system

Abstract

The system according to the embodiment aims to efficiently match professional gamers with trainers. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a notification unit, and an adjustment unit. The input unit inputs information about professional gamers and trainers. The analysis unit analyzes the information input by the input unit and performs matching. The notification unit issues a notification based on the analysis results obtained by the analysis unit. The adjustment unit manages a schedule based on the matching notified by the notification unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, professional gamers had to rely on personal connections to find practice partners, making it difficult for players with fewer connections to find others.

[0005] The system according to the embodiment aims to efficiently match professional gamers with trainers. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a notification unit, and an adjustment unit. The input unit inputs information about professional gamers and trainers. The analysis unit analyzes the information input by the input unit and performs matching. The notification unit issues a notification based on the analysis results obtained by the analysis unit. The adjustment unit manages a schedule based on the matching notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match professional gamers with trainers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching system according to an embodiment of the present invention matches professional gamers (recruiters) with trainers (applicants). In this matching system, professional gamers input information for recruiting practice partners, and trainers input their own skills and desired practice content. AI analyzes the information to determine optimal matching. The AI ​​compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. When a match is made, notifications are sent to both parties, and practice schedules are automatically adjusted. This service allows professional gamers to efficiently find practice partners and trainers to gain opportunities to utilize their skills. For example, professional gamers input information for recruiting practice partners, such as the type of game, desired practice content, practice frequency, and time of day. Next, trainers input their own skills and desired practice content. For example, trainers input information such as their skill level, preferred game types, and desired rewards. AI analyzes this information to determine optimal matching. The AI ​​compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. For example, an algorithm is used to learn from past match data and create optimal matches. When a match is made, both parties are notified and the practice schedule is automatically adjusted. For example, a system will be introduced that automatically adjusts both parties' schedules using a calendar function. This service allows professional gamers to efficiently find practice partners and trainers to have opportunities to utilize their skills. This allows the matching system to efficiently match professional gamers and trainers and adjust schedules.

[0029] A matching system according to an embodiment includes an input unit for inputting information about a professional gamer and information about a trainer, an analysis unit, a notification unit, and an adjustment unit. The input unit inputs information for the professional gamer to seek practice partners. The information input by the professional gamer includes, for example, the type of game, desired practice content, practice frequency and time period, etc. The input unit provides, for example, an interface through which the professional gamer can input the desired practice content in detail. The input unit also allows the trainer to input their own skills and desired practice content. The information input by the trainer includes, for example, their skill level, the type of game they are good at, and the desired reward. The input unit provides, for example, an interface through which the trainer can input their own skills in detail. The analysis unit analyzes the information input by the input unit and performs optimal matching. The analysis unit, for example, compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. The analysis unit, for example, learns past matching data and uses an algorithm for optimal matching. The notification unit provides a notification based on the analysis results obtained by the analysis unit. For example, when a match is made, the notification unit sends a notification to the professional gamer and the trainer. The notification unit sends the notification using, for example, email or an app notification. The adjustment unit adjusts schedules based on the match notified by the notification unit. The adjustment unit introduces a mechanism for automatically adjusting the schedules of both parties, for example, using a calendar function. The adjustment unit automatically adjusts the schedules of the professional gamer and the trainer and sets a practice schedule. This allows the matching system according to the embodiment to achieve efficient matching and schedule adjustment between professional gamers and trainers.

[0030] The analysis unit can compare the professional gamer's requirements with the trainer's skills. For example, the analysis unit compares the professional gamer's requirements with the trainer's skills. For example, the analysis unit compares the training content desired by the professional gamer with the training content that the trainer is good at, and recommends the most suitable trainer. The analysis unit can also compare the skill level of the professional gamer with the skill level of the trainer, and recommend a trainer with a matching skill level. Furthermore, the analysis unit can compare the professional gamer's past training history with the trainer's past training history, and recommend the most suitable trainer based on past performance. This makes it possible to find the trainer that best suits the professional gamer's requirements.

[0031] The analysis unit can learn from past matching data. For example, the analysis unit learns from past matching data. For example, based on the past matching data, it learns patterns of successful matching and improves accuracy. The analysis unit can also collect feedback after matching and improve the algorithm. Furthermore, the analysis unit can analyze past matching data, identify causes of unsuccessful matching, and implement improvements. This improves matching accuracy based on past data.

[0032] The adjustment unit can adjust schedules using a calendar function. The adjustment unit adjusts schedules using, for example, a calendar function. For example, the adjustment unit can link with Google (registered trademark) Calendar to automatically adjust the schedules of professional gamers and trainers. The adjustment unit can also use a reminder function to notify them of practice schedules. Furthermore, the adjustment unit can update the schedules of professional gamers and trainers in real time to optimize practice schedules. This makes schedule adjustments automatic and efficient.

[0033] The input unit may include, as information input by the professional gamer, the type of game, desired practice content, and practice frequency or time period. The input unit may, for example, provide an interface that allows the professional gamer to input the details of the practice content they desire. The input unit may also collect data for recommending the most suitable trainer based on the information input by the professional gamer. The input unit may also transmit the information input by the professional gamer to the analysis unit, providing data for the analysis unit to perform optimal matching. This allows for more appropriate matching based on the detailed information of the professional gamer.

[0034] The input unit may include, as information input by the trainer, the trainer's skill level, the type of game the trainer is good at, and the desired reward. The input unit may, for example, provide an interface that allows the trainer to input their own skills in detail. The input unit may also collect data for recommending the most suitable professional gamer based on the information input by the trainer. Furthermore, the input unit may transmit the information input by the trainer to the analysis unit and provide data for the analysis unit to perform optimal matching. This allows for more appropriate matching based on the trainer's detailed information.

[0035] The input unit can analyze the professional gamer's past practice history and provide an auto-completion function when inputting. The input unit can, for example, analyze the professional gamer's past practice history and provide an auto-completion function when inputting. For example, based on the practice content previously input by the professional gamer, similar practice content can be automatically displayed as candidates. It can also automatically suggest frequently used practice time periods based on the professional gamer's past practice history. Furthermore, it can automatically display candidates for related games based on the types of games previously input by the professional gamer. This reduces the effort required for input by completing the input based on the past practice history.

