system

Abstract

The system according to this embodiment aims to make it easy to find a sports opponent and to respond quickly even if the number of members suddenly decreases. [Solution] The system according to the embodiment comprises a reception unit, a search unit, and a response unit. The reception unit receives input for selecting a sport. The search unit searches for an opponent based on the sport received by the reception unit. The response unit searches for a new member if the number of members suddenly decreases.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to find a sports partner or to respond when the number of members suddenly decreases.

[0005] The system according to the embodiment aims to easily find a sports partner and quickly respond even when the number of members suddenly decreases.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a search unit, and a response unit. The reception unit receives an input for selecting a sport. The search unit searches for a partner based on the sport received by the reception unit. The response unit searches for new members when the number of members suddenly decreases. [Effects of the Invention]

[0007] The system according to this embodiment can easily find a sports opponent and respond quickly even if the number of members suddenly decreases. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The sports member matching system according to an embodiment of the present invention is a system that performs sports member matching using a generative AI. In this system, when a user selects a sport, the generative AI searches for a suitable partner for that sport and performs the matching. For example, it can find a partner instantly for any sport, such as golf, soccer, or baseball. Furthermore, even if the number of members suddenly decreases, the generative AI immediately searches for new members and performs the matching. This mechanism makes it easy to find partners to enjoy sports with, ensuring that the enjoyment of sports is never interrupted. Thus, the sports member matching system accepts input from the user to select a sport, searches for a partner, and can search for new members if the number of members suddenly decreases.

[0029] The sports member matching system according to this embodiment comprises a reception unit, a search unit, and a response unit. The reception unit receives input from the user to select a sport. Input from the user to select a sport includes, but is not limited to, the type of sport, date and time, and location. For example, the reception unit receives the type of sport selected by the user as input. The reception unit can also receive information on the date and time and location desired by the user. Furthermore, the reception unit can also receive input such as the user's skill level and the desired partner's conditions. For example, the reception unit receives the type of sport selected by the user as input. The reception unit can also receive information on the date and time and location desired by the user. Furthermore, the reception unit can also receive input such as the user's skill level and the desired partner's conditions. The search unit uses generative AI to search for a partner based on the sport received by the reception unit. For example, the search unit uses generative AI to search for a partner suitable for the sport selected by the user. Furthermore, the search unit can also use generative AI to search for a partner based on the user's skill level and the desired partner's conditions. Furthermore, the search unit can use generative AI to search for opponents based on the user's geographical proximity and available time slots. For example, the search unit can use generative AI to search for opponents suitable for the sport selected by the user. The search unit can also use generative AI to search for opponents based on the user's skill level and desired opponent criteria. Furthermore, the search unit can also use generative AI to search for opponents based on the user's geographical proximity and available time slots. The response unit searches for new members when the number of members suddenly decreases. For example, the response unit searches for new members using generative AI when the number of members suddenly decreases. Furthermore, the response unit can also search for new members based on the user's skill level and desired opponent criteria when the number of members suddenly decreases. Furthermore, the response unit can also search for new members based on the user's geographical proximity and available time slots when the number of members suddenly decreases. For example, the response unit searches for new members using generative AI when the number of members suddenly decreases.Furthermore, the system can search for new members based on the user's skill level and desired partner criteria if the number of members suddenly decreases. Additionally, the system can search for new members based on the user's geographical proximity and available time slots if the number of members suddenly decreases. Thus, the sports member matching system according to this embodiment can accept input from the user to select a sport, search for opponents, and search for new members if the number of members suddenly decreases.

[0030] The reception desk accepts input from users to select a sport. This input may include, but is not limited to, the type of sport, date and time, and location. For example, the reception desk accepts the type of sport selected by the user. It can also accept information about the user's preferred date and time and location. Furthermore, the reception desk can accept input about the user's skill level and desired opponent. For example, the reception desk accepts the type of sport selected by the user. It can also accept information about the user's preferred date and time and location. Furthermore, the reception desk can accept input about the user's skill level and desired opponent. For example, if the user selects soccer, the reception desk receives this information and registers it in the system. It can also accept information about the user's preferred date and time and location. For example, if the user wants to play soccer at a nearby park on a weekend afternoon, the reception desk will accept this information. Furthermore, the reception desk can accept input about the user's skill level and desired opponent. For example, if a user is a beginner and wishes to play with another beginner, the reception desk will accept this information. This allows the reception desk to receive detailed user preferences and register them in the system. The reception desk also accepts the type of sport the user has selected as input. For example, if a user selects tennis, the reception desk will receive this information and register it in the system. It can also accept information about the user's preferred date, time, and location. For example, if a user wishes to play tennis at a nearby tennis court on a weekday evening, the reception desk will accept this information. Furthermore, the reception desk can also accept input such as the user's skill level and the desired partner's conditions. For example, if a user is an intermediate player and wishes to play with another intermediate player, the reception desk will accept this information. This allows the reception desk to receive detailed user preferences and register them in the system.

[0031] The search unit uses generative AI to search for opponents based on the sport received by the reception unit. For example, the search unit uses generative AI to search for opponents suitable for the sport selected by the user. The search unit can also use generative AI to search for opponents based on the user's skill level and desired opponent criteria. Furthermore, the search unit can use generative AI to search for opponents based on the user's geographical proximity and available time slots. For example, if the user selects soccer, the generative AI searches the system's database for other users who also want to play soccer. For example, if the user is a beginner and wants to play with a beginner, the generative AI searches for opponents who meet those criteria. Furthermore, the search unit can use generative AI to find opponents based on the user's geographical proximity and available time slots. For example, if a user wants to play soccer in a nearby park, the generative AI will search for opponents who match that condition. This allows the search unit to find the best opponent based on the user's detailed preferences. The search unit uses generative AI to search for opponents suitable for the sport selected by the user. For example, if a user selects tennis, the generative AI will search the system's database for other users who want to play tennis. The search unit can also use generative AI to search for opponents based on the user's skill level and desired opponent conditions. For example, if a user is an intermediate player and wants an intermediate player, the generative AI will search for opponents who match that condition. Furthermore, the search unit can use generative AI to search for opponents based on the user's geographical proximity and available time slots.For example, if a user wants to play tennis at a nearby tennis court, the generating AI will search for an opponent that matches that condition. This allows the search unit to find the best opponent based on the user's detailed preferences.

