception unit may also analyze the user's social media activity and receive information that may be of interest to the user. The reception unit may also receive related information based on the activity of the user's friends on social media. This allows related information to be provided based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's social media data into a generation AI and cause the generation AI to select related information.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving information. Past feedback includes, but is not limited to, survey results, user reviews, etc. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially accept specific information formats (text, audio, image) based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the game during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the game during analysis. Examples of game importance include, but are not limited to, the number of players, sales, and review ratings. For example, the analysis unit provides detailed strategy information for important boss battles. The analysis unit can also provide concise strategy information for normal enemy battles. The analysis unit can also provide detailed analysis results for events that have a significant impact on the progress of the game. This allows for providing appropriate analysis results according to the importance of the game. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit can input game importance data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the game category during analysis. For example, the analysis unit applies different analysis algorithms depending on the game category during analysis. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies an analysis algorithm related to character development in the case of an RPG game. Furthermore, the analysis unit can apply an analysis algorithm related to combat techniques in the case of an action game. Furthermore, the analysis unit can apply an analysis algorithm related to solutions in the case of a puzzle game. This makes it possible to provide appropriate analysis results depending on the game category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input game category data into a generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, log data, analysis reports, etc. For example, the analysis unit can adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy. The analysis unit can also suggest an optimal analysis method based on the user's past analysis results. This can improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] The analysis unit can determine the analysis priority based on the release date of the game during analysis. For example, the analysis unit determines the analysis priority based on the release date of the game during analysis. Game release dates include, but are not limited to, new games and update dates. For example, the analysis unit prioritizes analysis of new games. The analysis unit can also lower the analysis priority of games that have been released for some time. The analysis unit can also adjust the analysis priority based on the release date of the game being played by the user. This allows for providing appropriate analysis results according to the release date of the game. Some or all of the above-described processing by the analysis unit may be performed using, or without, an AI. For example, the analysis unit can input game release date data into the generation AI and have the generation AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the games during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the games during analysis. Game relevance includes, but is not limited to, genre and story relevance. For example, the analysis unit prioritizes analysis of information related to the game currently being played by the user. The analysis unit can also prioritize analysis of information related to games in which the user is interested. The analysis unit can also prioritize analysis of highly relevant information based on the user's past play history. This allows for providing appropriate analysis results according to the relevance of the games. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input game relevance data to a generation AI and cause the generation AI to adjust the analysis order.

[0047] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced users. For example, the analysis unit can provide a novice user with a concise analysis result that avoids technical terms. The analysis unit can also provide an advanced user with an analysis result that includes detailed technical terms. The analysis unit can also adjust the way the analysis result is presented according to the user's level of expertise. This allows for the provision of an appropriate analysis result according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0048] The providing unit can adjust the level of detail of the information to be provided based on the progress of the game when providing the information. For example, the providing unit adjusts the level of detail of the information to be provided based on the progress of the game when providing the information. The level of detail of the information includes, but is not limited to, summary information, detailed information, etc. For example, the providing unit provides basic information in the early stages of the game. The providing unit can also provide detailed strategy information in the middle stages of the game. The providing unit can also provide specific strategies and techniques in the late stages of the game. This makes it possible to provide appropriate information according to the progress of the game. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input game progress data to a generating AI and cause the generating AI to adjust the level of detail of the information.

[0049] The providing unit can apply different providing algorithms depending on the game category when providing the game. For example, the providing unit applies different providing algorithms depending on the game category when providing the game. Examples of providing algorithms include, but are not limited to, recommendation algorithms and personalized algorithms. For example, the providing unit provides information on character development in the case of an RPG game. Furthermore, the providing unit can provide information on combat techniques in the case of an action game. Furthermore, the providing unit can provide information on solutions in the case of a puzzle game. This makes it possible to provide appropriate information depending on the game category. Some or all of the above-described processing in the providing unit may be performed using, or without using, AI. For example, the providing unit can input game category data into a generation AI and cause the generation AI to apply the providing algorithm.

[0050] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. For example, the providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. Past provision results include, but are not limited to, log data, user feedback, etc. For example, the providing unit can adjust the provision algorithm based on feedback provided by the user in the past. The providing unit can also analyze the user's past provision results and improve the accuracy. The providing unit can also suggest an optimal provision method based on the user's past provision results. This can improve the accuracy of the provision based on the user's past provision results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0051] The providing unit can determine the priority of information to be provided based on the release date of the game at the time of providing the information. For example, the providing unit determines the priority of information to be provided based on the release date of the game at the time of providing the information. The priority of information includes, but is not limited to, the release date, importance rating, etc. For example, the providing unit prioritizes providing information for a new game. The providing unit can also lower the priority of providing information for a game that has been released for some time. The providing unit can also adjust the priority of providing information based on the release date of the game being played by the user. This makes it possible to provide appropriate information according to the release date of the game. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input game release date data to a generating AI and cause the generating AI to determine the priority of information.

[0052] The providing unit can adjust the order of information to be provided based on the relevance of the game when providing the information. For example, the providing unit adjusts the order of information to be provided based on the relevance of the game when providing the information. The order of information includes, but is not limited to, an order based on relevance or importance. For example, the providing unit may prioritize providing information related to a game the user is playing. The providing unit may also prioritize providing information related to a game in which the user is interested. The providing unit may also prioritize providing highly relevant information based on the user's past play history. This makes it possible to provide appropriate information according to the relevance of the game. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input game relevance data to a generating AI and cause the generating AI to adjust the order of the information.