[0036] The input unit can acquire the current gameplay status of the professional gamer in real time and dynamically update the input content. The input unit, for example, acquires the current gameplay status of the professional gamer in real time and dynamically updates the input content. For example, the input unit can automatically acquire the type of game the professional gamer is currently playing and reflect this in the input content. The input unit can also acquire the professional gamer's current playing time in real time and suggest it as a practice time candidate. Furthermore, the input unit can acquire the professional gamer's current in-game progress and automatically suggest appropriate practice content. In this way, by updating the input content based on real-time information, more accurate information can be provided.

[0037] The input unit can prioritize displaying region-specific practice partners by taking into account the geographical location information of the professional gamer. The input unit, for example, prioritizes displaying region-specific practice partners by taking into account the geographical location information of the professional gamer. For example, based on the current location of the professional gamer, it can prioritize displaying nearby practice partners. It can also prioritize displaying trainers active in the same region based on the geographical location information of the professional gamer. Furthermore, it can prioritize displaying trainers participating in region-specific events or tournaments based on the geographical location information of the professional gamer. This allows for efficient matching by displaying region-specific practice partners based on geographical location information.

[0038] The input unit can analyze the social media activity of the professional gamer and automatically input related information. The input unit, for example, analyzes the social media activity of the professional gamer and automatically inputs related information. For example, it analyzes the content of the professional gamer's social media posts and automatically inputs information about the practice content and desired practice partners. It can also automatically suggest trainers suitable as practice partners based on the professional gamer's social media follower information. Furthermore, it can automatically display trainers with whom the professional gamer has interacted in the past based on the professional gamer's social media activity history. This reduces the effort required for input by completing the input based on social media information.

[0039] The input unit can analyze the trainer's past training history and provide an auto-completion function when inputting data. The input unit can, for example, analyze the trainer's past training history and provide an auto-completion function when inputting data. For example, based on training content previously input by the trainer, similar training content can be automatically displayed as candidates. Frequently used training time periods can also be automatically suggested based on the trainer's past training history. Furthermore, based on the types of games previously input by the trainer, related game candidates can also be automatically displayed. This reduces the effort required for input by completing input based on past training history.

[0040] The input unit can evaluate the trainer's current skill level in real time and dynamically update the input content. The input unit, for example, evaluates the trainer's current skill level in real time and dynamically updates the input content. For example, the input unit can automatically obtain the skill level of the game the trainer is currently playing and reflect this in the input content. The input unit can also obtain the trainer's current playing time in real time and suggest it as a training time candidate. Furthermore, the input unit can obtain the trainer's current in-game progress and automatically suggest appropriate training content. In this way, by updating the input content based on real-time information, more accurate information can be provided.

[0041] The input unit can prioritize displaying region-specific practice partners by taking into account the trainer's geographical location information. The input unit can prioritize displaying region-specific practice partners by taking into account the trainer's geographical location information, for example. For example, based on the trainer's current location, nearby practice partners can be prioritized. Also, based on the trainer's geographical location information, professional gamers active in the same region can be prioritized. Furthermore, based on the trainer's geographical location information, professional gamers participating in region-specific events or tournaments can be prioritized. In this way, by displaying region-specific practice partners based on geographical location information, efficient matching can be achieved.

[0042] The input unit can analyze the trainer's social media activity and automatically input related information. The input unit, for example, analyzes the trainer's social media activity and automatically inputs related information. For example, it analyzes the trainer's social media posts and automatically inputs information about training content and desired practice partners. It can also automatically suggest professional gamers suitable as practice partners based on the trainer's social media follower information. Furthermore, it can automatically display professional gamers with whom the trainer has interacted in the past based on the trainer's social media activity history. This reduces the effort required for input by completing the input based on social media information.

[0043] The analysis unit can refer to past matching data and build a feedback loop to improve matching accuracy. The analysis unit, for example, refers to past matching data and builds a feedback loop to improve matching accuracy. For example, the analysis unit learns successful matching patterns based on past matching data and improves accuracy. The analysis unit can also collect feedback after matching and improve the algorithm. Furthermore, the analysis unit can analyze past matching data to identify causes of unsuccessful matching and introduce improvement measures. This improves matching accuracy based on past data.

[0044] The analysis unit can analyze the gameplay data of the professional gamer and trainer in real time and perform optimal matching. The analysis unit, for example, analyzes the gameplay data of the professional gamer and trainer in real time and performs optimal matching. For example, the analysis unit obtains the current gameplay data of the professional gamer and trainer in real time and performs optimal matching. The analysis unit can also compare the real-time skill levels of the professional gamer and trainer to perform optimal matching. Furthermore, the analysis unit can analyze the real-time in-game progress of the professional gamer and trainer to perform optimal matching. This allows optimal matching to be performed based on real-time data.

[0045] The analysis unit can prioritize region-specific matching by taking into account the geographical location information of the pro gamer and trainer. The analysis unit can prioritize region-specific matching by taking into account the geographical location information of the pro gamer and trainer, for example. For example, based on the current locations of the pro gamer and trainer, it can prioritize matching nearby players. It can also prioritize matching players who are active in the same region based on the geographical location information of the pro gamer and trainer. Furthermore, it can prioritize matching players who are participating in region-specific events or tournaments based on the geographical location information of the pro gamer and trainer. This allows region-specific matching to be performed based on geographical location information.

[0046] The analysis unit can analyze the social media activities of professional gamers and trainers and reflect related information in matching. The analysis unit, for example, analyzes the social media activities of professional gamers and trainers and reflects related information in matching. For example, it analyzes the content of social media posts by professional gamers and trainers and prioritizes matching people who share common interests. It can also prioritize matching people who mutually follow based on the social media follower information of the professional gamer and trainer. Furthermore, it can prioritize matching people who have interacted in the past based on the social media activity history of the professional gamer and trainer. This allows matching to be performed based on social media information.

