system

Abstract

The system according to this embodiment aims to allow users to easily find golf partners. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives the user's golf schedule. The analysis unit analyzes the information received by the reception unit. The search unit searches for a golf partner based on the information analyzed by the analysis unit. The provision unit provides the golf partner found by the search unit to the user.

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 conventional technology, there is a problem that the process of finding a golf partner is time-consuming and it is difficult to cope with sudden member changes.

[0005] The system according to the embodiment aims to enable a user to easily find a golf partner.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives a user's golf schedule. The analysis unit analyzes the information received by the reception unit. The search unit searches for a golf partner based on the information analyzed by the analysis unit. The provision unit provides the golf partner searched by the search unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily find a golf partner. [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 golf matching system according to an embodiment of the present invention is a groundbreaking system that utilizes generative AI to easily find golf partners. This system allows users to input their golf schedule, and the generative AI analyzes this information to suggest the most suitable golf partners. For example, if a user inputs "I want to play golf this weekend," the generative AI analyzes this information and searches for other users who also want to play golf on the same weekend. Furthermore, if the user specifies a desired golf course, the system suggests golf partners that meet those criteria. Even if the number of players suddenly decreases, the generative AI searches for and suggests new members. Thus, the golf matching system utilizing generative AI makes it easy to find golf partners and can accommodate sudden changes, reducing the stress associated with enjoying golf. Additionally, it allows users to play at their desired golf course, providing a more satisfying golf experience. As a result, the golf matching system can efficiently receive, analyze, search, and provide users with their golf schedules.

[0029] The golf matching system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives the user's golf schedule. The user's golf schedule includes, for example, the date and time, location, and number of participants, but is not limited to these examples. The reception unit, for example, stores the golf schedule entered by the user in a database. The reception unit can also transmit the information entered by the user to the analysis unit in real time. The analysis unit analyzes the information received by the reception unit using a generation AI. The analysis unit analyzes, for example, the user's schedule, golf level, and desired golf course. The analysis unit generates data for the generation AI to suggest the most suitable golf partner based on the user's input information. The search unit searches for a golf partner based on the information analyzed by the analysis unit using a generation AI. The search unit, for example, searches for other users who want to play golf on the same weekend. The search unit can also search for other users who are planning to go to the golf course desired by the user. The provision unit provides the golf partner found by the search unit to the user. The provision unit, for example, notifies the user of the search results on their device. Furthermore, the service provider can also save the search results to the user's account. This allows the golf matching system according to the embodiment to efficiently receive, analyze, search, and provide the user's golf schedule.

[0030] The reception desk receives users' golf schedules. These schedules may include, but are not limited to, the date, time, location, and number of participants. Specifically, when users enter their golf schedules through a dedicated application or website, they can also input their preferred playing style, requests for specific golf courses, past playing history, and even criteria for preferred playing partners, in addition to the date, time, location, and number of participants. This allows the reception desk to understand users' detailed needs and enable more accurate matching. The reception desk, for example, stores the golf schedules entered by users in a database. This database is built on the cloud, is secure, and ensures the safe management of users' personal and schedule information. The reception desk can also transmit the information entered by users to the analysis department in real time. This allows the analysis department to receive the information immediately after the user enters the schedule and begin analysis quickly. Furthermore, the reception desk has a function to detect user input errors and incomplete information and provide appropriate feedback. For example, if the date and time are incomplete or the location is ambiguous, a message prompting the user to correct it will be displayed. This allows the reception desk to accurately and efficiently receive users' golf schedules.

[0031] The analysis unit uses generative AI to analyze information received by the reception unit. For example, the analysis unit analyzes information such as the user's schedule, golf level, and preferred golf course. Specifically, the generative AI analyzes information such as the date, time, location, and number of participants entered by the user, taking into account the user's playing style, past playing history, and compatibility with other users. The generative AI uses natural language processing technology to understand the user's input and extract appropriate data. For example, if a user enters "I want to play at a beginner-friendly golf course on the weekend," the generative AI extracts keywords such as "weekend," "beginner-friendly," and "golf course," and performs analysis based on these. The analysis unit generates data to suggest the optimal golf partner based on the user's input information. Specifically, the generative AI combines information such as the user's schedule, golf level, and preferred golf course to execute an algorithm to identify the optimal golf partner. For example, it identifies other users who want to play at the same time and course, and selects users with matching golf levels and playing styles from among them. Furthermore, the analysis unit can continuously improve its analysis algorithm based on past matching data and user feedback. This allows the analysis unit to generate data to suggest the optimal golf partner according to the user's needs, thereby improving the overall accuracy and reliability of the system.

[0032] The search unit uses generative AI to search for golf partners based on information analyzed by the analysis unit. Specifically, the generative AI searches for golf partners that match the user's criteria based on data provided by the analysis unit. For example, when searching for other users who want to play golf on the same weekend, the generative AI identifies the most suitable partner by considering the user's schedule, desired golf course, golf level, and other conditions. The search unit can also search for other users who are planning to play at the same golf course as the user. Specifically, the generative AI identifies other users who want to play under the same conditions based on the golf course and date specified by the user, and selects the most suitable partner from among them. Furthermore, the search unit can provide more accurate search results by considering the user's past playing history and feedback. For example, if the user had good compatibility with a user they played with in the past, that user will be displayed preferentially in the search results. In addition, the search unit can provide search results that reflect the latest information based on data that is updated in real time. As a result, the search unit can quickly and accurately search for and provide the user with the most suitable golf partner that matches the user's criteria.

[0033] The service provider delivers golf partners found by the search unit to the user. Specifically, it notifies the user of the search results on their device. For example, it sends push notifications to the user's smartphone or tablet to inform them of the search results in real time. The service provider can also save the search results to the user's account. This allows the user to review the search results later or try matching again. Furthermore, the service provider has a function to suggest actions to the user based on the search results. For example, it can send a message to the golf partner displayed in the search results and provide a link to check the details of the game. The service provider can also collect user feedback and continuously improve the accuracy and delivery method of the search results. For example, it can provide a function to let the user rate whether they were satisfied with playing with the provided golf partner, and use that feedback to improve the entire system. In this way, the service provider can quickly and accurately provide the user with the best golf partner and improve user satisfaction.