[0053] The providing unit can adjust the use of technical terminology in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit adjusts the use of technical terminology in the information to be provided according to the user's level of expertise when providing the information. The use of technical terminology includes, but is not limited to, terms for beginners, intermediate users, and advanced users. For example, the providing unit can provide concise information that avoids technical terminology to beginner users. The providing unit can also provide information including detailed technical terminology to advanced users. The providing unit can also adjust the way information is presented according to the user's level of expertise. This allows appropriate information to be provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology.

[0054] The monitoring unit can select an optimal monitoring method by referring to the user's past game play history during monitoring. For example, the monitoring unit can select an optimal monitoring method by referring to the user's past game play history during monitoring. The optimal monitoring method can include, but is not limited to, a method based on the user's past play history or a method based on the user's current progress. For example, the monitoring unit can prioritize monitoring the progress of games that the user has frequently played in the past. The monitoring unit can also customize a monitoring method for a specific game title based on the user's past play history. The monitoring unit can also analyze the user's past play time periods and suggest an optimal monitoring time. This allows the optimal monitoring method to be provided based on the user's past play history. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's past play history data into a generation AI and cause the generation AI to select an optimal monitoring method.

[0055] The monitoring unit can analyze the user's current game progress during monitoring and select the optimal monitoring means. For example, the monitoring unit can analyze the user's current game progress during monitoring and select the optimal monitoring means. The optimal monitoring means includes, but is not limited to, real-time data collection, log analysis, and the like. For example, the monitoring unit can perform real-time monitoring when the user is fighting a boss. The monitoring unit can also perform periodic monitoring when the user is exploring. The monitoring unit can also pause monitoring when the user is taking a break. This allows appropriate monitoring to be performed according to the user's current game progress. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's game progress data into a generation AI and cause the generation AI to select the optimal monitoring means.

[0056] The monitoring unit can select an optimal monitoring method during monitoring by taking into account the user's geographical location information. For example, the monitoring unit selects an optimal monitoring method during monitoring by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the monitoring unit may prioritize monitoring game information related to that area. The monitoring unit can also prioritize monitoring nearby game events and store information based on the user's location information. The monitoring unit can also prioritize monitoring area-specific strategy information based on the user's location information. This allows for providing an optimal monitoring method based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's location information data into a generation AI and cause the generation AI to select an optimal monitoring method.

[0057] The monitoring unit may analyze the user's social media activity during monitoring and suggest monitoring methods. For example, the monitoring unit may analyze the user's social media activity during monitoring and suggest monitoring methods. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of comments. For example, the monitoring unit may monitor related strategy information based on game information shared by the user on social media. The monitoring unit may also analyze the user's social media activity and monitor information that may be of interest to the user. The monitoring unit may also monitor related information based on the activity of the user's friends on social media. This may provide an optimal monitoring method based on the user's social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's social media data into a generation AI and have the generation AI suggest monitoring methods.

[0058] The feedback unit can select an optimal feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit can select an optimal feedback method by referring to the user's past feedback history when providing feedback. The optimal feedback method can include, but is not limited to, a method based on the user's past feedback history or a method based on the user's current progress. For example, the feedback unit can suggest an optimal feedback method based on feedback previously provided by the user. The feedback unit can also prioritize feedback of a specific information format (text, audio, image) based on the user's past feedback history. The feedback unit can also analyze the user's past feedback and improve the feedback method. This allows the optimal feedback method to be provided based on the user's past feedback history. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's past feedback data into a generation AI and cause the generation AI to select an optimal feedback method.

[0059] The feedback unit can analyze the user's current game progress at the time of feedback and select the optimal feedback means. For example, the feedback unit can analyze the user's current game progress at the time of feedback and select the optimal feedback means. The optimal feedback means includes, but is not limited to, real-time feedback and periodic feedback. For example, the feedback unit can provide real-time feedback when the user is fighting a boss. The feedback unit can also provide periodic feedback when the user is exploring. The feedback unit can also pause feedback when the user is taking a break. This allows appropriate feedback to be provided according to the user's current progress. Some or all of the above-described processing in the feedback unit can be performed using, or without, an AI. For example, the feedback unit can input user progress data to a generation AI and cause the generation AI to select the optimal feedback means.

[0060] The feedback unit can select an optimal feedback method by taking into account the user's geographical location information when providing feedback. For example, the feedback unit selects an optimal feedback method by taking into account the user's geographical location information when providing feedback. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the feedback unit may prioritize feedback of game information related to that area. The feedback unit can also prioritize feedback of nearby game events and store information based on the user's location information. The feedback unit can also prioritize feedback of area-specific strategy information based on the user's location information. This allows the optimal feedback method to be provided based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, an AI. For example, the feedback unit may input the user's location information data into a generation AI and cause the generation AI to select an optimal feedback method.