[0047] The notification unit can select the optimal notification method by referring to past notification history when issuing a notification. For example, the notification unit selects the optimal notification method by referring to past notification history when issuing a notification. For example, the notification unit learns successful notification methods based on the past notification history and selects the optimal notification method. It can also collect feedback after notification and improve the notification method. It can also analyze past notification history, identify the cause of failed notifications, and implement remedial measures. In this way, the optimal notification method is selected based on the past notification history.

[0048] The notification unit can customize the notification content and provide information that meets the individual needs of the professional gamer and the trainer. The notification unit, for example, customizes the notification content and provides information that meets the individual needs of the professional gamer and the trainer. For example, customized notifications can be provided based on the practice content desired by the professional gamer. Notifications that include appropriate information can also be provided depending on the skill level of the trainer. Furthermore, customized notifications can also be provided according to the individual needs of the professional gamer and the trainer. This allows notifications to be provided that meet individual needs.

[0049] The notification unit can select the optimal notification method by taking into consideration the geographical location information of the pro gamer and trainer when sending a notification. For example, the notification unit selects the optimal notification method by taking into consideration the geographical location information of the pro gamer and trainer when sending a notification. For example, based on the current locations of the pro gamer and trainer, nearby practice partners can be given priority in notification. Furthermore, based on the geographical location information of the pro gamer and trainer, it is also possible to give priority in notification to partners who are active in the same area. Furthermore, based on the geographical location information of the pro gamer and trainer, it is also possible to give priority in notification to partners who are participating in region-specific events or tournaments. In this way, the optimal notification method is selected based on the geographical location information.

[0050] The notification unit can link the notification content with social media and automatically notify related information. For example, the notification unit can link the notification content with social media and automatically notify related information. For example, it can analyze the social media posts of professional gamers and trainers and notify related information. It can also notify people who are mutually followed by professional gamers and trainers based on their social media follower information. It can also notify people who have interacted with them in the past based on the social media activity history of the professional gamers and trainers. This allows notifications to be made based on social media information.

[0051] The adjustment unit can select the optimal adjustment method by referring to past schedule history when adjusting a schedule. For example, the adjustment unit selects the optimal adjustment method by referring to past schedule history when adjusting a schedule. For example, the adjustment unit learns successful schedule adjustment methods based on past schedule history and selects the optimal adjustment method. It can also collect feedback after schedule adjustment and improve the adjustment method. Furthermore, it can analyze past schedule history, identify the cause of unsuccessful schedule adjustment, and introduce improvement measures. In this way, the optimal adjustment method is selected based on past schedule history.

[0052] The adjustment unit can take into account the real-time activity status of the professional gamer and trainer when adjusting the schedule. The adjustment unit, for example, can take into account the real-time activity status of the professional gamer and trainer when adjusting the schedule. For example, the adjustment unit can obtain the current activity status of the professional gamer and trainer in real time and propose an optimal schedule. It can also take into account the real-time skill levels of the professional gamer and trainer to propose an appropriate schedule. It can also take into account the real-time in-game progress of the professional gamer and trainer to propose an optimal schedule. In this way, an optimal schedule is set based on the real-time activity status.

[0053] The adjustment unit can select the optimal adjustment method when adjusting schedules by taking into consideration the geographical location information of the professional gamer and the trainer. For example, when adjusting schedules, the adjustment unit selects the optimal adjustment method by taking into consideration the geographical location information of the professional gamer and the trainer. For example, based on the current locations of the professional gamer and the trainer, the adjustment unit can prioritize schedules with nearby practice partners. Also, based on the geographical location information of the professional gamer and the trainer, the adjustment unit can prioritize schedules with partners who are active in the same area. Furthermore, based on the geographical location information of the professional gamer and the trainer, the adjustment unit can prioritize schedules with partners who are participating in region-specific events or tournaments. In this way, the optimal schedule is set based on the geographical location information.

[0054] The adjustment unit can select the optimal adjustment method when adjusting a schedule by taking into consideration social media activity status. For example, the adjustment unit selects the optimal adjustment method when adjusting a schedule by taking into consideration social media activity status. For example, the adjustment unit analyzes the social media posts of the pro gamer and trainer and adjusts the schedule based on the related information. It can also adjust schedules with people who mutually follow the pro gamer and trainer based on social media follower information of the pro gamer and trainer. It can also adjust schedules with people who have interacted with the pro gamer and trainer in the past based on their social media activity history. In this way, the optimal schedule is set based on social media information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The analysis unit can analyze the gameplay data of professional gamers and trainers in real time to make optimal matches. For example, it can obtain the current gameplay data of professional gamers and trainers in real time to make optimal matches. It can also compare the real-time skill levels of professional gamers and trainers to make optimal matches. It can also analyze the real-time in-game progress of professional gamers and trainers to make optimal matches. This allows optimal matches to be made based on real-time data.

[0057] The input unit can prioritize displaying region-specific practice partners by taking into account the geographical location information of the professional gamer. For example, based on the professional gamer's current location, nearby practice partners can be prioritized. Also, based on the geographical location information of the professional gamer, trainers active in the same region can be prioritized. Furthermore, based on the geographical location information of the professional gamer, trainers participating in region-specific events or tournaments can be prioritized. This allows for efficient matching by displaying region-specific practice partners based on geographical location information.

[0058] The analysis unit can analyze the social media activities of professional gamers and trainers and reflect related information in matching. For example, it can analyze the social media posts of professional gamers and trainers to prioritize matching people who share common interests. It can also prioritize matching people who mutually follow each other based on the social media follower information of the professional gamer and trainer. Furthermore, it can prioritize matching people who have interacted with each other in the past based on the social media activity history of the professional gamer and trainer. This allows matching to be done based on social media information.