[0034] The adjustment unit can search for new members if the number of members suddenly decreases. For example, if a scheduled golf player suddenly cancels, the adjustment unit will use a generation AI to search for a new member. The adjustment unit can also respond if a user suddenly changes their golf schedule. For example, if a user changes the date and time of their scheduled golf game, the adjustment unit can search for a golf partner that fits the new date and time. This allows the system to respond to sudden changes in members. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's change information into a generation AI and have the generation AI perform a search for a new member.

[0035] The selection unit allows the user to choose their desired golf course. For example, the selection unit can specify a golf course that is easily accessible to the user or a golf course they would like to play at. The selection unit uses a generative AI to search for a golf partner that matches the user's desired golf course. For example, if the user wants to play at a specific golf course, the unit searches for other users who are planning to go to that golf course and performs a match. This allows the user to play at their desired golf course. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input information about the user's desired golf course into the generative AI and have the generative AI perform the search for a golf partner.

[0036] The analysis unit can analyze information such as the user's schedule, golf level, and preferred golf course. For example, the analysis unit can acquire the user's calendar information and analyze their golf schedule. The analysis unit can also evaluate the user's golf level and generate data to suggest the most suitable golf partner. For example, the analysis unit can evaluate the user's golf level based on their handicap and past scores. The analysis unit can also analyze information on the golf course the user desires and suggest a golf partner that meets those criteria. By analyzing detailed user information, it is possible to suggest a more appropriate golf partner. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's schedule information into a generation AI and have the generation AI generate the analysis results.

[0037] The search unit can find the most suitable golf partner based on the analysis results. For example, the search unit suggests the most suitable golf partner based on information such as the user's schedule, golf level, and preferred golf course. The search unit uses generative AI to search for a golf partner that meets the user's criteria. For example, it searches for other users who want to play golf on the same weekend and performs matching. The search unit can also search for other users who are planning to go to the golf course the user wants. This allows the search unit to find the most suitable golf partner based on the analysis results. Some or all of the above processing in the search unit may be performed using generative AI, or it may be performed without generative AI. For example, the search unit can input the analysis results into the generative AI and have the generative AI perform the search for the most suitable golf partner.

[0038] The service provider can provide search results to the user. For example, the service provider can notify the user of the search results on the user's device. The service provider can also save the search results to the user's account. The service provider uses generative AI to suggest the best way to provide the search results to the user. For example, the service provider can send a push notification to the user's device and display the search results. The service provider can also send the search results to the user's email address. This allows the user to find a suitable golf partner by providing them with the search results. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the search results into a generative AI and have the generative AI suggest the best way to provide them.

[0039] The reception desk can analyze the user's past golf schedule history and select the most suitable reception method. For example, the reception desk can prioritize suggesting golf courses that the user has frequently used in the past. The reception desk can also consider the days of the week and times the user has used in the past when processing reservations. Furthermore, the reception desk can suggest the most suitable golf partner based on the user's past golf schedule history. In this way, the reception desk can select the most suitable reception method by analyzing the user's past golf schedule history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past golf schedule history into a generating AI and have the generating AI select the most suitable reception method.

[0040] The reception desk can filter golf reservations based on the user's current lifestyle and areas of interest. For example, if the user is busy with work, the reception desk will prioritize suggesting weekend golf reservations. It can also suggest health-conscious golf courses if the user is interested in health. Furthermore, if the user values ​​spending time with family, it can suggest family-friendly golf courses. This allows for more appropriate golf reservations to be suggested by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input information about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize accepting golf reservations by considering the user's geographical location when the reservation is submitted. For example, the reception desk can prioritize suggesting golf courses close to the user's current location. It can also prioritize suggesting golf courses in areas the user frequently visits. Furthermore, if the user is traveling, the reception desk can prioritize suggesting golf courses in their travel destination. In this way, by considering the user's geographical location, the reception desk can prioritize accepting golf reservations that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize accepting reservations that are highly relevant.

[0042] The reception desk can analyze a user's social media activity when they submit a golf reservation and submit relevant reservations. For example, the reception desk can prioritize suggesting golf courses that the user has shared on social media. It can also suggest reservations where the user can play with golf friends they follow on social media. Furthermore, the reception desk can suggest golf events that the user has shown interest in on social media. In this way, relevant golf reservations can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI submit relevant reservations.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the golf activity. For example, the analysis unit will perform a detailed analysis for important golf events. It can also perform a simplified analysis for casual golf play. Furthermore, it can perform a detailed analysis for golf courses of particular interest to the user. By adjusting the level of detail based on the importance of the golf activity, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information on the importance of the golf activity into a generative AI and have the generative AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the golf category during analysis. For example, in the case of professional golf play, the analysis unit can apply a specialized analysis algorithm. In the case of amateur golf play, the analysis unit can also apply a simplified analysis algorithm. Furthermore, in the case of a golf event, the analysis unit can apply an event-specific analysis algorithm. By applying different analysis algorithms according to the golf category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information about the golf category into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of golf records. For example, the analysis unit can prioritize analysis for urgent golf appointments. It can also postpone analysis for golf appointments scheduled for later dates. Furthermore, it can prioritize analysis for golf events with approaching submission deadlines. By prioritizing analysis based on the submission timing of golf records, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input information on the submission timing of golf records into a generation AI and have the generation AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of golf during the analysis process. For example, the analysis unit may prioritize analyzing golf courses that the user is particularly interested in. It can also prioritize analyzing golf courses that the user frequently uses. Furthermore, the analysis unit may postpone the analysis of golf courses that the user is using for the first time. By adjusting the order of analysis based on the relevance of golf, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information on the relevance of golf into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0047] The search unit can improve search accuracy by considering the relationships between golf partners during the search process. For example, the search unit can prioritize searching for golf partners who have played with the user in the past. It can also prioritize searching for golf partners recommended by the user's friends or acquaintances. Furthermore, the search unit can prioritize searching for partners who belong to the same golf club as the user. This improves search accuracy by considering the relationships between golf partners. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input information about the relationships between golf partners into a generative AI and have the generative AI perform the search accuracy improvement.