[0061] The feedback unit may analyze the user's social media activity and suggest a means of providing feedback when providing feedback. For example, the feedback unit may analyze the user's social media activity and suggest a means of providing feedback when providing feedback. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of comments. For example, the feedback unit may provide feedback on related strategy information based on game information shared by the user on social media. The feedback unit may also analyze the user's social media activity and provide feedback on information that may be of interest to the user. The feedback unit may also provide feedback on related information based on the activity of the user's friends on social media. This allows for providing an optimal feedback method based on the user's social media activity. Some or all of the above-described processing by the feedback unit may be performed using, or without, AI. For example, the feedback unit may input the user's social media data into a generation AI and cause the generation AI to suggest a means of providing feedback.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The analysis unit can also learn the user's play style and provide individual strategies. For example, the analysis unit can analyze the user's preferred strategies in the past and suggest optimal strategies based on that analysis. Furthermore, if the user frequently uses a particular character or item, the analysis unit can generate strategies that utilize that character or item. Furthermore, the analysis unit can provide effective strategies in a short amount of time, taking into account the user's play time and frequency. This allows users to obtain strategy information that matches their play style and enjoy the game more.

[0064] The reception unit can also automatically suggest related past walkthrough information based on the user's input. For example, if a user inputs "I can't beat the level 10 boss," the reception unit will search for and suggest walkthrough information from other users who have had similar problems in the past. The reception unit can also provide related videos and tutorials based on the information entered by the user. Furthermore, the reception unit can display feedback and ratings provided by other users based on the information entered by the user. This allows the user to obtain walkthrough information from multiple perspectives.

[0065] The providing unit can also suggest the next goal or task to be challenged based on the user's progress. For example, if the user has cleared a specific level, the providing unit can suggest the next level to be challenged or the item to be acquired. The providing unit can also suggest a training method for the user to improve a specific skill or ability. Furthermore, the providing unit can set the next goal to be aimed for based on the results and progress the user has achieved in the game. This allows the user to always have new goals to progress through the game.

[0066] The monitoring unit can also analyze a user's gameplay behavior in real time and detect abnormal behavior. For example, if a user suddenly adopts an unusual playing style, the monitoring unit can detect the change and provide appropriate advice. Also, if a user repeatedly fails at a particular point, the monitoring unit can identify the cause and suggest improvements. Furthermore, if a user plays for a long period of time, the monitoring unit can display a message encouraging the user to take a break. This can optimize the user's gameplay and provide a better experience.

[0067] The reception unit can also automatically suggest related community information based on the user's input. For example, if a user inputs "I can't beat the level 10 boss," the reception unit will search for and suggest information from related online forums and social networking groups. The reception unit can also display walkthrough videos and tutorials provided by other users based on the information entered by the user. Furthermore, the reception unit can also provide information on related events and campaigns based on the information entered by the user. This allows the user to obtain walkthrough information from multiple perspectives.

[0068] The providing unit can also customize the format of the information to be provided based on the user's past feedback. For example, if the user has previously preferred text-format information, the providing unit can preferentially provide walkthrough information in text format. Also, if the user has previously preferred video-format information, the providing unit can provide walkthrough information in video format. Furthermore, if the user has previously preferred information about a specific character or item, the providing unit can preferentially provide information about that character or item. This makes it possible to provide information in the optimal format according to the user's preferences.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit receives information about the game the user is playing. The information about the game the user is playing includes the game title, current progress, and any problems the user is having. The reception unit can receive information in text, audio, or image format. For example, it analyzes the text information entered by the user to determine the game progress. Audio information is converted into text using voice recognition technology, and image information is analyzed using image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit generates strategies for the points where the user is having trouble based on the game database and past strategy information. It uses data mining technology to extract relevant information from the game database, statistical analysis technology to analyze past strategy information, and machine learning algorithms to generate strategies that suit the user's play style. Step 3: The provider provides strategy information based on the information obtained by the analyzer. This is often done in real time. For example, the strategy information generated by the generation AI is displayed on the user's smartphone or PC, providing customized information according to the user's progress. It also provides advice in real time, monitors the progress of the game the user is playing, and provides strategy information at the appropriate time.

[0071] (Example 2) An information provision system according to an embodiment of the present invention utilizes a generation AI to provide information for game progress. The information provision system inputs information about the game a user is playing, and the generation AI analyzes the information to generate and provide information useful for game progress to the user. This mechanism allows the user to obtain specific game strategy information for game progress. For example, the information provision system inputs detailed information about the game the user is playing, such as the title of the game, the user's current progress, and the problem areas. For example, the user inputs information such as "I can't beat the boss at level 10 in game X." This information is input to the generation AI. The information provision system then analyzes the input information using the generation AI. The generation AI generates a strategy for the problem area based on a game database and past game strategy information. For example, the generation AI generates specific game strategy information such as "To beat the boss at level 10, use a specific item." The information provision system then provides the generated game strategy information to the user. For example, the game strategy information generated by the generation AI is displayed on the user's smartphone or PC. Based on this information, the user can progress through the game advantageously. This allows the information provision system to obtain specific game strategy information for game progress advantageously. For example, detailed strategy information such as how to obtain a specific item or the weaknesses of a boss can be obtained. In addition, because the generation AI proposes new strategies based on past strategy information, the user can always obtain the latest strategy information. This allows the information provision system to obtain specific strategy information that will allow the user to progress through the game with an advantage. For example, detailed strategy information such as how to obtain a specific item or the weaknesses of a boss can be obtained. In addition, because the generation AI proposes new strategies based on past strategy information, the user can always obtain the latest strategy information.