[0032] The response unit searches for new members when the number of members suddenly decreases. For example, if the number of members suddenly decreases, the response unit uses generative AI to search for new members. The response unit can also search for new members based on the user's skill level and desired partner criteria when the number of members suddenly decreases. Furthermore, the response unit can also search for new members based on the user's geographical proximity and available time slots when the number of members suddenly decreases. For example, if the number of members suddenly decreases during a soccer match, the generative AI searches for new members from the system's database. Furthermore, the system can search for new members based on the user's skill level and desired opponent criteria if the number of members suddenly decreases. For example, if a beginner user suddenly needs a member, the generating AI will search for a new member that matches those criteria. In addition, the system can also search for new members based on the user's geographical proximity and available time slots if the number of members suddenly decreases. For example, if a user playing soccer in a nearby park suddenly needs a member, the generating AI will search for a new member that matches those criteria. This allows the system to quickly search for new members and support the user's sports activities even if the number of members suddenly decreases. The system searches for new members if the number of members suddenly decreases. For example, if the number of members suddenly decreases during a tennis match, the generating AI will search for a new member from the system's database. Furthermore, the system can also search for new members based on the user's skill level and desired opponent criteria if the number of members suddenly decreases.For example, if an intermediate-level user suddenly needs a member, the generating AI will search for a new member that matches those criteria. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's geographical proximity and available time slots. For example, if a user playing tennis at a nearby court suddenly needs a member, the generating AI will search for a new member that matches those criteria. This allows the response unit to quickly find new members even when the number of members suddenly decreases, supporting the user's sports activities.

[0033] The search unit can search for opponents using generative AI. For example, the search unit can use generative AI to search for opponents suitable for a sport selected by the user. For example, the generative AI takes the type of sport selected by the user as input and searches for opponents suitable for that sport. The generative AI can also search for opponents based on the user's skill level and desired opponent criteria. For example, the generative AI takes the user's skill level as input and searches for opponents suitable for that skill level. The generative AI can also take the user's desired opponent criteria as input and search for opponents suitable for those criteria. Furthermore, the generative AI can also search for opponents based on the user's geographical proximity and available time slots. For example, the generative AI takes the user's geographical location information as input and searches for opponents based on that location information. The generative AI can also take the user's available time slots as input and search for opponents based on those time slots. As a result, using generative AI improves the accuracy of opponent searches.

[0034] The response unit can search for new members if the number of members suddenly decreases. For example, if the number of members suddenly decreases, the response unit can use generative AI to search for new members. For example, if the number of members suddenly decreases, the response unit can also search for new members based on the user's skill level and desired partner criteria. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's geographical proximity and available time slots. For example, if the number of members suddenly decreases, the response unit can use generative AI to search for new members. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's skill level and desired partner criteria. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's geographical proximity and available time slots. This allows for the rapid search for new members even if the number of members suddenly decreases. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can use a generation AI to search for new members if the number of members suddenly decreases.

[0035] The reception desk can accept input from the user to select a sport. For example, the reception desk can accept the type of sport selected by the user as input. For example, the reception desk can also accept information such as the date, time, and location desired by the user. Furthermore, the reception desk can also accept input such as the user's skill level and the conditions of their desired opponent. For example, the reception desk can accept the type of sport selected by the user as input. Furthermore, the reception desk can also accept information such as the date, time, and location desired by the user. In addition, the reception desk can also accept input such as the user's skill level and the conditions of their desired opponent. This allows the reception desk to accept input from the user to select a sport. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can accept the type of sport selected by the user as input.

[0036] The search unit can find suitable opponents for sports such as golf, soccer, and baseball. For example, it uses generative AI to find opponents suitable for the sport selected by the user. For instance, the generative AI takes the type of sport selected by the user as input and searches for suitable opponents. The generative AI can also search for opponents based on the user's skill level and desired opponent criteria. For example, it takes the user's skill level as input and searches for opponents suitable for that skill level. Furthermore, it can take the user's desired opponent criteria as input and search for opponents suitable for those criteria. In addition, the generative AI can search for opponents based on the user's geographical proximity and available time slots. For example, it takes the user's geographical location as input and searches for opponents based on that location. It can also take the user's available time slots as input and search for opponents based on those time slots. This allows for the search of opponents suitable for a variety of sports.

[0037] The reception desk can analyze the user's past sports selection history and suggest the most suitable sport. For example, the reception desk can prioritize displaying sports that the user has frequently selected in the past. For example, the reception desk can suggest sports suitable for a specific season or time of day based on the user's past selection history. Furthermore, the reception desk can also suggest new sports based on the user's past selection history. This improves user satisfaction by suggesting the most suitable sport based on past selection history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past selection history into a generating AI and have the generating AI suggest the most suitable sport.

[0038] The reception desk can filter sports selections based on the user's current physical condition and fitness level. For example, if the user is tired, the reception desk can suggest light exercise. For example, if the user is seeking healthy exercise, the reception desk can suggest moderate exercise. Furthermore, if the user is feeling unwell, the reception desk can also suggest relaxing sports. This allows for more appropriate selections by suggesting sports that match the user's physical condition and fitness level. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's physical condition data into a generating AI and have the generating AI perform the filtering.