[0059] The notification unit can customize the notification content and provide information tailored to the individual needs of the professional gamer and trainer. For example, customized notifications can be sent based on the professional gamer's desired training content. Notifications containing appropriate information can also be sent based on the trainer's skill level. Furthermore, customized notifications can also be sent based on the individual needs of the professional gamer and trainer. This allows notifications to be sent according to individual needs.

[0060] The adjustment unit can take into account the real-time activity status of the professional gamer and trainer when adjusting the schedule. For example, it can obtain the current activity status of the professional gamer and trainer in real time and propose an optimal schedule. It can also take into account the real-time skill levels of the professional gamer and trainer to propose an appropriate schedule. It can also take into account the real-time in-game progress of the professional gamer and trainer to propose an optimal schedule. In this way, an optimal schedule is set based on the real-time activity status.

[0061] The input unit can analyze the professional gamer's past practice history and provide an auto-completion function when inputting information. For example, similar practice content can be automatically displayed as candidates based on the practice content previously entered by the professional gamer. Frequently used practice time slots can also be automatically suggested based on the professional gamer's past practice history. Furthermore, related game candidates can be automatically displayed based on the types of games previously entered by the professional gamer. This reduces the effort required for input by completing input based on past practice history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The input unit is where the professional gamer inputs information to recruit practice partners. The information input by the professional gamer includes the type of game, desired practice content, practice frequency and time period, etc. The input unit also allows the trainer to input their own skills and desired practice content. The information input by the trainer includes their skill level, the type of game they are good at, desired rewards, etc. Step 2: The analysis unit analyzes the information entered by the input unit and performs optimal matching. The analysis unit compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. Furthermore, it uses an algorithm that learns from past matching data to perform optimal matching. Step 3: The notification unit issues a notification based on the analysis results obtained by the analysis unit. If a match is made, the notification unit sends a notification to the professional gamer and trainer. Notification methods include email and app notifications. Step 4: The adjustment unit adjusts the schedule based on the matching notified by the notification unit. The adjustment unit uses a calendar function to automatically adjust the schedules of the professional gamer and trainer and set a practice schedule.

[0064] (Example 2) A matching system according to an embodiment of the present invention matches professional gamers (recruiters) with trainers (applicants). In this matching system, professional gamers input information for recruiting practice partners, and trainers input their own skills and desired practice content. AI analyzes the information to determine optimal matching. The AI ​​compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. When a match is made, notifications are sent to both parties, and practice schedules are automatically adjusted. This service allows professional gamers to efficiently find practice partners and trainers to gain opportunities to utilize their skills. For example, professional gamers input information for recruiting practice partners, such as the type of game, desired practice content, practice frequency, and time of day. Next, trainers input their own skills and desired practice content. For example, trainers input information such as their skill level, preferred game types, and desired rewards. AI analyzes this information to determine optimal matching. The AI ​​compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. For example, an algorithm is used to learn from past match data and create optimal matches. When a match is made, both parties are notified and the practice schedule is automatically adjusted. For example, a system will be introduced that automatically adjusts both parties' schedules using a calendar function. This service allows professional gamers to efficiently find practice partners and trainers to have opportunities to utilize their skills. This allows the matching system to efficiently match professional gamers and trainers and adjust schedules.

[0065] A matching system according to an embodiment includes an input unit for inputting information about a professional gamer, an input unit for inputting information about a trainer, an analysis unit, a notification unit, and an adjustment unit. The input unit inputs information for the professional gamer to recruit practice partners. The information input by the professional gamer includes, for example, the type of game, desired practice content, practice frequency and time period, etc. The input unit provides, for example, an interface through which the professional gamer can input the desired practice content in detail. The input unit also allows the trainer to input their own skills and desired practice content. The information input by the trainer includes, for example, their skill level, the type of game they are good at, and the desired reward. The input unit provides, for example, an interface through which the trainer can input their own skills in detail. The analysis unit analyzes the information input by the input unit and performs optimal matching. The analysis unit, for example, compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. The analysis unit, for example, learns past matching data and uses an algorithm for optimal matching. The notification unit provides a notification based on the analysis results obtained by the analysis unit. For example, when a match is made, the notification unit sends a notification to the professional gamer and the trainer. The notification unit sends the notification using, for example, email or an app notification. The adjustment unit adjusts schedules based on the match notified by the notification unit. The adjustment unit introduces a mechanism for automatically adjusting the schedules of both parties, for example, using a calendar function. The adjustment unit automatically adjusts the schedules of the professional gamer and the trainer and sets a practice schedule. This allows the matching system according to the embodiment to achieve efficient matching and schedule adjustment between professional gamers and trainers.

[0066] The analysis unit can compare the professional gamer's requirements with the trainer's skills. For example, the analysis unit compares the professional gamer's requirements with the trainer's skills. For example, the analysis unit compares the training content desired by the professional gamer with the training content that the trainer is good at, and recommends the most suitable trainer. The analysis unit can also compare the skill level of the professional gamer with the skill level of the trainer, and recommend a trainer with a matching skill level. Furthermore, the analysis unit can compare the professional gamer's past training history with the trainer's past training history, and recommend the most suitable trainer based on past performance. This makes it possible to find the trainer that best suits the professional gamer's requirements.

[0067] The analysis unit can learn from past matching data. For example, the analysis unit learns from past matching data. For example, based on the past matching data, it learns patterns of successful matching and improves accuracy. The analysis unit can also collect feedback after matching and improve the algorithm. Furthermore, the analysis unit can analyze past matching data, identify causes of unsuccessful matching, and implement improvements. This improves matching accuracy based on past data.

[0068] The adjustment unit can adjust schedules using a calendar function. The adjustment unit adjusts schedules using, for example, a calendar function. For example, the adjustment unit can link with Google Calendar to automatically adjust the schedules of professional gamers and trainers. The adjustment unit can also use a reminder function to notify them of practice schedules. Furthermore, the adjustment unit can update the schedules of professional gamers and trainers in real time to optimize practice schedules. This makes schedule adjustments automatic and efficient.