[0072] An information provision system according to an embodiment includes a receiving unit, an analysis unit, and a providing unit. The receiving unit receives information about a game being played by a user. The information about the game being played by a user includes, for example, the game title, current progress, and difficulty points, but is not limited to these examples. The receiving unit receives information in, for example, text format. The receiving unit can also receive information in audio format. The receiving unit can also receive information in image format. For example, the receiving unit analyzes text information entered by a user to determine the progress of the game. The audio information is converted into text using audio recognition technology. The image information is analyzed using image recognition technology. The analysis unit analyzes the information received by the receiving unit using a generation AI. The analysis is performed using, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to these examples. For example, the analysis unit generates a strategy for a difficulty point based on a game database and past strategy information. The analysis unit extracts related information from the game database using, for example, data mining technology. The analysis unit can also analyze past strategy information using statistical analysis technology. The analysis unit can also use a machine learning algorithm to generate strategies tailored to the user's play style. The providing unit provides strategy information based on the information obtained by the analysis unit. The provision can be, for example, in real time, but is not limited to this example. For example, the providing unit displays the strategy information generated by the generation AI on the user's smartphone or PC. The providing unit can also provide customized information tailored to the user's progress. The providing unit can also provide real-time advice. For example, the providing unit monitors the progress of the game being played by the user and provides strategy information at appropriate times. This allows the information providing system according to the embodiment to obtain specific strategy information to help the user advance through the game. For example, detailed strategy information, such as how to obtain specific items or the weaknesses of bosses, can be obtained. Furthermore, since the generation AI suggests new strategies based on past strategy information, the user can always obtain the latest strategy information.

[0073] The reception unit can accept information in text, audio, or image format. The reception unit accepts information in, for example, text format. Examples of text formats include, but are not limited to, text files and chat messages. The reception unit can also accept information in audio format. Examples of audio formats include, but are not limited to, audio files and real-time audio input. The reception unit can also accept information in image format. Examples of image formats include, but are not limited to, image files and screenshots. This allows the user to input information in various formats. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input an audio file to a generation AI and have the generation AI convert the audio data into text data.

[0074] The analysis unit can analyze information including official guidebooks or user community information. The analysis unit, for example, analyzes information from official guidebooks. Official guidebooks include, for example, but are not limited to, walkthrough guides published by game manufacturers. The analysis unit can also analyze information from user communities. User community information includes, for example, but is not limited to, online forums and social networking groups. This allows for analysis based on a variety of information sources, thereby providing more accurate walkthrough information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from official guidebooks into a generation AI and cause the generation AI to generate walkthrough information.

[0075] The providing unit can provide advice in real time. For example, the providing unit provides advice in real time. Real-time advice includes, but is not limited to, updates every second and real-time feedback. For example, the providing unit monitors the progress of a game being played by a user and provides strategy information at an appropriate timing. The providing unit can also use a generation AI to provide advice in real time. For example, the providing unit inputs the user's progress into the generation AI and causes the generation AI to execute real-time advice. This allows the user to obtain strategy information in real time.

[0076] The providing unit can provide customized information according to the user's progress. The providing unit provides, for example, customized information according to the user's progress. Customized information includes, for example, personalized information based on the user's progress, but is not limited to such examples. For example, the providing unit monitors the progress of a game being played by the user and provides strategy information at an appropriate time. The providing unit can also use a generation AI to provide customized information according to the user's progress. For example, the providing unit can input the user's progress to the generation AI and cause the generation AI to execute the customized information. This makes it possible to provide appropriate strategy information according to the user's progress.

[0077] The information providing system includes a monitoring unit that monitors the user's progress. The monitoring unit monitors the user's progress in real time, for example. Real-time monitoring includes, but is not limited to, real-time data collection, log analysis, and the like. For example, the monitoring unit grasps the progress of a game being played by the user in real time and provides information at an appropriate timing. The monitoring unit can also use a generation AI to monitor the user's progress. For example, the monitoring unit can input the user's progress into the generation AI and cause the generation AI to perform real-time monitoring. This allows the user's progress to be grasped in real time and appropriate information to be provided.

[0078] The information provision system includes a feedback unit that collects user feedback. The feedback unit collects, for example, user feedback. Examples of collected feedback include, but are not limited to, questionnaires, user reviews, and usage logs. For example, the feedback unit improves the system based on the feedback provided by the user. The feedback unit can also use a generation AI to collect user feedback. For example, the feedback unit can input user feedback into the generation AI and have the generation AI analyze the feedback. This makes it possible to improve the system based on user feedback.

[0079] The reception unit can estimate the user's emotion and adjust the timing of receiving information based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of receiving information based on the estimated user emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving information to provide the user with time to relax. Furthermore, if the user is excited, the reception unit can immediately receive information and quickly start analysis. Furthermore, if the user is tired, the reception unit can adjust the timing of receiving information to take into account rest periods. This allows information to be received at an appropriate time depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0080] The reception unit can analyze the user's past game play history and select the optimal reception method. The reception unit, for example, analyzes the user's past game play history and selects the optimal reception method. The past game play history includes, but is not limited to, in-game logs, play time, and achievement status. For example, the reception unit preferentially suggests input methods (text, voice, image) that the user has frequently used in the past. The reception unit can also customize the reception method for a specific game title based on the user's past play history. The reception unit can also analyze the user's past play time periods and suggest the optimal reception time. This makes it possible to provide the optimal reception method based on the user's past play history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past play history data into a generation AI and have the generation AI select the optimal reception method.