[0039] The reception desk can prioritize displaying highly relevant sports when a user selects a sport, taking into account the user's geographical location. For example, the reception desk can prioritize displaying sports events being held near the user's current location. For example, the reception desk can suggest nearby sports facilities based on the user's geographical location. The reception desk can also suggest easily accessible sports, taking into account the user's geographical location. For example, the reception desk can prioritize displaying sports events being held near the user's current location. For example, the reception desk can suggest nearby sports facilities based on the user's geographical location. Furthermore, the reception desk can suggest easily accessible sports, taking into account the user's geographical location. This allows for the suggestion of more relevant sports by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI suggest highly relevant sports.

[0040] The reception desk can analyze the user's social media activity when selecting a sport and suggest relevant sports. For example, the reception desk can suggest sports that the user has shown interest in on social media. For example, the reception desk can suggest sports events that the user's friends are participating in. Furthermore, the reception desk can also suggest trending sports based on the user's social media activity. This allows the user to select a sport that matches their interests by suggesting relevant sports based on their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant sports.

[0041] The search unit can apply different search algorithms depending on the type of sport during a search. For example, in the case of golf, the search unit applies a search algorithm that takes into account the skill level of the players. For example, in the case of soccer, the search unit applies a search algorithm that takes into account the positional balance of the team. Furthermore, in the case of baseball, the search unit can also apply a search algorithm that takes into account the position and skill level of the players. This allows for more accurate search results by applying a search algorithm appropriate to the type of sport. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can have a generative AI execute a search algorithm appropriate to the type of sport.

[0042] The search unit can suggest the most suitable partner by referring to the user's past matching history during a search. For example, the search unit can suggest the most suitable partner based on the user's evaluation of past matches. For example, the search unit can suggest compatible partners based on the user's past matching history. The search unit can also analyze the user's past matching history and suggest the most suitable partner. For example, the search unit can suggest the most suitable partner based on the user's evaluation of past matches. For example, the search unit can suggest compatible partners based on the user's past matching history. Furthermore, the search unit can analyze the user's past matching history and suggest the most suitable partner. This improves user satisfaction by suggesting the most suitable partner based on past matching history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past matching history into a generative AI and have the generative AI suggest the most suitable partner.

[0043] The search unit can prioritize displaying highly relevant matches by considering the user's geographical location during a search. For example, the search unit can prioritize displaying matches active in the vicinity of the user's current location. For example, the search unit can suggest matches active at nearby sports facilities based on the user's geographical location. The search unit can also suggest matches that are easily accessible by considering the user's geographical location. For example, the search unit can prioritize displaying matches active in the vicinity of the user's current location. For example, the search unit can suggest matches active at nearby sports facilities based on the user's geographical location. Furthermore, the search unit can suggest matches that are easily accessible by considering the user's geographical location. This allows for the suggestion of more relevant matches by considering the user's geographical location. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's geographical location information into a generative AI and have the generative AI suggest highly relevant matches.

[0044] The search unit can analyze the user's social media activity during a search and suggest relevant individuals. For example, the search unit can suggest individuals who play sports that the user has shown interest in on social media. For example, the search unit can suggest individuals who are participating in a sport event that the user's friends are also participating in. Furthermore, the search unit can suggest individuals who play trending sports based on the user's social media activity. This allows the user to select individuals who match their interests by suggesting relevant individuals based on their social media activity. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not using generative AI. For example, the search unit can input the user's social media data into a generative AI and have the generative AI suggest relevant individuals.

[0045] The response unit can suggest the most suitable new members by referring to past member history when the number of members suddenly decreases. For example, the response unit can suggest new members based on evaluations of members who have played together in the past. For example, the response unit can suggest compatible new members from past member history. The response unit can also analyze past member history and suggest the most suitable new members. For example, the response unit can suggest new members based on evaluations of members who have played together in the past. For example, the response unit can suggest compatible new members from past member history. Furthermore, the response unit can analyze past member history and suggest the most suitable new members. This allows for the rapid discovery of new members by suggesting the most suitable new members based on past member history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past member history into a generating AI and have the generating AI suggest the most suitable new members.

[0046] The support unit can filter new members based on the user's current sports activity status when searching for new members. For example, the support unit can suggest new members suitable for the sport the user is currently participating in. For example, the support unit can filter new members based on the user's current sports activity status. The support unit can also suggest the most suitable new members considering the user's current sports activity status. For example, the support unit can suggest new members suitable for the sport the user is currently participating in. The support unit can also filter new members based on the user's current sports activity status. Furthermore, the support unit can also suggest the most suitable new members considering the user's current sports activity status. This allows for more appropriate selection by suggesting new members according to the current sports activity status. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's sports activity data into a generating AI and have the generating AI perform the filtering.

[0047] The response unit can prioritize displaying highly relevant members when searching for new members, taking into account the user's geographical location. For example, the response unit can prioritize displaying new members who are active near the user's current location. For example, the response unit can suggest new members who are active at nearby sports facilities based on the user's geographical location. The response unit can also suggest easily accessible new members, taking into account the user's geographical location. For example, the response unit can prioritize displaying new members who are active near the user's current location. For example, the response unit can suggest new members who are active at nearby sports facilities based on the user's geographical location. Furthermore, the response unit can suggest easily accessible new members, taking into account the user's geographical location. This allows for the suggestion of more relevant new members by considering the user's geographical location. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant new members.

[0048] The response unit can analyze the user's social media activity when searching for new members and suggest relevant members. For example, the response unit can suggest new members who play sports that the user has shown interest in on social media. For example, the response unit can suggest new members who are participating in sports events that the user's friends are participating in. Furthermore, the response unit can also suggest new members who play trending sports based on the user's social media activity. For example, the response unit can suggest new members who play sports that the user has shown interest in on social media. Furthermore, the response unit can suggest new members who are participating in sports events that the user's friends are participating in. Furthermore, the response unit can also suggest new members who play trending sports based on the user's social media activity. This allows the user to select new members that match their interests by suggesting relevant new members based on social media activity. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the user's social media data into a generating AI and have the generating AI suggest relevant new members.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The sports member matching system can also acquire users' health data and suggest appropriate sports based on their health status. For example, it can acquire users' heart rate and sleep data and suggest light exercise if they are fatigued. It can also consider the user's weight and BMI and suggest sports that support healthy weight management. Furthermore, it can analyze the user's past health data and suggest sports based on long-term health goals. This allows users to choose sports that suit their health status and supports a healthier lifestyle.