[0048] The search unit can perform searches while considering the attribute information of the golf partner. For example, the search unit can consider the age and gender of the golf partner when performing a search. It can also consider the golf level of the golf partner when performing a search. Furthermore, it can also consider the playing style of the golf partner when performing a search. By considering the attribute information of the golf partner, it is possible to provide more appropriate search results. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input the attribute information of the golf partner into a generation AI and have the generation AI perform the search.

[0049] The search unit can perform searches while considering the geographical distribution of golf partners. For example, the search unit can prioritize searching for golf partners close to the user's current location. It can also prioritize searching for golf partners in areas the user frequently visits. Furthermore, if the user is traveling, the search unit can prioritize searching for golf partners in their travel destination. By considering the geographical distribution of golf partners, it can provide more appropriate search results. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input information on the geographical distribution of golf partners into a generative AI and have the generative AI perform the search.

[0050] The search unit can improve search accuracy by referring to relevant literature related to the golf partner during the search. For example, the search unit can search by referring to the golf partner's past playing history. The search unit can also search by referring to the golf partner's ratings and reviews. Furthermore, the search unit can search by referring to information about the golf partner's affiliated club or organization. In this way, the search accuracy can be improved by referring to relevant literature related to the golf partner. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input information about the golf partner's relevant literature into a generative AI and have the generative AI perform the search accuracy improvement.

[0051] The service provider can analyze the user's past golf partner history to select the optimal service provider method at the time of service provision. For example, the service provider can prioritize suggesting golf partners the user has played with in the past. The service provider can also suggest the most suitable golf partner based on the user's past golf partner history. Furthermore, the service provider can analyze the user's past golf partner history to select the optimal service provider method. This allows the service provider to select the optimal service provider method by analyzing the user's past golf partner history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past golf partner history into a generating AI and have the generating AI select the optimal service provider method.

[0052] The service provider can customize the means of providing information based on the user's current living situation at the time of delivery. For example, if the user is busy with work, the service provider can provide concise information. If the user is relaxed, the service provider can also provide detailed information. Furthermore, if the user is traveling, the service provider can prioritize suggesting golf partners at their travel destination. By customizing the means of providing information based on the user's living situation, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input information about the user's living situation into a generating AI and have the generating AI perform the customization of the means of providing information.

[0053] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize suggesting golf partners close to the user's current location. It can also prioritize suggesting golf partners in areas the user frequently visits. Furthermore, if the user is traveling, the service provider can prioritize suggesting golf partners in their travel destination. This allows for more appropriate information delivery by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0054] The service provider can analyze the user's social media activity and propose methods of provision at the time of provision. For example, the service provider can prioritize suggesting golf courses that the user has shared on social media. It can also suggest schedules for playing with golf friends that the user follows on social media. Furthermore, it can suggest golf events that the user has shown interest in on social media. This allows for more appropriate information to be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI propose methods of provision.

[0055] The adjustment unit can analyze the user's past golf partner history during the adjustment process to select the optimal adjustment method. For example, the adjustment unit may prioritize suggesting golf partners the user has played with in the past. The adjustment unit can also suggest the most suitable golf partner based on the user's past golf partner history. Furthermore, the adjustment unit can analyze the user's past golf partner history to select the optimal adjustment method. In this way, the optimal adjustment method can be selected by analyzing the user's past golf partner history. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past golf partner history into a generating AI and have the generating AI select the optimal adjustment method.

[0056] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, the adjustment unit can prioritize suggesting golf partners close to the user's current location. It can also prioritize suggesting golf partners in areas the user frequently visits. Furthermore, if the user is traveling, the adjustment unit can prioritize suggesting golf partners in their travel destination. This allows for more appropriate adjustments by considering the user's geographical location information. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal adjustment method.

[0057] The selection unit can analyze the user's past golf course selection history to select the optimal selection method. For example, the selection unit may prioritize suggesting golf courses the user has played at in the past. It can also suggest the most suitable golf course based on the user's past selection history. Furthermore, the selection unit can analyze the user's past golf course selection history to select the optimal selection method. This allows the selection of the optimal method by analyzing the user's past golf course selection history. Some or all of the above processing in the selection unit may be performed using AI, or without AI. For example, the selection unit can input the user's past golf course selection history into a generating AI and have the generating AI select the optimal selection method.

[0058] The selection unit can choose the optimal selection method by considering the user's geographical location information during the selection process. For example, the selection unit can prioritize suggesting golf courses close to the user's current location. It can also prioritize suggesting golf courses in areas the user frequently visits. Furthermore, if the user is traveling, the selection unit can prioritize suggesting golf courses in their travel destination. This allows for more appropriate selections by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal selection method.

[0059] The selection unit can analyze the user's social media activity and suggest selection options during the selection process. For example, the selection unit might prioritize suggesting golf courses shared by the user on social media. It could also suggest golf courses where the user can play with golf buddies they follow on social media. Furthermore, it could suggest golf events the user has shown interest in on social media. This allows for more appropriate selections by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit could input data on the user's social media activity into a generating AI and have the generating AI suggest selection options.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The golf matching system can also be equipped with a notification function. This function can send real-time notifications to other users when a user enters a golf schedule. For example, if a user enters "I want to play golf this weekend," the notification function can send a push notification to other users. Furthermore, if a user specifies a desired golf course, the notification function can also send notifications to other users who are planning to play at that course. Additionally, if the number of members suddenly decreases, the notification function can send a notification to other users requesting new members. This allows users to quickly find golf partners and makes it easier to adapt to sudden changes.