[0081] The reception unit may perform filtering based on the user's current game progress and areas of interest when receiving information. For example, the reception unit may perform filtering based on the user's current game progress and areas of interest when receiving information. Examples of filtering include, but are not limited to, filtering based on progress and areas of interest. For example, the reception unit may receive only relevant information based on the progress of the game the user is currently playing. The reception unit may also filter information based on the user's areas of interest (e.g., specific characters or items). The reception unit may also grasp the user's current game progress in real time and receive appropriate information. This allows the reception of information according to the user's current situation and interests. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's progress data to a generation AI and cause the generation AI to perform filtering.

[0082] The reception unit can select the optimal reception means depending on the user's input method when receiving information. For example, the reception unit selects the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving information. Optimal reception means include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit accepts the information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the information using natural language processing technology. Furthermore, if the user selects image input, the reception unit can analyze the information using image recognition technology. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0083] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of information to be received based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the reception unit can prioritize receiving information of high importance. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving information that requires a quick response. In this way, by determining the priority of information according to the user's emotion, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the information.

[0084] The reception unit may prioritize receiving highly relevant information based on the user's geographical location information when receiving information. For example, the reception unit may prioritize receiving highly relevant information taking the user's geographical location information into consideration when receiving information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit may prioritize receiving game information related to that area. The reception unit may also prioritize receiving information about nearby game events and stores based on the user's location information. The reception unit may also prioritize receiving area-specific strategy information based on the user's location information. This allows for providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's location information data into a generation AI and cause the generation AI to select highly relevant information.

[0085] The reception unit may analyze the user's social media activity and receive related information when receiving information. For example, the reception unit may analyze the user's social media activity and receive related information when receiving information. Social media activity may include, but is not limited to, post content, the number of likes, and the number of comments. For example, the reception unit may receive related strategy information based on game information shared by the user on social media. The reception unit may also analyze the user's social media activity and receive information that may be of interest to the user. The reception unit may also receive related information based on the activity of the user's friends on social media. This allows related information to be provided based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's social media data into a generation AI and cause the generation AI to select related information.

[0086] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving information. Past feedback includes, but is not limited to, survey results, user reviews, etc. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially accept specific information formats (text, audio, image) based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0087] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can, for example, estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. This makes it possible to provide appropriate analysis results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the game during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the game during analysis. Examples of game importance include, but are not limited to, the number of players, sales, and review ratings. For example, the analysis unit provides detailed strategy information for important boss battles. The analysis unit can also provide concise strategy information for normal enemy battles. The analysis unit can also provide detailed analysis results for events that have a significant impact on the progress of the game. This allows for providing appropriate analysis results according to the importance of the game. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit can input game importance data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the game category during analysis. For example, the analysis unit applies different analysis algorithms depending on the game category during analysis. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies an analysis algorithm related to character development in the case of an RPG game. Furthermore, the analysis unit can apply an analysis algorithm related to combat techniques in the case of an action game. Furthermore, the analysis unit can apply an analysis algorithm related to solutions in the case of a puzzle game. This makes it possible to provide appropriate analysis results depending on the game category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input game category data into a generation AI and cause the generation AI to apply the analysis algorithm.

[0090] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, log data, analysis reports, etc. For example, the analysis unit can adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy. The analysis unit can also suggest an optimal analysis method based on the user's past analysis results. This can improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. This allows for providing an appropriate analysis result according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0092] The analysis unit can determine the analysis priority based on the release date of the game during analysis. For example, the analysis unit determines the analysis priority based on the release date of the game during analysis. Game release dates include, but are not limited to, new games and update dates. For example, the analysis unit prioritizes analysis of new games. The analysis unit can also lower the analysis priority of games that have been released for some time. The analysis unit can also adjust the analysis priority based on the release date of the game being played by the user. This allows for providing appropriate analysis results according to the release date of the game. Some or all of the above-described processing by the analysis unit may be performed using, or without, an AI. For example, the analysis unit can input game release date data into the generation AI and have the generation AI determine the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the games during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the games during analysis. Game relevance includes, but is not limited to, genre and story relevance. For example, the analysis unit prioritizes analysis of information related to the game currently being played by the user. The analysis unit can also prioritize analysis of information related to games in which the user is interested. The analysis unit can also prioritize analysis of highly relevant information based on the user's past play history. This allows for providing appropriate analysis results according to the relevance of the games. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input game relevance data to a generation AI and cause the generation AI to adjust the analysis order.

[0094] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced users. For example, the analysis unit can provide a novice user with a concise analysis result that avoids technical terms. The analysis unit can also provide an advanced user with an analysis result that includes detailed technical terms. The analysis unit can also adjust the way the analysis result is presented according to the user's level of expertise. This allows for the provision of an appropriate analysis result according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0095] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the presentation method of the information to be provided based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the providing unit can provide detailed information when the user is relaxed. Furthermore, the providing unit can provide concise information that focuses on the main points when the user is in a hurry. Furthermore, the providing unit can provide visually appealing information when the user is excited. This makes it possible to provide appropriate information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the information.

[0096] The providing unit can adjust the level of detail of the information to be provided based on the progress of the game when providing the information. For example, the providing unit adjusts the level of detail of the information to be provided based on the progress of the game when providing the information. The level of detail of the information includes, but is not limited to, summary information, detailed information, etc. For example, the providing unit provides basic information in the early stages of the game. The providing unit can also provide detailed strategy information in the middle stages of the game. The providing unit can also provide specific strategies and techniques in the late stages of the game. This makes it possible to provide appropriate information according to the progress of the game. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input game progress data to a generating AI and cause the generating AI to adjust the level of detail of the information.