[0051] The sports member matching system can further suggest sports based on the user's hobbies and interests. For example, if a user enjoys music, it can suggest exercises performed to music. If a user enjoys outdoor activities, it can suggest sports that take place in nature. Furthermore, if a user is sociable, it can prioritize suggesting team sports. This allows users to choose sports that match their hobbies and interests, making sports more enjoyable.

[0052] The sports member matching system can further analyze a user's past sports performance data and suggest sports that can improve their performance. For example, it can analyze the results of sports events the user has participated in in the past and suggest a training plan to improve performance. It can also suggest a suitable training partner based on the user's past performance data. Furthermore, it can monitor the user's performance data in real time and provide appropriate feedback. This enables support to improve the user's sports performance.

[0053] The sports member matching system can further enhance connections with local sports communities by considering the user's geographical location. For example, it can suggest sports clubs and teams active near the user's current location. It can also provide information on local sports events and tournaments. Furthermore, it can provide local transportation information and access methods for sports events the user wishes to participate in. This makes it easier for users to connect with local sports communities and broadens the scope of their sports activities.

[0054] The sports member matching system can further collect user feedback and use it to improve the system. For example, it can collect user ratings of sports events they have participated in and incorporate them into future event suggestions. It can also improve the system's interface and functionality based on user feedback. Furthermore, it can analyze user feedback to improve the accuracy of sports member matching. This enables continuous improvement to increase user satisfaction.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk accepts input from the user to select a sport. This input can include the type of sport the user chooses, date and time, location, skill level, and desired opponent criteria. Step 2: The search unit searches for opponents based on the sport received by the reception unit. Using generative AI, it searches for opponents suitable for the sport selected by the user, and can search for opponents based on skill level, desired opponent conditions, geographical proximity, and available time slots. Step 3: The response unit searches for new members in case of a sudden decrease in members. Using a generation AI, it can search for new members based on the user's skill level, desired partner criteria, geographical proximity, and available time slots.

[0057] (Example of form 2) The sports member matching system according to an embodiment of the present invention is a system that performs sports member matching using a generative AI. In this system, when a user selects a sport, the generative AI searches for a suitable partner for that sport and performs the matching. For example, it can find a partner instantly for any sport, such as golf, soccer, or baseball. Furthermore, even if the number of members suddenly decreases, the generative AI immediately searches for new members and performs the matching. This mechanism makes it easy to find partners to enjoy sports with, ensuring that the enjoyment of sports is never interrupted. Thus, the sports member matching system accepts input from the user to select a sport, searches for a partner, and can search for new members if the number of members suddenly decreases.

[0058] The sports member matching system according to this embodiment comprises a reception unit, a search unit, and a response unit. The reception unit receives input from the user to select a sport. Input from the user to select a sport includes, but is not limited to, the type of sport, date and time, and location. For example, the reception unit receives the type of sport selected by the user as input. The reception unit can also receive information on the date and time and location desired by the user. Furthermore, the reception unit can also receive input such as the user's skill level and the desired partner's conditions. For example, the reception unit receives the type of sport selected by the user as input. The reception unit can also receive information on the date and time and location desired by the user. Furthermore, the reception unit can also receive input such as the user's skill level and the desired partner's conditions. The search unit uses generative AI to search for a partner based on the sport received by the reception unit. For example, the search unit uses generative AI to search for a partner suitable for the sport selected by the user. Furthermore, the search unit can also use generative AI to search for a partner based on the user's skill level and the desired partner's conditions. Furthermore, the search unit can use generative AI to search for opponents based on the user's geographical proximity and available time slots. For example, the search unit can use generative AI to search for opponents suitable for the sport selected by the user. The search unit can also use generative AI to search for opponents based on the user's skill level and desired opponent criteria. Furthermore, the search unit can also use generative AI to search for opponents based on the user's geographical proximity and available time slots. The response unit searches for new members when the number of members suddenly decreases. For example, the response unit searches for new members using generative AI when the number of members suddenly decreases. Furthermore, the response unit can also search for new members based on the user's skill level and desired opponent criteria when the number of members suddenly decreases. Furthermore, the response unit can also search for new members based on the user's geographical proximity and available time slots when the number of members suddenly decreases. For example, the response unit searches for new members using generative AI when the number of members suddenly decreases.Furthermore, the system can search for new members based on the user's skill level and desired partner criteria if the number of members suddenly decreases. Additionally, the system can search for new members based on the user's geographical proximity and available time slots if the number of members suddenly decreases. Thus, the sports member matching system according to this embodiment can accept input from the user to select a sport, search for opponents, and search for new members if the number of members suddenly decreases.

[0059] The reception desk accepts input from users to select a sport. This input may include, but is not limited to, the type of sport, date and time, and location. For example, the reception desk accepts the type of sport selected by the user. It can also accept information about the user's preferred date and time and location. Furthermore, the reception desk can accept input about the user's skill level and desired opponent. For example, the reception desk accepts the type of sport selected by the user. It can also accept information about the user's preferred date and time and location. Furthermore, the reception desk can accept input about the user's skill level and desired opponent. For example, if the user selects soccer, the reception desk receives this information and registers it in the system. It can also accept information about the user's preferred date and time and location. For example, if the user wants to play soccer at a nearby park on a weekend afternoon, the reception desk will accept this information. Furthermore, the reception desk can accept input about the user's skill level and desired opponent. For example, if a user is a beginner and wishes to play with another beginner, the reception desk will accept this information. This allows the reception desk to receive detailed user preferences and register them in the system. The reception desk also accepts the type of sport the user has selected as input. For example, if a user selects tennis, the reception desk will receive this information and register it in the system. It can also accept information about the user's preferred date, time, and location. For example, if a user wishes to play tennis at a nearby tennis court on a weekday evening, the reception desk will accept this information. Furthermore, the reception desk can also accept input such as the user's skill level and the desired partner's conditions. For example, if a user is an intermediate player and wishes to play with another intermediate player, the reception desk will accept this information. This allows the reception desk to receive detailed user preferences and register them in the system.