[0069] The input unit may include, as information input by the professional gamer, the type of game, desired practice content, and practice frequency or time period. The input unit may, for example, provide an interface that allows the professional gamer to input the details of the practice content they desire. The input unit may also collect data for recommending the most suitable trainer based on the information input by the professional gamer. The input unit may also transmit the information input by the professional gamer to the analysis unit, providing data for the analysis unit to perform optimal matching. This allows for more appropriate matching based on the detailed information of the professional gamer.

[0070] The input unit may include, as information input by the trainer, the trainer's skill level, the type of game the trainer is good at, and the desired reward. The input unit may, for example, provide an interface that allows the trainer to input their own skills in detail. The input unit may also collect data for recommending the most suitable professional gamer based on the information input by the trainer. Furthermore, the input unit may transmit the information input by the trainer to the analysis unit and provide data for the analysis unit to perform optimal matching. This allows for more appropriate matching based on the trainer's detailed information.

[0071] The input unit can estimate the professional gamer's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, the input unit estimates the professional gamer's emotions and dynamically changes the design of the input interface based on the estimated emotions. For example, if the professional gamer is stressed, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the professional gamer is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the professional gamer is in a hurry, voice input can be prioritized to allow information to be entered quickly. This improves input efficiency by providing an interface that corresponds to the professional gamer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The input unit can analyze the professional gamer's past practice history and provide an auto-completion function when inputting. The input unit can, for example, analyze the professional gamer's past practice history and provide an auto-completion function when inputting. For example, based on the practice content previously input by the professional gamer, similar practice content can be automatically displayed as candidates. It can also automatically suggest frequently used practice time periods based on the professional gamer's past practice history. Furthermore, it can automatically display candidates for related games based on the types of games previously input by the professional gamer. This reduces the effort required for input by completing the input based on the past practice history.

[0073] The input unit can acquire the current gameplay status of the professional gamer in real time and dynamically update the input content. The input unit, for example, acquires the current gameplay status of the professional gamer in real time and dynamically updates the input content. For example, the input unit can automatically acquire the type of game the professional gamer is currently playing and reflect this in the input content. The input unit can also acquire the professional gamer's current playing time in real time and suggest it as a practice time candidate. Furthermore, the input unit can acquire the professional gamer's current in-game progress and automatically suggest appropriate practice content. In this way, by updating the input content based on real-time information, more accurate information can be provided.

[0074] The input unit can estimate the professional gamer's emotions and automatically adjust the priority of input content based on the estimated emotions. The input unit, for example, estimates the professional gamer's emotions and automatically adjusts the priority of input content based on the estimated emotions. For example, if the professional gamer is feeling stressed, important information can be prioritized and other information can be postponed. Also, if the professional gamer is relaxed, the priority can be adjusted to encourage detailed information to be input. Furthermore, if the professional gamer is in a hurry, only the most important information can be prioritized. Thus, by adjusting the priority of input content according to the professional gamer's emotions, important information is prioritized and input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The input unit can prioritize displaying region-specific practice partners by taking into account the geographical location information of the professional gamer. The input unit, for example, prioritizes displaying region-specific practice partners by taking into account the geographical location information of the professional gamer. For example, based on the current location of the professional gamer, it can prioritize displaying nearby practice partners. It can also prioritize displaying trainers active in the same region based on the geographical location information of the professional gamer. Furthermore, it can prioritize displaying trainers participating in region-specific events or tournaments based on the geographical location information of the professional gamer. This allows for efficient matching by displaying region-specific practice partners based on geographical location information.

[0076] The input unit can analyze the social media activity of the professional gamer and automatically input related information. The input unit, for example, analyzes the social media activity of the professional gamer and automatically inputs related information. For example, it analyzes the content of the professional gamer's social media posts and automatically inputs information about the practice content and desired practice partners. It can also automatically suggest trainers suitable as practice partners based on the professional gamer's social media follower information. Furthermore, it can automatically display trainers with whom the professional gamer has interacted in the past based on the professional gamer's social media activity history. This reduces the effort required for input by completing the input based on social media information.

[0077] The input unit can estimate the trainer's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, the input unit estimates the trainer's emotions and dynamically changes the design of the input interface based on the estimated emotions. For example, if the trainer is feeling stressed, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the trainer is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the trainer is in a hurry, voice input can be prioritized to enable quick information input. This improves input efficiency by providing an interface that corresponds to the trainer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The input unit can analyze the trainer's past training history and provide an auto-completion function when inputting data. The input unit can, for example, analyze the trainer's past training history and provide an auto-completion function when inputting data. For example, based on training content previously input by the trainer, similar training content can be automatically displayed as candidates. Frequently used training time periods can also be automatically suggested based on the trainer's past training history. Furthermore, based on the types of games previously input by the trainer, related game candidates can also be automatically displayed. This reduces the effort required for input by completing input based on past training history.

[0079] The input unit can evaluate the trainer's current skill level in real time and dynamically update the input content. The input unit, for example, evaluates the trainer's current skill level in real time and dynamically updates the input content. For example, the input unit can automatically obtain the skill level of the game the trainer is currently playing and reflect this in the input content. The input unit can also obtain the trainer's current playing time in real time and suggest it as a training time candidate. Furthermore, the input unit can obtain the trainer's current in-game progress and automatically suggest appropriate training content. In this way, by updating the input content based on real-time information, more accurate information can be provided.

[0080] The input unit can estimate the trainer's emotions and automatically adjust the priority of input content based on the estimated emotions. The input unit, for example, estimates the trainer's emotions and automatically adjusts the priority of input content based on the estimated emotions. For example, if the trainer is feeling stressed, important information can be input first, and other information can be put off. Also, if the trainer is relaxed, the priority can be adjusted to input detailed information. Furthermore, if the trainer is in a hurry, only the most important information can be input first. In this way, by adjusting the priority of input content according to the trainer's emotions, important information is input first. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The input unit can prioritize displaying region-specific practice partners by taking into account the trainer's geographical location information. The input unit can prioritize displaying region-specific practice partners by taking into account the trainer's geographical location information, for example. For example, based on the trainer's current location, nearby practice partners can be prioritized. Also, based on the trainer's geographical location information, professional gamers active in the same region can be prioritized. Furthermore, based on the trainer's geographical location information, professional gamers participating in region-specific events or tournaments can be prioritized. In this way, by displaying region-specific practice partners based on geographical location information, efficient matching can be achieved.