[0097] The providing unit can apply different providing algorithms depending on the game category when providing the game. For example, the providing unit applies different providing algorithms depending on the game category when providing the game. Examples of providing algorithms include, but are not limited to, recommendation algorithms and personalized algorithms. For example, the providing unit provides information on character development in the case of an RPG game. Furthermore, the providing unit can provide information on combat techniques in the case of an action game. Furthermore, the providing unit can provide information on solutions in the case of a puzzle game. This makes it possible to provide appropriate information depending on the game category. Some or all of the above-described processing in the providing unit may be performed using, or without using, AI. For example, the providing unit can input game category data into a generation AI and cause the generation AI to apply the providing algorithm.

[0098] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. For example, the providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. Past provision results include, but are not limited to, log data, user feedback, etc. For example, the providing unit can adjust the provision algorithm based on feedback provided by the user in the past. The providing unit can also analyze the user's past provision results and improve the accuracy. The providing unit can also suggest an optimal provision method based on the user's past provision results. This can improve the accuracy of the provision based on the user's past provision results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0099] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the information to be provided based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is in a hurry, the providing unit can provide short, concise information. When the user is relaxed, the providing unit can also provide detailed information. When the user is excited, the providing unit can also provide visually appealing information. This allows appropriate information to be provided according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0100] The providing unit can determine the priority of information to be provided based on the release date of the game at the time of providing the information. For example, the providing unit determines the priority of information to be provided based on the release date of the game at the time of providing the information. The priority of information includes, but is not limited to, the release date, importance rating, etc. For example, the providing unit prioritizes providing information for a new game. The providing unit can also lower the priority of providing information for a game that has been released for some time. The providing unit can also adjust the priority of providing information based on the release date of the game being played by the user. This makes it possible to provide appropriate information according to the release date of the game. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input game release date data to a generating AI and cause the generating AI to determine the priority of information.

[0101] The providing unit can adjust the order of information to be provided based on the relevance of the game when providing the information. For example, the providing unit adjusts the order of information to be provided based on the relevance of the game when providing the information. The order of information includes, but is not limited to, an order based on relevance or importance. For example, the providing unit may prioritize providing information related to a game the user is playing. The providing unit may also prioritize providing information related to a game in which the user is interested. The providing unit may also prioritize providing highly relevant information based on the user's past play history. This makes it possible to provide appropriate information according to the relevance of the game. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input game relevance data to a generating AI and cause the generating AI to adjust the order of the information.

[0102] The providing unit can adjust the use of technical terminology in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit adjusts the use of technical terminology in the information to be provided according to the user's level of expertise when providing the information. The use of technical terminology includes, but is not limited to, terms for beginners, intermediate users, and advanced users. For example, the providing unit can provide concise information that avoids technical terminology to beginner users. The providing unit can also provide information including detailed technical terminology to advanced users. The providing unit can also adjust the way information is presented according to the user's level of expertise. This allows appropriate information to be provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology.

[0103] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the monitoring method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the monitoring unit reduces the monitoring frequency to reduce the burden. The monitoring unit can also perform detailed monitoring when the user is relaxed. The monitoring unit can also enhance real-time monitoring when the user is excited. This allows appropriate monitoring to be performed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the monitoring method.

[0104] The monitoring unit can select an optimal monitoring method by referring to the user's past game play history during monitoring. For example, the monitoring unit can select an optimal monitoring method by referring to the user's past game play history during monitoring. The optimal monitoring method can include, but is not limited to, a method based on the user's past play history or a method based on the user's current progress. For example, the monitoring unit can prioritize monitoring the progress of games that the user has frequently played in the past. The monitoring unit can also customize a monitoring method for a specific game title based on the user's past play history. The monitoring unit can also analyze the user's past play time periods and suggest an optimal monitoring time. This allows the optimal monitoring method to be provided based on the user's past play history. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's past play history data into a generation AI and cause the generation AI to select an optimal monitoring method.

[0105] The monitoring unit can analyze the user's current game progress during monitoring and select the optimal monitoring means. For example, the monitoring unit can analyze the user's current game progress during monitoring and select the optimal monitoring means. The optimal monitoring means includes, but is not limited to, real-time data collection, log analysis, and the like. For example, the monitoring unit can perform real-time monitoring when the user is fighting a boss. The monitoring unit can also perform periodic monitoring when the user is exploring. The monitoring unit can also pause monitoring when the user is taking a break. This allows appropriate monitoring to be performed according to the user's current game progress. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's game progress data into a generation AI and cause the generation AI to select the optimal monitoring means.

[0106] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines monitoring priorities based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the monitoring unit prioritizes monitoring of information of high importance. Furthermore, when the user is relaxed, the monitoring unit can prioritize monitoring of detailed information. Furthermore, when the user is in a hurry, the monitoring unit can prioritize monitoring of information requiring a quick response. This enables appropriate monitoring according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI determine the monitoring priorities.

[0107] The monitoring unit can select an optimal monitoring method during monitoring by taking into account the user's geographical location information. For example, the monitoring unit selects an optimal monitoring method during monitoring by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the monitoring unit may prioritize monitoring game information related to that area. The monitoring unit can also prioritize monitoring nearby game events and store information based on the user's location information. The monitoring unit can also prioritize monitoring area-specific strategy information based on the user's location information. This allows for providing an optimal monitoring method based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's location information data into a generation AI and cause the generation AI to select an optimal monitoring method.