[0060] The search unit uses generative AI to search for opponents based on the sport received by the reception unit. For example, the search unit uses generative AI to search for opponents suitable for the sport selected by the user. The search unit can also use generative AI to search for opponents based on the user's skill level and desired opponent criteria. Furthermore, the search unit can use generative AI to search for opponents based on the user's geographical proximity and available time slots. For example, if the user selects soccer, the generative AI searches the system's database for other users who also want to play soccer. For example, if the user is a beginner and wants to play with a beginner, the generative AI searches for opponents who meet those criteria. Furthermore, the search unit can use generative AI to find opponents based on the user's geographical proximity and available time slots. For example, if a user wants to play soccer in a nearby park, the generative AI will search for opponents who match that condition. This allows the search unit to find the best opponent based on the user's detailed preferences. The search unit uses generative AI to search for opponents suitable for the sport selected by the user. For example, if a user selects tennis, the generative AI will search the system's database for other users who want to play tennis. The search unit can also use generative AI to search for opponents based on the user's skill level and desired opponent conditions. For example, if a user is an intermediate player and wants an intermediate player, the generative AI will search for opponents who match that condition. Furthermore, the search unit can use generative AI to search for opponents based on the user's geographical proximity and available time slots.For example, if a user wants to play tennis at a nearby tennis court, the generating AI will search for an opponent that matches that condition. This allows the search unit to find the best opponent based on the user's detailed preferences.

[0061] The response unit searches for new members when the number of members suddenly decreases. For example, if the number of members suddenly decreases, the response unit uses generative AI to search for new members. The response unit can also search for new members based on the user's skill level and desired partner criteria when the number of members suddenly decreases. Furthermore, the response unit can also search for new members based on the user's geographical proximity and available time slots when the number of members suddenly decreases. For example, if the number of members suddenly decreases during a soccer match, the generative AI searches for new members from the system's database. Furthermore, the system can search for new members based on the user's skill level and desired opponent criteria if the number of members suddenly decreases. For example, if a beginner user suddenly needs a member, the generating AI will search for a new member that matches those criteria. In addition, the system can also search for new members based on the user's geographical proximity and available time slots if the number of members suddenly decreases. For example, if a user playing soccer in a nearby park suddenly needs a member, the generating AI will search for a new member that matches those criteria. This allows the system to quickly search for new members and support the user's sports activities even if the number of members suddenly decreases. The system searches for new members if the number of members suddenly decreases. For example, if the number of members suddenly decreases during a tennis match, the generating AI will search for a new member from the system's database. Furthermore, the system can also search for new members based on the user's skill level and desired opponent criteria if the number of members suddenly decreases.For example, if an intermediate-level user suddenly needs a member, the generating AI will search for a new member that matches those criteria. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's geographical proximity and available time slots. For example, if a user playing tennis at a nearby court suddenly needs a member, the generating AI will search for a new member that matches those criteria. This allows the response unit to quickly find new members even when the number of members suddenly decreases, supporting the user's sports activities.

[0062] The search unit can search for opponents using generative AI. For example, the search unit can use generative AI to search for opponents suitable for a sport selected by the user. For example, the generative AI takes the type of sport selected by the user as input and searches for opponents suitable for that sport. The generative AI can also search for opponents based on the user's skill level and desired opponent criteria. For example, the generative AI takes the user's skill level as input and searches for opponents suitable for that skill level. The generative AI can also take the user's desired opponent criteria as input and search for opponents suitable for those criteria. Furthermore, the generative AI can also search for opponents based on the user's geographical proximity and available time slots. For example, the generative AI takes the user's geographical location information as input and searches for opponents based on that location information. The generative AI can also take the user's available time slots as input and search for opponents based on those time slots. As a result, using generative AI improves the accuracy of opponent searches.

[0063] The response unit can search for new members if the number of members suddenly decreases. For example, if the number of members suddenly decreases, the response unit can use generative AI to search for new members. For example, if the number of members suddenly decreases, the response unit can also search for new members based on the user's skill level and desired partner criteria. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's geographical proximity and available time slots. For example, if the number of members suddenly decreases, the response unit can use generative AI to search for new members. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's skill level and desired partner criteria. Furthermore, if the number of members suddenly decreases, the response unit can also search for new members based on the user's geographical proximity and available time slots. This allows for the rapid search for new members even if the number of members suddenly decreases. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can use a generation AI to search for new members if the number of members suddenly decreases.

[0064] The reception desk can accept input from the user to select a sport. For example, the reception desk can accept the type of sport selected by the user as input. For example, the reception desk can also accept information such as the date, time, and location desired by the user. Furthermore, the reception desk can also accept input such as the user's skill level and the conditions of their desired opponent. For example, the reception desk can accept the type of sport selected by the user as input. Furthermore, the reception desk can also accept information such as the date, time, and location desired by the user. In addition, the reception desk can also accept input such as the user's skill level and the conditions of their desired opponent. This allows the reception desk to accept input from the user to select a sport. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can accept the type of sport selected by the user as input.