[0062] The golf matching system can also include an evaluation unit. This unit can collect and analyze evaluations of golf partners the user has played with in the past. For example, if a user enters an evaluation of their partner's manners and skills after a round of golf, the evaluation unit can save this information in a database and use it for future matching. The evaluation unit can also prioritize suggesting partners who have received high ratings from the user. Furthermore, the evaluation unit can provide a filtering function to exclude partners who have received low ratings from the user. This allows users to have a more comfortable golfing experience.

[0063] The golf matching system can also include a customization feature. This customization feature can tailor the suggestions for golf partners to the user's individual needs and preferences. For example, if the user is a member of a specific golf club, other members of that club can be prioritized. Similarly, if the user prefers a particular style of golf (e.g., casual play or competitive play), partners suited to that style can be suggested. Furthermore, the customization feature can suggest the most suitable golf partner based on the user's past playing history and ratings. This ensures that the user can play with the most appropriate partner.

[0064] The golf matching system can also include a scheduling unit. This unit can retrieve the user's calendar information and coordinate schedules with other users. For example, when a user enters a golf appointment, the scheduling unit can refer to other users' calendar information and suggest the most suitable date and time. Furthermore, the scheduling unit can readjust schedules with other users if the user suddenly changes their plans. Additionally, if a user has multiple golf appointments, the scheduling unit can efficiently manage those appointments. This allows users to plan their golf games smoothly.

[0065] The golf matching system can also include a reminder function. This reminder function can set and send reminders based on the user's golf schedule. For example, when a user enters a golf schedule, the reminder function can send a reminder the day before the scheduled date. It can also send a reminder before departure, taking into account the user's arrival time at the golf course. Furthermore, the reminder function can send a list of necessary items as a reminder to ensure the user doesn't forget their golf preparations. This allows users to prepare smoothly without forgetting their golf schedule.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the user's golf schedule. The user's golf schedule includes the date and time, location, and number of participants. The reception desk can also save the golf schedule entered by the user to a database and send it to the analysis department in real time. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit analyzes information such as the user's schedule, golf level, and preferred golf course, and generates data to suggest the most suitable golf partner. Step 3: The search unit uses generational AI to search for golf partners based on the information analyzed by the analysis unit. The search unit searches for other users who want to play golf on the same weekend, or other users who plan to go to the golf course of the user's choice. Step 4: The service provider provides the user with golf partners found by the search unit. The service provider can also notify the user of the search results on the user's device and save them to the user's account.

[0068] (Example of form 2) The golf matching system according to an embodiment of the present invention is a groundbreaking system that utilizes generative AI to easily find golf partners. This system allows users to input their golf schedule, and the generative AI analyzes this information to suggest the most suitable golf partners. For example, if a user inputs "I want to play golf this weekend," the generative AI analyzes this information and searches for other users who also want to play golf on the same weekend. Furthermore, if the user specifies a desired golf course, the system suggests golf partners that meet those criteria. Even if the number of players suddenly decreases, the generative AI searches for and suggests new members. Thus, the golf matching system utilizing generative AI makes it easy to find golf partners and can accommodate sudden changes, reducing the stress associated with enjoying golf. Additionally, it allows users to play at their desired golf course, providing a more satisfying golf experience. As a result, the golf matching system can efficiently receive, analyze, search, and provide users with their golf schedules.

[0069] The golf matching system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives the user's golf schedule. The user's golf schedule includes, for example, the date and time, location, and number of participants, but is not limited to these examples. The reception unit, for example, stores the golf schedule entered by the user in a database. The reception unit can also transmit the information entered by the user to the analysis unit in real time. The analysis unit analyzes the information received by the reception unit using a generation AI. The analysis unit analyzes, for example, the user's schedule, golf level, and desired golf course. The analysis unit generates data for the generation AI to suggest the most suitable golf partner based on the user's input information. The search unit searches for a golf partner based on the information analyzed by the analysis unit using a generation AI. The search unit, for example, searches for other users who want to play golf on the same weekend. The search unit can also search for other users who are planning to go to the golf course desired by the user. The provision unit provides the golf partner found by the search unit to the user. The provision unit, for example, notifies the user of the search results on their device. Furthermore, the service provider can also save the search results to the user's account. This allows the golf matching system according to the embodiment to efficiently receive, analyze, search, and provide the user's golf schedule.

[0070] The reception desk receives users' golf schedules. These schedules may include, but are not limited to, the date, time, location, and number of participants. Specifically, when users enter their golf schedules through a dedicated application or website, they can also input their preferred playing style, requests for specific golf courses, past playing history, and even criteria for preferred playing partners, in addition to the date, time, location, and number of participants. This allows the reception desk to understand users' detailed needs and enable more accurate matching. The reception desk, for example, stores the golf schedules entered by users in a database. This database is built on the cloud, is secure, and ensures the safe management of users' personal and schedule information. The reception desk can also transmit the information entered by users to the analysis department in real time. This allows the analysis department to receive the information immediately after the user enters the schedule and begin analysis quickly. Furthermore, the reception desk has a function to detect user input errors and incomplete information and provide appropriate feedback. For example, if the date and time are incomplete or the location is ambiguous, a message prompting the user to correct it will be displayed. This allows the reception desk to accurately and efficiently receive users' golf schedules.

[0071] The analysis unit uses generative AI to analyze information received by the reception unit. For example, the analysis unit analyzes information such as the user's schedule, golf level, and preferred golf course. Specifically, the generative AI analyzes information such as the date, time, location, and number of participants entered by the user, taking into account the user's playing style, past playing history, and compatibility with other users. The generative AI uses natural language processing technology to understand the user's input and extract appropriate data. For example, if a user enters "I want to play at a beginner-friendly golf course on the weekend," the generative AI extracts keywords such as "weekend," "beginner-friendly," and "golf course," and performs analysis based on these. The analysis unit generates data to suggest the optimal golf partner based on the user's input information. Specifically, the generative AI combines information such as the user's schedule, golf level, and preferred golf course to execute an algorithm to identify the optimal golf partner. For example, it identifies other users who want to play at the same time and course, and selects users with matching golf levels and playing styles from among them. Furthermore, the analysis unit can continuously improve its analysis algorithm based on past matching data and user feedback. This allows the analysis unit to generate data to suggest the optimal golf partner according to the user's needs, thereby improving the overall accuracy and reliability of the system.