[0108] The monitoring unit may analyze the user's social media activity during monitoring and suggest monitoring methods. For example, the monitoring unit may analyze the user's social media activity during monitoring and suggest monitoring methods. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of comments. For example, the monitoring unit may monitor related strategy information based on game information shared by the user on social media. The monitoring unit may also analyze the user's social media activity and monitor information that may be of interest to the user. The monitoring unit may also monitor related information based on the activity of the user's friends on social media. This may provide an optimal monitoring method based on the user's social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's social media data into a generation AI and have the generation AI suggest monitoring methods.

[0109] The feedback unit can estimate the user's emotion and adjust the feedback method based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion and adjusts the feedback method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the feedback unit can provide concise and to-the-point feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is excited, the feedback unit can provide visually appealing feedback. This allows appropriate feedback to be provided according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback method.

[0110] The feedback unit can select an optimal feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit can select an optimal feedback method by referring to the user's past feedback history when providing feedback. The optimal feedback method can include, but is not limited to, a method based on the user's past feedback history or a method based on the user's current progress. For example, the feedback unit can suggest an optimal feedback method based on feedback previously provided by the user. The feedback unit can also prioritize feedback of a specific information format (text, audio, image) based on the user's past feedback history. The feedback unit can also analyze the user's past feedback and improve the feedback method. This allows the optimal feedback method to be provided based on the user's past feedback history. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's past feedback data into a generation AI and cause the generation AI to select an optimal feedback method.

[0111] The feedback unit can analyze the user's current game progress at the time of feedback and select the optimal feedback means. For example, the feedback unit can analyze the user's current game progress at the time of feedback and select the optimal feedback means. The optimal feedback means includes, but is not limited to, real-time feedback and periodic feedback. For example, the feedback unit can provide real-time feedback when the user is fighting a boss. The feedback unit can also provide periodic feedback when the user is exploring. The feedback unit can also pause feedback when the user is taking a break. This allows appropriate feedback to be provided according to the user's current progress. Some or all of the above-described processing in the feedback unit can be performed using, or without, an AI. For example, the feedback unit can input user progress data to a generation AI and cause the generation AI to select the optimal feedback means.

[0112] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The feedback unit can, for example, estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is feeling stressed, the feedback unit can prioritize feedback of information of high importance. Furthermore, if the user is relaxed, the feedback unit can prioritize feedback of detailed information. Furthermore, if the user is in a hurry, the feedback unit can prioritize feedback of information requiring a quick response. This makes it possible to provide appropriate feedback according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of feedback.

[0113] The feedback unit can select an optimal feedback method by taking into account the user's geographical location information when providing feedback. For example, the feedback unit selects an optimal feedback method by taking into account the user's geographical location information when providing feedback. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the feedback unit may prioritize feedback of game information related to that area. The feedback unit can also prioritize feedback of nearby game events and store information based on the user's location information. The feedback unit can also prioritize feedback of area-specific strategy information based on the user's location information. This allows the optimal feedback method to be provided based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, an AI. For example, the feedback unit may input the user's location information data into a generation AI and cause the generation AI to select an optimal feedback method.

[0114] The feedback unit may analyze the user's social media activity and suggest a means of providing feedback when providing feedback. For example, the feedback unit may analyze the user's social media activity and suggest a means of providing feedback when providing feedback. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of comments. For example, the feedback unit may provide feedback on related strategy information based on game information shared by the user on social media. The feedback unit may also analyze the user's social media activity and provide feedback on information that may be of interest to the user. The feedback unit may also provide feedback on related information based on the activity of the user's friends on social media. This allows for providing an optimal feedback method based on the user's social media activity. Some or all of the above-described processing by the feedback unit may be performed using, or without, AI. For example, the feedback unit may input the user's social media data into a generation AI and cause the generation AI to suggest a means of providing feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, provision unit, monitoring unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive information about a game being played by a user using the reception device 38 of the smart device 14. For example, the analysis unit can perform information analysis using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the user with strategy information generated using the output device 40 of the smart device 14. For example, the monitoring unit can monitor the user's progress in real time using the camera 42 and communication I / F 44 of the smart device 14. For example, the feedback unit can collect user feedback using the reception device 38 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, monitoring unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive information about the game being played by the user using the microphone 238 of the smart glasses 214. For example, the analysis unit can perform information analysis using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the user with strategy information generated using the speaker 240 of the smart glasses 214. For example, the monitoring unit can monitor the user's progress in real time using the camera 42 and communication I / F 44 of the smart glasses 214. For example, the feedback unit can collect user feedback using the microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, monitoring unit, and feedback unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive information about the game being played by the user using the microphone 238 of the headset-type terminal 314. For example, the analysis unit can perform information analysis using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the user with strategy information generated using the speaker 240 of the headset-type terminal 314. For example, the monitoring unit can monitor the user's progress in real time using the camera 42 and communication I / F 44 of the headset-type terminal 314. For example, the feedback unit can collect user feedback using the microphone 238 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, monitoring unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive information about the game being played by the user using the microphone 238 of the robot 414. For example, the analysis unit can perform information analysis using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the user with strategy information generated using the speaker 240 of the robot 414. For example, the monitoring unit can monitor the user's progress in real time using the camera 42 and communication I / F 44 of the robot 414. For example, the feedback unit can collect user feedback using the microphone 238 of the robot 414.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The analysis unit can also learn the user's play style and provide individual strategies. For example, the analysis unit can analyze the user's preferred strategies in the past and suggest optimal strategies based on that analysis. Furthermore, if the user frequently uses a particular character or item, the analysis unit can generate strategies that utilize that character or item. Furthermore, the analysis unit can provide effective strategies in a short amount of time, taking into account the user's play time and frequency. This allows users to obtain strategy information that matches their play style and enjoy the game more.