[0065] The search unit can find suitable opponents for sports such as golf, soccer, and baseball. For example, it uses generative AI to find opponents suitable for the sport selected by the user. For instance, the generative AI takes the type of sport selected by the user as input and searches for suitable opponents. The generative AI can also search for opponents based on the user's skill level and desired opponent criteria. For example, it takes the user's skill level as input and searches for opponents suitable for that skill level. Furthermore, it can take the user's desired opponent criteria as input and search for opponents suitable for those criteria. In addition, the generative AI can search for opponents based on the user's geographical proximity and available time slots. For example, it takes the user's geographical location as input and searches for opponents based on that location. It can also take the user's available time slots as input and search for opponents based on those time slots. This allows for the search of opponents suitable for a variety of sports.

[0066] The reception desk can estimate the user's emotions and customize the sports selection interface based on those emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface to facilitate sports selection. For example, if the user is relaxed, the reception desk can provide detailed sports information to broaden the options. Furthermore, if the user is excited, the reception desk can provide a visually stimulating interface to make the selection process more enjoyable. This allows for more appropriate sports selection by customizing the interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0067] The reception desk can analyze the user's past sports selection history and suggest the most suitable sport. For example, the reception desk can prioritize displaying sports that the user has frequently selected in the past. For example, the reception desk can suggest sports suitable for a specific season or time of day based on the user's past selection history. Furthermore, the reception desk can also suggest new sports based on the user's past selection history. This improves user satisfaction by suggesting the most suitable sport based on past selection history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past selection history into a generating AI and have the generating AI suggest the most suitable sport.

[0068] The reception desk can filter sports selections based on the user's current physical condition and fitness level. For example, if the user is tired, the reception desk can suggest light exercise. For example, if the user is seeking healthy exercise, the reception desk can suggest moderate exercise. Furthermore, if the user is feeling unwell, the reception desk can also suggest relaxing sports. This allows for more appropriate selections by suggesting sports that match the user's physical condition and fitness level. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's physical condition data into a generating AI and have the generating AI perform the filtering.

[0069] The reception desk can estimate the user's emotions and determine the priority of sports selection based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize displaying relaxing sports. For example, if the user is excited, the reception desk will prioritize displaying energetic sports. Furthermore, if the user is relaxed, the reception desk can also prioritize displaying enjoyable sports. This allows for more appropriate selection by prioritizing sports selection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0070] The reception desk can prioritize displaying highly relevant sports when a user selects a sport, taking into account the user's geographical location. For example, the reception desk can prioritize displaying sports events being held near the user's current location. For example, the reception desk can suggest nearby sports facilities based on the user's geographical location. The reception desk can also suggest easily accessible sports, taking into account the user's geographical location. For example, the reception desk can prioritize displaying sports events being held near the user's current location. For example, the reception desk can suggest nearby sports facilities based on the user's geographical location. Furthermore, the reception desk can suggest easily accessible sports, taking into account the user's geographical location. This allows for the suggestion of more relevant sports by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI suggest highly relevant sports.

[0071] The reception desk can analyze the user's social media activity when selecting a sport and suggest relevant sports. For example, the reception desk can suggest sports that the user has shown interest in on social media. For example, the reception desk can suggest sports events that the user's friends are participating in. Furthermore, the reception desk can also suggest trending sports based on the user's social media activity. This allows the user to select a sport that matches their interests by suggesting relevant sports based on their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant sports.

[0072] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit can display detailed search results. For example, if the user is in a hurry, the search unit can display concise search results. Furthermore, if the user is excited, the search unit can also display visually stimulating search results. This allows for more appropriate search results to be provided by adjusting how search results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust how search results are displayed.

[0073] The search unit can apply different search algorithms depending on the type of sport during a search. For example, in the case of golf, the search unit applies a search algorithm that takes into account the skill level of the players. For example, in the case of soccer, the search unit applies a search algorithm that takes into account the positional balance of the team. Furthermore, in the case of baseball, the search unit can also apply a search algorithm that takes into account the position and skill level of the players. This allows for more accurate search results by applying a search algorithm appropriate to the type of sport. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can have a generative AI execute a search algorithm appropriate to the type of sport.

[0074] The search unit can suggest the most suitable partner by referring to the user's past matching history during a search. For example, the search unit can suggest the most suitable partner based on the user's evaluation of past matches. For example, the search unit can suggest compatible partners based on the user's past matching history. The search unit can also analyze the user's past matching history and suggest the most suitable partner. For example, the search unit can suggest the most suitable partner based on the user's evaluation of past matches. For example, the search unit can suggest compatible partners based on the user's past matching history. Furthermore, the search unit can analyze the user's past matching history and suggest the most suitable partner. This improves user satisfaction by suggesting the most suitable partner based on past matching history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past matching history into a generative AI and have the generative AI suggest the most suitable partner.

[0075] The search unit can estimate the user's emotions and prioritize search results based on the estimated emotions. For example, if the user is stressed, the search unit will prioritize displaying people who can help them relax. For example, if the user is excited, the search unit will prioritize displaying energetic people. Furthermore, if the user is relaxed, the search unit can also prioritize displaying people who can provide enjoyment. By prioritizing search results according to the user's emotions, the search unit can suggest more appropriate matches. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can input user sentiment data into a generating AI, which can then be used to determine the priority of search results.

[0076] The search unit can prioritize displaying highly relevant matches by considering the user's geographical location during a search. For example, the search unit can prioritize displaying matches active in the vicinity of the user's current location. For example, the search unit can suggest matches active at nearby sports facilities based on the user's geographical location. The search unit can also suggest matches that are easily accessible by considering the user's geographical location. For example, the search unit can prioritize displaying matches active in the vicinity of the user's current location. For example, the search unit can suggest matches active at nearby sports facilities based on the user's geographical location. Furthermore, the search unit can suggest matches that are easily accessible by considering the user's geographical location. This allows for the suggestion of more relevant matches by considering the user's geographical location. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's geographical location information into a generative AI and have the generative AI suggest highly relevant matches.