[0072] The search unit uses generative AI to search for golf partners based on information analyzed by the analysis unit. Specifically, the generative AI searches for golf partners that match the user's criteria based on data provided by the analysis unit. For example, when searching for other users who want to play golf on the same weekend, the generative AI identifies the most suitable partner by considering the user's schedule, desired golf course, golf level, and other conditions. The search unit can also search for other users who are planning to play at the same golf course as the user. Specifically, the generative AI identifies other users who want to play under the same conditions based on the golf course and date specified by the user, and selects the most suitable partner from among them. Furthermore, the search unit can provide more accurate search results by considering the user's past playing history and feedback. For example, if the user had good compatibility with a user they played with in the past, that user will be displayed preferentially in the search results. In addition, the search unit can provide search results that reflect the latest information based on data that is updated in real time. As a result, the search unit can quickly and accurately search for and provide the user with the most suitable golf partner that matches the user's criteria.

[0073] The service provider delivers golf partners found by the search unit to the user. Specifically, it notifies the user of the search results on their device. For example, it sends push notifications to the user's smartphone or tablet to inform them of the search results in real time. The service provider can also save the search results to the user's account. This allows the user to review the search results later or try matching again. Furthermore, the service provider has a function to suggest actions to the user based on the search results. For example, it can send a message to the golf partner displayed in the search results and provide a link to check the details of the game. The service provider can also collect user feedback and continuously improve the accuracy and delivery method of the search results. For example, it can provide a function to let the user rate whether they were satisfied with playing with the provided golf partner, and use that feedback to improve the entire system. In this way, the service provider can quickly and accurately provide the user with the best golf partner and improve user satisfaction.

[0074] The adjustment unit can search for new members if the number of members suddenly decreases. For example, if a scheduled golf player suddenly cancels, the adjustment unit will use a generation AI to search for a new member. The adjustment unit can also respond if a user suddenly changes their golf schedule. For example, if a user changes the date and time of their scheduled golf game, the adjustment unit can search for a golf partner that fits the new date and time. This allows the system to respond to sudden changes in members. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's change information into a generation AI and have the generation AI perform a search for a new member.

[0075] The selection unit allows the user to choose their desired golf course. For example, the selection unit can specify a golf course that is easily accessible to the user or a golf course they would like to play at. The selection unit uses a generative AI to search for a golf partner that matches the user's desired golf course. For example, if the user wants to play at a specific golf course, the unit searches for other users who are planning to go to that golf course and performs a match. This allows the user to play at their desired golf course. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input information about the user's desired golf course into the generative AI and have the generative AI perform the search for a golf partner.

[0076] The analysis unit can analyze information such as the user's schedule, golf level, and preferred golf course. For example, the analysis unit can acquire the user's calendar information and analyze their golf schedule. The analysis unit can also evaluate the user's golf level and generate data to suggest the most suitable golf partner. For example, the analysis unit can evaluate the user's golf level based on their handicap and past scores. The analysis unit can also analyze information on the golf course the user desires and suggest a golf partner that meets those criteria. By analyzing detailed user information, it is possible to suggest a more appropriate golf partner. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's schedule information into a generation AI and have the generation AI generate the analysis results.

[0077] The search unit can find the most suitable golf partner based on the analysis results. For example, the search unit suggests the most suitable golf partner based on information such as the user's schedule, golf level, and preferred golf course. The search unit uses generative AI to search for a golf partner that meets the user's criteria. For example, it searches for other users who want to play golf on the same weekend and performs matching. The search unit can also search for other users who are planning to go to the golf course the user wants. This allows the search unit to find the most suitable golf partner based on the analysis results. Some or all of the above processing in the search unit may be performed using generative AI, or it may be performed without generative AI. For example, the search unit can input the analysis results into the generative AI and have the generative AI perform the search for the most suitable golf partner.

[0078] The service provider can provide search results to the user. For example, the service provider can notify the user of the search results on the user's device. The service provider can also save the search results to the user's account. The service provider uses generative AI to suggest the best way to provide the search results to the user. For example, the service provider can send a push notification to the user's device and display the search results. The service provider can also send the search results to the user's email address. This allows the user to find a suitable golf partner by providing them with the search results. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the search results into a generative AI and have the generative AI suggest the best way to provide them.

[0079] The reception desk can estimate the user's emotions and adjust the timing of golf reservation registration based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can register the golf reservation at a time when the user can relax. If the user is excited, the reception desk can register the golf reservation immediately. Furthermore, if the user is tired, the reception desk can register the golf reservation the following morning. This allows for registration at a more appropriate time by adjusting the timing of golf reservation registration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk can analyze the user's past golf schedule history and select the most suitable reception method. For example, the reception desk can prioritize suggesting golf courses that the user has frequently used in the past. The reception desk can also consider the days of the week and times the user has used in the past when processing reservations. Furthermore, the reception desk can suggest the most suitable golf partner based on the user's past golf schedule history. In this way, the reception desk can select the most suitable reception method by analyzing the user's past golf schedule history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past golf schedule history into a generating AI and have the generating AI select the most suitable reception method.

[0081] The reception desk can filter golf reservations based on the user's current lifestyle and areas of interest. For example, if the user is busy with work, the reception desk will prioritize suggesting weekend golf reservations. It can also suggest health-conscious golf courses if the user is interested in health. Furthermore, if the user values ​​spending time with family, it can suggest family-friendly golf courses. This allows for more appropriate golf reservations to be suggested by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input information about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0082] The reception desk can estimate the user's emotions and determine the priority of golf appointments to accept based on those emotions. For example, if the user is feeling stressed, the reception desk will prioritize relaxing golf appointments. If the user is excited, the reception desk can also prioritize appointments that can be played immediately. Furthermore, if the user is tired, the reception desk can prioritize appointments for the following day. This allows for the suggestion of more appropriate golf appointments by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception desk can prioritize accepting golf reservations by considering the user's geographical location when the reservation is submitted. For example, the reception desk can prioritize suggesting golf courses close to the user's current location. It can also prioritize suggesting golf courses in areas the user frequently visits. Furthermore, if the user is traveling, the reception desk can prioritize suggesting golf courses in their travel destination. In this way, by considering the user's geographical location, the reception desk can prioritize accepting golf reservations that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize accepting reservations that are highly relevant.