[0117] The reception unit can also automatically suggest related past walkthrough information based on the user's input. For example, if a user inputs "I can't beat the level 10 boss," the reception unit will search for and suggest walkthrough information from other users who have had similar problems in the past. The reception unit can also provide related videos and tutorials based on the information entered by the user. Furthermore, the reception unit can display feedback and ratings provided by other users based on the information entered by the user. This allows the user to obtain walkthrough information from multiple perspectives.

[0118] The analysis unit can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide concise, to-the-point strategy information. If the user is relaxed, the analysis unit can provide detailed strategy information. Furthermore, if the user is excited, the analysis unit can provide the analysis results using visually appealing graphics and animations. This makes it possible to provide strategy information in an optimal form according to the user's emotions.

[0119] The providing unit can also suggest the next goal or task to be challenged based on the user's progress. For example, if the user has cleared a specific level, the providing unit can suggest the next level to be challenged or the item to be acquired. The providing unit can also suggest a training method for the user to improve a specific skill or ability. Furthermore, the providing unit can set the next goal to be aimed for based on the results and progress the user has achieved in the game. This allows the user to always have new goals to progress through the game.

[0120] The providing unit can also estimate the user's emotions and adjust the timing of providing information based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can temporarily refrain from providing information and provide time for the user to relax. Also, if the user is excited, the providing unit can immediately provide information to support the user's progress in the game. Furthermore, if the user is tired, the providing unit can display a message encouraging the user to take a break. This makes it possible to provide information at the optimal timing according to the user's emotions.

[0121] The monitoring unit can also analyze a user's gameplay behavior in real time and detect abnormal behavior. For example, if a user suddenly adopts an unusual playing style, the monitoring unit can detect the change and provide appropriate advice. Also, if a user repeatedly fails at a particular point, the monitoring unit can identify the cause and suggest improvements. Furthermore, if a user plays for a long period of time, the monitoring unit can display a message encouraging the user to take a break. This can optimize the user's gameplay and provide a better experience.

[0122] The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide positive feedback preferentially. If the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is excited, the feedback unit can provide visually appealing feedback. In this way, it is possible to provide optimal feedback according to the user's emotions.

[0123] The reception unit can also automatically suggest related community information based on the user's input. For example, if a user inputs "I can't beat the level 10 boss," the reception unit will search for and suggest information from related online forums and social networking groups. The reception unit can also display walkthrough videos and tutorials provided by other users based on the information entered by the user. Furthermore, the reception unit can also provide information on related events and campaigns based on the information entered by the user. This allows the user to obtain walkthrough information from multiple perspectives.

[0124] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize analyzing information of high importance. Also, if the user is relaxed, the analysis unit can prioritize analyzing detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing information that requires a quick response. This makes it possible to provide optimal analysis results according to the user's emotions.

[0125] The providing unit can also customize the format of the information to be provided based on the user's past feedback. For example, if the user has previously preferred text-format information, the providing unit can preferentially provide walkthrough information in text format. Also, if the user has previously preferred video-format information, the providing unit can provide walkthrough information in video format. Furthermore, if the user has previously preferred information about a specific character or item, the providing unit can preferentially provide information about that character or item. This makes it possible to provide information in the optimal format according to the user's preferences.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The reception unit receives information about the game the user is playing. The information about the game the user is playing includes the game title, current progress, and any problems the user is having. The reception unit can receive information in text, audio, or image format. For example, it analyzes the text information entered by the user to determine the game progress. Audio information is converted into text using voice recognition technology, and image information is analyzed using image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit generates strategies for the points where the user is having trouble based on the game database and past strategy information. It uses data mining technology to extract relevant information from the game database, statistical analysis technology to analyze past strategy information, and machine learning algorithms to generate strategies that suit the user's play style. Step 3: The provider provides strategy information based on the information obtained by the analyzer. This is often done in real time. For example, the strategy information generated by the generation AI is displayed on the user's smartphone or PC, providing customized information according to the user's progress. It also provides advice in real time, monitors the progress of the game the user is playing, and provides strategy information at the appropriate time.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 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 reception unit that receives information; an analysis unit that analyzes the information received by the reception unit; a providing unit that provides strategy information based on the information obtained by the analyzing unit; Equipped with A system characterized by:

2. The reception unit Accepts information in the form of text, audio, and images 2. The system of claim 1.

3. The analysis unit Analyze information from official guidebooks or user communities 2. The system of claim 1.

4. The providing unit Providing real-time advice 2. The system of claim 1.

5. The providing unit Providing customized information based on the user's progress 2. The system of claim 1.

6. Equipped with a monitoring unit that monitors the user's progress 2. The system of claim 1.

7. A feedback section is provided to collect user feedback.

2. The system of claim 1.

8. The reception unit Estimates user emotions and adjusts the timing of information reception based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Analyze the user's past gameplay history and select the optimal reception method 2. The system of claim 1.

10. The reception unit When receiving information, filter it based on the user's current game progress and interests.

2. The system of claim 1.

Images