[0077] The search unit can analyze the user's social media activity during a search and suggest relevant individuals. For example, the search unit can suggest individuals who play sports that the user has shown interest in on social media. For example, the search unit can suggest individuals who are participating in a sport event that the user's friends are also participating in. Furthermore, the search unit can suggest individuals who play trending sports based on the user's social media activity. This allows the user to select individuals who match their interests by suggesting relevant individuals based on their social media activity. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not using generative AI. For example, the search unit can input the user's social media data into a generative AI and have the generative AI suggest relevant individuals.

[0078] The response unit can estimate the user's emotions and adjust the search method for new members based on the estimated emotions. For example, if the user is stressed, the response unit provides a simple and intuitive search method. For example, if the user is relaxed, the response unit provides detailed search options. Furthermore, if the user is excited, the response unit can also provide a visually stimulating search method. For example, if the user is stressed, the response unit provides a simple and intuitive search method. Furthermore, if the user is relaxed, the response unit can also provide detailed search options. Furthermore, if the user is excited, the response unit can also provide a visually stimulating search method. This allows for more appropriate new members to be found by adjusting the search method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into the generating AI and have the generating AI adjust the search method.

[0079] The response unit can suggest the most suitable new members by referring to past member history when the number of members suddenly decreases. For example, the response unit can suggest new members based on evaluations of members who have played together in the past. For example, the response unit can suggest compatible new members from past member history. The response unit can also analyze past member history and suggest the most suitable new members. For example, the response unit can suggest new members based on evaluations of members who have played together in the past. For example, the response unit can suggest compatible new members from past member history. Furthermore, the response unit can analyze past member history and suggest the most suitable new members. This allows for the rapid discovery of new members by suggesting the most suitable new members based on past member history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input past member history into a generating AI and have the generating AI suggest the most suitable new members.

[0080] The support unit can filter new members based on the user's current sports activity status when searching for new members. For example, the support unit can suggest new members suitable for the sport the user is currently participating in. For example, the support unit can filter new members based on the user's current sports activity status. The support unit can also suggest the most suitable new members considering the user's current sports activity status. For example, the support unit can suggest new members suitable for the sport the user is currently participating in. The support unit can also filter new members based on the user's current sports activity status. Furthermore, the support unit can also suggest the most suitable new members considering the user's current sports activity status. This allows for more appropriate selection by suggesting new members according to the current sports activity status. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's sports activity data into a generating AI and have the generating AI perform the filtering.

[0081] The response unit can estimate the user's emotions and determine the priority of new members based on the estimated emotions. For example, if the user is stressed, the response unit will prioritize displaying relaxing new members. For example, if the user is excited, the response unit will prioritize displaying energetic new members. Furthermore, if the user is relaxed, the response unit can also prioritize displaying enjoyable new members. For example, if the user is stressed, the response unit will prioritize displaying relaxing new members. Furthermore, if the user is excited, the response unit can also prioritize displaying energetic new members. Furthermore, if the user is relaxed, the response unit can also prioritize displaying enjoyable new members. This allows for the suggestion of more appropriate new members by determining the priority of new members according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into a generating AI and have the generating AI determine the priority of new members.

[0082] The response unit can prioritize displaying highly relevant members when searching for new members, taking into account the user's geographical location. For example, the response unit can prioritize displaying new members who are active near the user's current location. For example, the response unit can suggest new members who are active at nearby sports facilities based on the user's geographical location. The response unit can also suggest easily accessible new members, taking into account the user's geographical location. For example, the response unit can prioritize displaying new members who are active near the user's current location. For example, the response unit can suggest new members who are active at nearby sports facilities based on the user's geographical location. Furthermore, the response unit can suggest easily accessible new members, taking into account the user's geographical location. This allows for the suggestion of more relevant new members by considering the user's geographical location. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant new members.

[0083] The response unit can analyze the user's social media activity when searching for new members and suggest relevant members. For example, the response unit can suggest new members who play sports that the user has shown interest in on social media. For example, the response unit can suggest new members who are participating in sports events that the user's friends are participating in. Furthermore, the response unit can also suggest new members who play trending sports based on the user's social media activity. For example, the response unit can suggest new members who play sports that the user has shown interest in on social media. Furthermore, the response unit can suggest new members who are participating in sports events that the user's friends are participating in. Furthermore, the response unit can also suggest new members who play trending sports based on the user's social media activity. This allows the user to select new members that match their interests by suggesting relevant new members based on social media activity. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the user's social media data into a generating AI and have the generating AI suggest relevant new members.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The sports member matching system can also acquire users' health data and suggest appropriate sports based on their health status. For example, it can acquire users' heart rate and sleep data and suggest light exercise if they are fatigued. It can also consider the user's weight and BMI and suggest sports that support healthy weight management. Furthermore, it can analyze the user's past health data and suggest sports based on long-term health goals. This allows users to choose sports that suit their health status and supports a healthier lifestyle.

[0086] The sports member matching system can further suggest sports based on the user's hobbies and interests. For example, if a user enjoys music, it can suggest exercises performed to music. If a user enjoys outdoor activities, it can suggest sports that take place in nature. Furthermore, if a user is sociable, it can prioritize suggesting team sports. This allows users to choose sports that match their hobbies and interests, making sports more enjoyable.

[0087] The sports member matching system can further estimate the user's emotions and adjust the difficulty level of the sport based on those emotions. For example, if the user is feeling stressed, it can suggest an easy sport that will help them relax. If the user is excited, it can suggest a challenging sport. Furthermore, if the user is relaxed, it can suggest a sport that they can enjoy. This allows for sport selection tailored to the user's emotions, providing a more appropriate sports experience.

[0088] The sports member matching system can further analyze a user's past sports performance data and suggest sports that can improve their performance. For example, it can analyze the results of sports events the user has participated in in the past and suggest a training plan to improve performance. It can also suggest a suitable training partner based on the user's past performance data. Furthermore, it can monitor the user's performance data in real time and provide appropriate feedback. This enables support to improve the user's sports performance.