[0084] The reception desk can analyze a user's social media activity when they submit a golf reservation and submit relevant reservations. For example, the reception desk can prioritize suggesting golf courses that the user has shared on social media. It can also suggest reservations where the user can play with golf friends they follow on social media. Furthermore, the reception desk can suggest golf events that the user has shown interest in on social media. In this way, relevant golf reservations can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI submit relevant reservations.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the golf activity. For example, the analysis unit will perform a detailed analysis for important golf events. It can also perform a simplified analysis for casual golf play. Furthermore, it can perform a detailed analysis for golf courses of particular interest to the user. By adjusting the level of detail based on the importance of the golf activity, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information on the importance of the golf activity into a generative AI and have the generative AI adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the golf category during analysis. For example, in the case of professional golf play, the analysis unit can apply a specialized analysis algorithm. In the case of amateur golf play, the analysis unit can also apply a simplified analysis algorithm. Furthermore, in the case of a golf event, the analysis unit can apply an event-specific analysis algorithm. By applying different analysis algorithms according to the golf category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information about the golf category into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short analysis result. It can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without the generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the submission timing of golf records. For example, the analysis unit can prioritize analysis for urgent golf appointments. It can also postpone analysis for golf appointments scheduled for later dates. Furthermore, it can prioritize analysis for golf events with approaching submission deadlines. By prioritizing analysis based on the submission timing of golf records, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input information on the submission timing of golf records into a generation AI and have the generation AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of golf during the analysis process. For example, the analysis unit may prioritize analyzing golf courses that the user is particularly interested in. It can also prioritize analyzing golf courses that the user frequently uses. Furthermore, the analysis unit may postpone the analysis of golf courses that the user is using for the first time. By adjusting the order of analysis based on the relevance of golf, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information on the relevance of golf into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0091] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is relaxed, the search unit can apply broad search criteria. If the user is in a hurry, the search unit can also apply narrow search criteria. Furthermore, if the user is excited, the search unit can provide visually appealing search results. By adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 search unit may be performed using or without generative AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the search criteria.

[0092] The search unit can improve search accuracy by considering the relationships between golf partners during the search process. For example, the search unit can prioritize searching for golf partners who have played with the user in the past. It can also prioritize searching for golf partners recommended by the user's friends or acquaintances. Furthermore, the search unit can prioritize searching for partners who belong to the same golf club as the user. This improves search accuracy by considering the relationships between golf partners. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input information about the relationships between golf partners into a generative AI and have the generative AI perform the search accuracy improvement.

[0093] The search unit can perform searches while considering the attribute information of the golf partner. For example, the search unit can consider the age and gender of the golf partner when performing a search. It can also consider the golf level of the golf partner when performing a search. Furthermore, it can also consider the playing style of the golf partner when performing a search. By considering the attribute information of the golf partner, it is possible to provide more appropriate search results. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input the attribute information of the golf partner into a generation AI and have the generation AI perform the search.

[0094] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit may prioritize displaying detailed search results. It can also prioritize displaying concise search results if the user is in a hurry. Furthermore, if the user is excited, the search unit may prioritize displaying visually appealing search results. This allows for more appropriate search results to be provided by adjusting the display order of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using or without generative AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the display order of search results.

[0095] The search unit can perform searches while considering the geographical distribution of golf partners. For example, the search unit can prioritize searching for golf partners close to the user's current location. It can also prioritize searching for golf partners in areas the user frequently visits. Furthermore, if the user is traveling, the search unit can prioritize searching for golf partners in their travel destination. By considering the geographical distribution of golf partners, it can provide more appropriate search results. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input information on the geographical distribution of golf partners into a generative AI and have the generative AI perform the search.

[0096] The search unit can improve search accuracy by referring to relevant literature related to the golf partner during the search. For example, the search unit can search by referring to the golf partner's past playing history. The search unit can also search by referring to the golf partner's ratings and reviews. Furthermore, the search unit can search by referring to information about the golf partner's affiliated club or organization. In this way, the search accuracy can be improved by referring to relevant literature related to the golf partner. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input information about the golf partner's relevant literature into a generative AI and have the generative AI perform the search accuracy improvement.

[0097] The information delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the delivery unit can provide detailed information. If the user is in a hurry, the delivery unit can provide concise information. Furthermore, if the user is excited, the delivery unit can provide visually appealing information. By adjusting the delivery method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the delivery method.

[0098] The service provider can analyze the user's past golf partner history to select the optimal service provider method at the time of service provision. For example, the service provider can prioritize suggesting golf partners the user has played with in the past. The service provider can also suggest the most suitable golf partner based on the user's past golf partner history. Furthermore, the service provider can analyze the user's past golf partner history to select the optimal service provider method. This allows the service provider to select the optimal service provider method by analyzing the user's past golf partner history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past golf partner history into a generating AI and have the generating AI select the optimal service provider method.

[0099] The service provider can customize the means of providing information based on the user's current living situation at the time of delivery. For example, if the user is busy with work, the service provider can provide concise information. If the user is relaxed, the service provider can also provide detailed information. Furthermore, if the user is traveling, the service provider can prioritize suggesting golf partners at their travel destination. By customizing the means of providing information based on the user's living situation, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input information about the user's living situation into a generating AI and have the generating AI perform the customization of the means of providing information.

[0100] The service provider can estimate the user's emotions and determine the priority of services based on those emotions. For example, if the user is stressed, the service provider will prioritize suggesting a relaxing golf partner. If the user is excited, the service provider can also prioritize suggesting a golf partner who is available to play immediately. Furthermore, if the user is tired, the service provider can prioritize suggesting a golf partner for the following day. This allows for more appropriate information to be provided by prioritizing services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of services.