[0082] The input unit can analyze the trainer's social media activity and automatically input related information. The input unit, for example, analyzes the trainer's social media activity and automatically inputs related information. For example, it analyzes the trainer's social media posts and automatically inputs information about training content and desired practice partners. It can also automatically suggest professional gamers suitable as practice partners based on the trainer's social media follower information. Furthermore, it can automatically display professional gamers with whom the trainer has interacted in the past based on the trainer's social media activity history. This reduces the effort required for input by completing the input based on social media information.

[0083] The analysis unit can estimate the emotions of the professional gamer and trainer and adjust the matching algorithm based on the estimated emotions. For example, the analysis unit estimates the emotions of the professional gamer and trainer and adjusts the matching algorithm based on the estimated emotions. For example, if the professional gamer is stressed, it can prioritize matching with a relaxed trainer. Also, if the trainer is relaxed, it can prioritize matching with a similarly relaxed professional gamer. Furthermore, if the emotions of the professional gamer and trainer match, it can also perform optimal matching. This allows for more appropriate matching by adjusting the matching algorithm based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can refer to past matching data and build a feedback loop to improve matching accuracy. The analysis unit, for example, refers to past matching data and builds a feedback loop to improve matching accuracy. For example, the analysis unit learns successful matching patterns based on past matching data and improves accuracy. The analysis unit can also collect feedback after matching and improve the algorithm. Furthermore, the analysis unit can analyze past matching data to identify causes of unsuccessful matching and introduce improvement measures. This improves matching accuracy based on past data.

[0085] The analysis unit can analyze the gameplay data of the professional gamer and trainer in real time and perform optimal matching. The analysis unit, for example, analyzes the gameplay data of the professional gamer and trainer in real time and performs optimal matching. For example, the analysis unit obtains the current gameplay data of the professional gamer and trainer in real time and performs optimal matching. The analysis unit can also compare the real-time skill levels of the professional gamer and trainer to perform optimal matching. Furthermore, the analysis unit can analyze the real-time in-game progress of the professional gamer and trainer to perform optimal matching. This allows optimal matching to be performed based on real-time data.

[0086] The analysis unit can estimate the emotions of the professional gamer and the trainer and adjust the display order of the matching results based on the estimated emotions. The analysis unit, for example, estimates the emotions of the professional gamer and the trainer and adjusts the display order of the matching results based on the estimated emotions. For example, if the professional gamer is stressed, a relaxed trainer can be displayed preferentially. Also, if the trainer is relaxed, a similarly relaxed professional gamer can be displayed preferentially. Furthermore, if the emotions of the professional gamer and the trainer match, the optimal matching result can be displayed at the top. In this way, adjusting the display order based on emotions displays more appropriate matching results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The analysis unit can prioritize region-specific matching by taking into account the geographical location information of the pro gamer and trainer. The analysis unit can prioritize region-specific matching by taking into account the geographical location information of the pro gamer and trainer, for example. For example, based on the current locations of the pro gamer and trainer, it can prioritize matching nearby players. It can also prioritize matching players who are active in the same region based on the geographical location information of the pro gamer and trainer. Furthermore, it can prioritize matching players who are participating in region-specific events or tournaments based on the geographical location information of the pro gamer and trainer. This allows region-specific matching to be performed based on geographical location information.

[0088] The analysis unit can analyze the social media activities of professional gamers and trainers and reflect related information in matching. The analysis unit, for example, analyzes the social media activities of professional gamers and trainers and reflects related information in matching. For example, it analyzes the content of social media posts by professional gamers and trainers and prioritizes matching people who share common interests. It can also prioritize matching people who mutually follow based on the social media follower information of the professional gamer and trainer. Furthermore, it can prioritize matching people who have interacted in the past based on the social media activity history of the professional gamer and trainer. This allows matching to be performed based on social media information.

[0089] The notification unit can estimate the emotions of the professional gamer and the trainer, and adjust the content and timing of notifications based on the estimated emotions. The notification unit, for example, estimates the emotions of the professional gamer and the trainer, and adjusts the content and timing of notifications based on the estimated emotions. For example, if the professional gamer is feeling stressed, the notification can be sent at a time when the professional gamer is relaxed. Also, if the trainer is relaxed, a notification containing detailed information can be sent. Furthermore, if the emotions of the professional gamer and the trainer match, the notification can be sent at the optimal timing. In this way, by adjusting the notification based on emotions, notifications can be sent at more appropriate times. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The notification unit can select the optimal notification method by referring to past notification history when issuing a notification. For example, the notification unit selects the optimal notification method by referring to past notification history when issuing a notification. For example, the notification unit learns successful notification methods based on the past notification history and selects the optimal notification method. It can also collect feedback after notification and improve the notification method. It can also analyze past notification history, identify the cause of failed notifications, and implement remedial measures. In this way, the optimal notification method is selected based on the past notification history.

[0091] The notification unit can customize the notification content and provide information that meets the individual needs of the professional gamer and the trainer. The notification unit, for example, customizes the notification content and provides information that meets the individual needs of the professional gamer and the trainer. For example, customized notifications can be provided based on the practice content desired by the professional gamer. Notifications that include appropriate information can also be provided depending on the skill level of the trainer. Furthermore, customized notifications can also be provided according to the individual needs of the professional gamer and the trainer. This allows notifications to be provided that meet individual needs.