[0089] The sports member matching system can further estimate the user's emotions and adjust the way sports event notifications are sent based on those emotions. For example, if the user is feeling stressed, a simple and intuitive notification can be sent. If the user is relaxed, detailed event information can be provided. Furthermore, if the user is excited, a visually stimulating notification can be sent. This allows for more effective communication by providing notification methods tailored to the user's emotions.

[0090] The sports member matching system can further enhance connections with local sports communities by considering the user's geographical location. For example, it can suggest sports clubs and teams active near the user's current location. It can also provide information on local sports events and tournaments. Furthermore, it can provide local transportation information and access methods for sports events the user wishes to participate in. This makes it easier for users to connect with local sports communities and broadens the scope of their sports activities.

[0091] The sports member matching system can further estimate the user's emotions and provide content to enhance their motivation for sports based on those emotions. For example, if a user is feeling stressed, it can provide relaxing music or videos. If a user is excited, it can provide energetic content to boost their motivation. Furthermore, if a user is relaxed, it can provide enjoyable content. This makes it possible to support motivation enhancement in accordance with the user's emotions.

[0092] The sports member matching system can further collect user feedback and use it to improve the system. For example, it can collect user ratings of sports events they have participated in and incorporate them into future event suggestions. It can also improve the system's interface and functionality based on user feedback. Furthermore, it can analyze user feedback to improve the accuracy of sports member matching. This enables continuous improvement to increase user satisfaction.

[0093] The sports member matching system can further estimate the user's emotions and adjust the difficulty level of the sport based on those emotions. For example, if the user is feeling stressed, it can suggest an easy sport that will help them relax. If the user is excited, it can suggest a challenging sport. Furthermore, if the user is relaxed, it can suggest a sport that they can enjoy. This allows for sport selection tailored to the user's emotions, providing a more appropriate sports experience.

[0094] The sports member matching system can further estimate the user's emotions and adjust the way sports event notifications are sent based on those emotions. For example, if the user is feeling stressed, a simple and intuitive notification can be sent. If the user is relaxed, detailed event information can be provided. Furthermore, if the user is excited, a visually stimulating notification can be sent. This allows for more effective communication by providing notification methods tailored to the user's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk accepts input from the user to select a sport. This input can include the type of sport the user chooses, date and time, location, skill level, and desired opponent criteria. Step 2: The search unit searches for opponents based on the sport received by the reception unit. Using generative AI, it searches for opponents suitable for the sport selected by the user, and can search for opponents based on skill level, desired opponent conditions, geographical proximity, and available time slots. Step 3: The response unit searches for new members in case of a sudden decrease in members. Using a generation AI, it can search for new members based on the user's skill level, desired partner criteria, geographical proximity, and available time slots.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the reception unit, search unit, and response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input information such as the type of sport, date and time, and location. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, using a generating AI to search for a suitable opponent based on the user's conditions. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to search for a new member if the number of members suddenly decreases. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements, including the reception unit, search unit, and response unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input information such as the type of sport, date and time, and location. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, using generated AI to search for a suitable partner based on the user's conditions. The response unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, to search for a new member if the number of members suddenly decreases. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, search unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input information such as the type of sport, date and time, and location. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, using a generating AI to search for a suitable partner based on the user's criteria. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to search for a new member if the number of members suddenly decreases. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, search unit, and response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input information such as the type of sport, date and time, and location. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, using generated AI to search for a suitable opponent based on the user's conditions. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, searching for a new member if the number of members suddenly decreases. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A reception area where you can input information to select a sport, A search unit that searches for an opponent based on the sport received by the reception unit, It includes a section for searching for new members in the event of a sudden decrease in the number of members. A system characterized by the following features. (Note 2) The aforementioned search unit, Search for opponents using generative AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The corresponding part is, Search for new members if the number of members suddenly decreases. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system accepts input from the user to select a sport. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, Search for a suitable partner for sports such as golf, soccer, and baseball. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and customizes the sports selection interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past sports selection history and suggests the most suitable sport. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When selecting a sport, filtering is performed based on the user's current physical condition and fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of sports selection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When selecting a sport, the system prioritizes displaying sports that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users choose a sport, the system analyzes their social media activity and suggests relevant sports. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching, different search algorithms are applied depending on the type of sport. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When you search, we refer to your past matching history to suggest the most suitable partners. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the system prioritizes displaying highly relevant results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When you search, we analyze your social media activity and suggest relevant people. The system described in Appendix 1, characterized by the features described herein. (Note 18) The corresponding part is, It estimates the user's emotions and adjusts how new members are searched based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The corresponding part is, In the event of a sudden decrease in members, the system will suggest the most suitable new members by referring to past member history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The corresponding part is, When searching for new members, the system filters based on the user's current sports activity status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The corresponding part is, It estimates user sentiment and prioritizes new members based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The corresponding part is, When searching for new members, the system prioritizes displaying members with higher relevance, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The corresponding part is, When searching for new members, the system analyzes the user's social media activity and suggests relevant members. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you can input information to select a sport, A search unit that searches for an opponent based on the sport received by the reception unit, It includes a section for searching for new members in the event of a sudden decrease in the number of members. A system characterized by the following features.

2. The aforementioned search unit, Search for opponents using generative AI The system according to feature 1.

3. The corresponding part is, Search for new members if the number of members suddenly decreases. The system according to feature 1.

4. The aforementioned reception unit is The system accepts input from the user to select a sport. The system according to feature 1.

5. The aforementioned search unit, Search for a suitable partner for sports such as golf, soccer, and baseball. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the sports selection interface based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past sports selection history and suggests the most suitable sport. The system according to feature 1.

8. The aforementioned reception unit is When selecting a sport, filtering is performed based on the user's current physical condition and fitness level. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of sports selection based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When selecting a sport, the system prioritizes displaying sports that are highly relevant to the user's geographical location. The system according to feature 1.

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