[0101] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize suggesting golf partners close to the user's current location. It can also prioritize suggesting golf partners in areas the user frequently visits. Furthermore, if the user is traveling, the service provider can prioritize suggesting golf partners in their travel destination. This allows for more appropriate information delivery by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0102] The service provider can analyze the user's social media activity and propose methods of provision at the time of provision. For example, the service provider can prioritize suggesting golf courses that the user has shared on social media. It can also suggest schedules for playing with golf friends that the user follows on social media. Furthermore, it can suggest golf events that the user has shown interest in on social media. This allows for more appropriate information to be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI propose methods of provision.

[0103] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can provide a detailed adjustment method. If the user is in a hurry, the adjustment unit can also provide a concise adjustment method. Furthermore, if the user is excited, the adjustment unit can provide a visually appealing adjustment method. This allows for more appropriate adjustments by adjusting the adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the adjustment method.

[0104] The adjustment unit can analyze the user's past golf partner history during the adjustment process to select the optimal adjustment method. For example, the adjustment unit may prioritize suggesting golf partners the user has played with in the past. The adjustment unit can also suggest the most suitable golf partner based on the user's past golf partner history. Furthermore, the adjustment unit can analyze the user's past golf partner history to select the optimal adjustment method. In this way, the optimal adjustment method can be selected by analyzing the user's past golf partner history. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past golf partner history into a generating AI and have the generating AI select the optimal adjustment method.

[0105] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit will prioritize suggesting a relaxing golf partner. If the user is excited, the adjustment unit can also prioritize suggesting a golf partner available immediately. Furthermore, if the user is tired, the adjustment unit can prioritize suggesting a golf partner for the following day. This allows for more appropriate adjustments by prioritizing adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of adjustments.

[0106] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, the adjustment unit can prioritize suggesting golf partners close to the user's current location. It can also prioritize suggesting golf partners in areas the user frequently visits. Furthermore, if the user is traveling, the adjustment unit can prioritize suggesting golf partners in their travel destination. This allows for more appropriate adjustments by considering the user's geographical location information. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal adjustment method.

[0107] The selection unit can estimate the user's emotions and adjust the selection method based on the estimated emotions. For example, if the user is relaxed, the selection unit can provide a detailed selection method. If the user is in a hurry, it can also provide a concise selection method. Furthermore, if the user is excited, it can provide a visually appealing selection method. This allows for more appropriate choices by adjusting the selection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI adjust the selection method.

[0108] The selection unit can analyze the user's past golf course selection history to select the optimal selection method. For example, the selection unit may prioritize suggesting golf courses the user has played at in the past. It can also suggest the most suitable golf course based on the user's past selection history. Furthermore, the selection unit can analyze the user's past golf course selection history to select the optimal selection method. This allows the selection of the optimal method by analyzing the user's past golf course selection history. Some or all of the above processing in the selection unit may be performed using AI, or without AI. For example, the selection unit can input the user's past golf course selection history into a generating AI and have the generating AI select the optimal selection method.

[0109] The selection unit can estimate the user's emotions and determine selection priorities based on those emotions. For example, if the user is feeling stressed, the selection unit will prioritize suggesting relaxing golf courses. If the user is excited, the selection unit can also prioritize suggesting golf courses available for immediate play. Furthermore, if the user is tired, the selection unit can prioritize suggesting golf courses for the following day. This allows for more appropriate choices by prioritizing selections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI determine the selection priorities.

[0110] The selection unit can choose the optimal selection method by considering the user's geographical location information during the selection process. For example, the selection unit can prioritize suggesting golf courses close to the user's current location. It can also prioritize suggesting golf courses in areas the user frequently visits. Furthermore, if the user is traveling, the selection unit can prioritize suggesting golf courses in their travel destination. This allows for more appropriate selections by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal selection method.

[0111] The selection unit can analyze the user's social media activity and suggest selection options during the selection process. For example, the selection unit might prioritize suggesting golf courses shared by the user on social media. It could also suggest golf courses where the user can play with golf buddies they follow on social media. Furthermore, it could suggest golf events the user has shown interest in on social media. This allows for more appropriate selections by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit could input data on the user's social media activity into a generating AI and have the generating AI suggest selection options.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The golf matching system can also be equipped with a notification function. This function can send real-time notifications to other users when a user enters a golf schedule. For example, if a user enters "I want to play golf this weekend," the notification function can send a push notification to other users. Furthermore, if a user specifies a desired golf course, the notification function can also send notifications to other users who are planning to play at that course. Additionally, if the number of members suddenly decreases, the notification function can send a notification to other users requesting new members. This allows users to quickly find golf partners and makes it easier to adapt to sudden changes.

[0114] The golf matching system can also include an evaluation unit. This unit can collect and analyze evaluations of golf partners the user has played with in the past. For example, if a user enters an evaluation of their partner's manners and skills after a round of golf, the evaluation unit can save this information in a database and use it for future matching. The evaluation unit can also prioritize suggesting partners who have received high ratings from the user. Furthermore, the evaluation unit can provide a filtering function to exclude partners who have received low ratings from the user. This allows users to have a more comfortable golfing experience.

[0115] The golf matching system can also include a customization feature. This customization feature can tailor the suggestions for golf partners to the user's individual needs and preferences. For example, if the user is a member of a specific golf club, other members of that club can be prioritized. Similarly, if the user prefers a particular style of golf (e.g., casual play or competitive play), partners suited to that style can be suggested. Furthermore, the customization feature can suggest the most suitable golf partner based on the user's past playing history and ratings. This ensures that the user can play with the most appropriate partner.

[0116] The golf matching system can also include a scheduling unit. This unit can retrieve the user's calendar information and coordinate schedules with other users. For example, when a user enters a golf appointment, the scheduling unit can refer to other users' calendar information and suggest the most suitable date and time. Furthermore, the scheduling unit can readjust schedules with other users if the user suddenly changes their plans. Additionally, if a user has multiple golf appointments, the scheduling unit can efficiently manage those appointments. This allows users to plan their golf games smoothly.