[0092] The notification unit can estimate the emotions of the professional gamer and the trainer and determine the priority of notifications based on the estimated emotions. The notification unit, for example, estimates the emotions of the professional gamer and the trainer and determines the priority of notifications based on the estimated emotions. For example, if the professional gamer is feeling stressed, important notifications can be prioritized. Also, if the trainer is relaxed, notifications containing detailed information can be prioritized. Furthermore, if the emotions of the professional gamer and the trainer match, the most appropriate notifications can be prioritized. In this way, important notifications are prioritized by determining the priority of notifications based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The notification unit can select the optimal notification method by taking into consideration the geographical location information of the pro gamer and trainer when sending a notification. For example, the notification unit selects the optimal notification method by taking into consideration the geographical location information of the pro gamer and trainer when sending a notification. For example, based on the current locations of the pro gamer and trainer, nearby practice partners can be given priority in notification. Furthermore, based on the geographical location information of the pro gamer and trainer, it is also possible to give priority in notification to partners who are active in the same area. Furthermore, based on the geographical location information of the pro gamer and trainer, it is also possible to give priority in notification to partners who are participating in region-specific events or tournaments. In this way, the optimal notification method is selected based on the geographical location information.

[0094] The notification unit can link the notification content with social media and automatically notify related information. For example, the notification unit can link the notification content with social media and automatically notify related information. For example, it can analyze the social media posts of professional gamers and trainers and notify related information. It can also notify people who are mutually followed by professional gamers and trainers based on their social media follower information. It can also notify people who have interacted with them in the past based on the social media activity history of the professional gamers and trainers. This allows notifications to be made based on social media information.

[0095] The adjustment unit can estimate the emotions of the professional gamer and the trainer and change the schedule adjustment method based on the estimated emotions. The adjustment unit, for example, estimates the emotions of the professional gamer and the trainer and changes the schedule adjustment method based on the estimated emotions. For example, if the professional gamer is feeling stressed, the adjustment unit can adjust the schedule to a time when the professional gamer is relaxed. Also, if the trainer is relaxed, the adjustment unit can adjust the schedule to include detailed information. Furthermore, if the emotions of the professional gamer and the trainer match, the adjustment unit can adjust the schedule to the optimal time. In this way, a more appropriate schedule is set by adjusting the schedule based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The adjustment unit can select the optimal adjustment method by referring to past schedule history when adjusting a schedule. For example, the adjustment unit selects the optimal adjustment method by referring to past schedule history when adjusting a schedule. For example, the adjustment unit learns successful schedule adjustment methods based on past schedule history and selects the optimal adjustment method. It can also collect feedback after schedule adjustment and improve the adjustment method. Furthermore, it can analyze past schedule history, identify the cause of unsuccessful schedule adjustment, and introduce improvement measures. In this way, the optimal adjustment method is selected based on past schedule history.

[0097] The adjustment unit can take into account the real-time activity status of the professional gamer and trainer when adjusting the schedule. The adjustment unit, for example, can take into account the real-time activity status of the professional gamer and trainer when adjusting the schedule. For example, the adjustment unit can obtain the current activity status of the professional gamer and trainer in real time and propose an optimal schedule. It can also take into account the real-time skill levels of the professional gamer and trainer to propose an appropriate schedule. It can also take into account the real-time in-game progress of the professional gamer and trainer to propose an optimal schedule. In this way, an optimal schedule is set based on the real-time activity status.

[0098] The adjustment unit can estimate the emotions of the professional gamer and the trainer and determine schedule priorities based on the estimated emotions. The adjustment unit, for example, estimates the emotions of the professional gamer and the trainer and determines schedule priorities based on the estimated emotions. For example, if the professional gamer is feeling stressed, it can prioritize important schedules. Also, if the trainer is relaxed, it can prioritize schedules that include detailed information. Furthermore, if the emotions of the professional gamer and the trainer match, it can prioritize the optimal schedule. In this way, important schedules are prioritized by determining schedule priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The adjustment unit can select the optimal adjustment method when adjusting schedules by taking into consideration the geographical location information of the professional gamer and the trainer. For example, when adjusting schedules, the adjustment unit selects the optimal adjustment method by taking into consideration the geographical location information of the professional gamer and the trainer. For example, based on the current locations of the professional gamer and the trainer, the adjustment unit can prioritize schedules with nearby practice partners. Also, based on the geographical location information of the professional gamer and the trainer, the adjustment unit can prioritize schedules with partners who are active in the same area. Furthermore, based on the geographical location information of the professional gamer and the trainer, the adjustment unit can prioritize schedules with partners who are participating in region-specific events or tournaments. In this way, the optimal schedule is set based on the geographical location information.

[0100] The adjustment unit can select the optimal adjustment method when adjusting a schedule by taking into consideration social media activity status. For example, the adjustment unit selects the optimal adjustment method when adjusting a schedule by taking into consideration social media activity status. For example, the adjustment unit analyzes the social media posts of the pro gamer and trainer and adjusts the schedule based on the related information. It can also adjust schedules with people who mutually follow the pro gamer and trainer based on social media follower information of the pro gamer and trainer. It can also adjust schedules with people who have interacted with the pro gamer and trainer in the past based on their social media activity history. In this way, the optimal schedule is set based on social media information. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, notification unit, and adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14 and provides an interface for inputting information about professional gamers and trainers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information to perform optimal matching. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies when matching is achieved. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adjusts schedules using a calendar function. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, notification unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting information about professional gamers and trainers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information to perform optimal matching. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies when matching is achieved. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adjusts schedules using a calendar function. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, notification unit, and adjustment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for inputting information about professional gamers and trainers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information to perform optimal matching. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and issues a notification when matching is achieved. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adjusts schedules using a calendar function. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, notification unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting information about professional gamers and trainers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information to perform optimal matching. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies when matching is achieved. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically adjusts schedules using a calendar function.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit can analyze the gameplay data of professional gamers and trainers in real time to make optimal matches. For example, it can obtain the current gameplay data of professional gamers and trainers in real time to make optimal matches. It can also compare the real-time skill levels of professional gamers and trainers to make optimal matches. It can also analyze the real-time in-game progress of professional gamers and trainers to make optimal matches. This allows optimal matches to be made based on real-time data.

[0103] The notification unit can estimate the emotions of the professional gamer and trainer, and adjust the content and timing of notifications based on the estimated emotions. For example, if the professional gamer is feeling stressed, the notification can be sent at a time when the trainer is relaxed. Also, if the trainer is relaxed, the notification can include detailed information. Furthermore, if the emotions of the professional gamer and trainer match, the notification can be sent at the optimal timing. This allows notifications to be sent at more appropriate times by adjusting the notifications based on emotions.

[0104] The input unit can prioritize displaying region-specific practice partners by taking into account the geographical location information of the professional gamer. For example, based on the professional gamer's current location, nearby practice partners can be prioritized. Also, based on the geographical location information of the professional gamer, trainers active in the same region can be prioritized. Furthermore, based on the geographical location information of the professional gamer, trainers participating in region-specific events or tournaments can be prioritized. This allows for efficient matching by displaying region-specific practice partners based on geographical location information.

[0105] The adjustment unit can estimate the emotions of the professional gamer and trainer and change the schedule adjustment method based on the estimated emotions. For example, if the professional gamer is feeling stressed, the schedule can be adjusted to coincide with a relaxed time. Also, if the trainer is feeling relaxed, the schedule can be adjusted to include detailed information. Furthermore, if the emotions of the professional gamer and trainer match, the schedule can be adjusted to coincide with the optimal time. This allows for a more appropriate schedule to be set through schedule adjustment based on emotions.

[0106] The analysis unit can analyze the social media activities of professional gamers and trainers and reflect related information in matching. For example, it can analyze the social media posts of professional gamers and trainers to prioritize matching people who share common interests. It can also prioritize matching people who mutually follow each other based on the social media follower information of the professional gamer and trainer. Furthermore, it can prioritize matching people who have interacted with each other in the past based on the social media activity history of the professional gamer and trainer. This allows matching to be done based on social media information.

[0107] The notification unit can customize the notification content and provide information tailored to the individual needs of the professional gamer and trainer. For example, customized notifications can be sent based on the professional gamer's desired training content. Notifications containing appropriate information can also be sent based on the trainer's skill level. Furthermore, customized notifications can also be sent based on the individual needs of the professional gamer and trainer. This allows notifications to be sent according to individual needs.

[0108] The input unit can estimate the professional gamer's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the professional gamer is feeling stressed, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the professional gamer is relaxed, detailed input options can be provided and customizable input methods can be suggested. Furthermore, if the professional gamer is in a hurry, voice input can be prioritized to allow for quick information input. This improves input efficiency by providing an interface that responds to the professional gamer's emotions.

[0109] The adjustment unit can take into account the real-time activity status of the professional gamer and trainer when adjusting the schedule. For example, it can obtain the current activity status of the professional gamer and trainer in real time and propose an optimal schedule. It can also take into account the real-time skill levels of the professional gamer and trainer to propose an appropriate schedule. It can also take into account the real-time in-game progress of the professional gamer and trainer to propose an optimal schedule. In this way, an optimal schedule is set based on the real-time activity status.

[0110] The analysis unit can estimate the emotions of professional gamers and trainers and adjust the matching algorithm based on the estimated emotions. For example, if a professional gamer is feeling stressed, it can prioritize matching with a relaxed trainer. Also, if a trainer is relaxed, it can prioritize matching with a similarly relaxed professional gamer. Furthermore, if the emotions of a professional gamer and trainer match, it can also perform optimal matching. This allows for more appropriate matching by adjusting the matching algorithm based on emotions.

[0111] The input unit can analyze the professional gamer's past practice history and provide an auto-completion function when inputting information. For example, similar practice content can be automatically displayed as candidates based on the practice content previously entered by the professional gamer. Frequently used practice time slots can also be automatically suggested based on the professional gamer's past practice history. Furthermore, related game candidates can be automatically displayed based on the types of games previously entered by the professional gamer. This reduces the effort required for input by completing input based on past practice history.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The input unit is where the professional gamer inputs information to recruit practice partners. The information input by the professional gamer includes the type of game, desired practice content, practice frequency and time period, etc. The input unit also allows the trainer to input their own skills and desired practice content. The information input by the trainer includes their skill level, the type of game they are good at, desired rewards, etc. Step 2: The analysis unit analyzes the information entered by the input unit and performs optimal matching. The analysis unit compares the professional gamer's requirements with the trainer's skills and recommends the most suitable trainer. Furthermore, it uses an algorithm that learns from past matching data to perform optimal matching. Step 3: The notification unit issues a notification based on the analysis results obtained by the analysis unit. If a match is made, the notification unit sends a notification to the professional gamer and trainer. Notification methods include email and app notifications. Step 4: The adjustment unit adjusts the schedule based on the matching notified by the notification unit. The adjustment unit uses a calendar function to automatically adjust the schedules of the professional gamer and trainer and set a practice schedule.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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. an input section for inputting information about professional gamers and trainers; an analysis unit that analyzes the information input by the input unit and performs matching; a notification unit that issues a notification based on the analysis result obtained by the analysis unit; an adjustment unit that manages a schedule based on the matching notified by the notification unit; A system characterized by:

2. The analysis unit Comparing the demands of professional gamers with the skills of trainers 2. The system of claim 1.

3. The analysis unit Learn from past matching data 2. The system of claim 1.

4. The adjustment unit Use the calendar feature to coordinate your schedule 2. The system of claim 1.

5. The input unit Information entered by professional gamers includes the type of game, desired practice content, practice frequency and time period.

2. The system of claim 1.

6. The input unit Trainers enter information including their skill level, preferred game type, and desired reward.

2. The system of claim 1.

7. The input unit Estimating the emotions of professional gamers and dynamically changing the design of the input interface based on the estimated emotions 2. The system of claim 1.

8. The input unit Analyzes the past practice history of professional gamers and provides auto-complete function when inputting 2. The system of claim 1.

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