[0117] The golf matching system can also include a reminder function. This reminder function can set and send reminders based on the user's golf schedule. For example, when a user enters a golf schedule, the reminder function can send a reminder the day before the scheduled date. It can also send a reminder before departure, taking into account the user's arrival time at the golf course. Furthermore, the reminder function can send a list of necessary items as a reminder to ensure the user doesn't forget their golf preparations. This allows users to prepare smoothly without forgetting their golf schedule.

[0118] The golf matching system can further customize golf partner suggestions based on the user's emotions using emotion estimation functionality. For example, if the user is feeling stressed, it can suggest a relaxing golf partner. If the user is excited, it can suggest a competitive golf partner. Furthermore, if the user is tired, it can suggest a golf partner with a casual playing style. This allows the system to provide a more satisfying golf experience by suggesting the optimal golf partner according to the user's emotions.

[0119] The golf matching system can further adjust golf schedules based on the user's emotions using emotion estimation technology. For example, if a user is feeling stressed, it can suggest a golf schedule for a relaxing time. If the user is excited, it can suggest a time when they can play immediately. Furthermore, if the user is tired, it can suggest a golf schedule for the following morning. This allows users to enjoy golf at the most appropriate time by suggesting the optimal golf schedule according to their emotions.

[0120] The golf matching system can further customize golf course selections based on the user's emotions using emotion estimation capabilities. For example, if the user is relaxed, it can suggest a golf course with a quiet environment. If the user is excited, it can suggest a golf course with an active event. Furthermore, if the user is tired, it can suggest a nearby golf course. This allows the system to provide a more satisfying golf experience by suggesting the optimal golf course according to the user's emotions.

[0121] The golf matching system can further utilize emotion estimation to suggest golf playing styles based on the user's emotions. For example, if the user is relaxed, a casual playing style can be suggested. If the user is excited, a competitive playing style can be suggested. Furthermore, if the user is tired, a playing style that can be enjoyed in a short time can be suggested. In this way, by suggesting the optimal playing style according to the user's emotions, a more satisfying golf experience can be provided.

[0122] The golf matching system can further customize golf feedback based on the user's emotions using emotion estimation capabilities. For example, if the user is relaxed, it can provide detailed feedback. If the user is excited, it can provide visually appealing feedback. Furthermore, if the user is tired, it can provide concise feedback. This allows for more accurate identification of areas for improvement in golf by providing optimal feedback according to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives the user's golf schedule. The user's golf schedule includes the date and time, location, and number of participants. The reception desk can also save the golf schedule entered by the user to a database and send it to the analysis department in real time. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit analyzes information such as the user's schedule, golf level, and preferred golf course, and generates data to suggest the most suitable golf partner. Step 3: The search unit uses generational AI to search for golf partners based on the information analyzed by the analysis unit. The search unit searches for other users who want to play golf on the same weekend, or other users who plan to go to the golf course of the user's choice. Step 4: The service provider provides the user with golf partners found by the search unit. The service provider can also notify the user of the search results on the user's device and save them to the user's account.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, adjustment unit, and selection unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives the user's golf schedule via the control unit 46A of the smart device 14. The analysis unit analyzes the information using generated AI via the specific processing unit 290 of the data processing unit 12. The search unit searches for a golf partner via the specific processing unit 290 of the data processing unit 12. The provision unit notifies the user of the search results via the control unit 46A of the smart device 14. The adjustment unit searches for a new member via the specific processing unit 290 of the data processing unit 12. The selection unit selects the golf course desired by the user via the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, adjustment unit, and selection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's golf schedule via the control unit 46A of the smart glasses 214. The analysis unit analyzes the information using generated AI via the identification processing unit 290 of the data processing unit 12. The search unit searches for a golf partner via the identification processing unit 290 of the data processing unit 12. The provision unit notifies the user of the search results via the control unit 46A of the smart glasses 214. The adjustment unit searches for a new member via the identification processing unit 290 of the data processing unit 12. The selection unit selects the golf course desired by the user via the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, adjustment unit, and selection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the user's golf schedule via the control unit 46A of the headset terminal 314. The analysis unit analyzes the information using generated AI via the specific processing unit 290 of the data processing unit 12. The search unit searches for a golf partner via the specific processing unit 290 of the data processing unit 12. The provision unit notifies the user of the search results via the control unit 46A of the headset terminal 314. The adjustment unit searches for a new member via the specific processing unit 290 of the data processing unit 12. The selection unit selects the golf course desired by the user via the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, adjustment unit, and selection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives the user's golf schedule via the control unit 46A of the robot 414. The analysis unit analyzes the information using generated AI via the specific processing unit 290 of the data processing unit 12. The search unit searches for a golf partner via the specific processing unit 290 of the data processing unit 12. The provision unit notifies the user of the search results via the control unit 46A of the robot 414. The adjustment unit searches for a new member via the specific processing unit 290 of the data processing unit 12. The selection unit selects the golf course desired by the user via the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reception desk that accepts users' golf schedules, An analysis unit that analyzes the information received by the reception unit, A search unit searches for a golf partner based on the information analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the user with golf partners found by the search unit. A system characterized by the following features. (Note 2) It includes a coordination unit that searches for new members in the event of a sudden decrease in the number of members. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a selection section where users can choose their preferred golf course. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes information such as the user's schedule, golf skill level, and preferred golf course. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, Search for the optimal golf partner based on the analysis results The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide search results to users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of golf appointment bookings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past golf reservation history and selects the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When registering for a golf appointment, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes golf appointments based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting golf reservations, the system prioritizes accepting reservations that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a golf appointment is submitted, the system analyzes the user's social media activity and accepts relevant appointments. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of golf. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the golf category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the golf data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance to golf. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, consider the relationships between golf partners to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When searching, the search is performed while taking into account the attributes of the golf partner. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When searching, the search will take into account the geographical distribution of golf partners. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, refer to related literature on golf partners to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system analyzes the user's past golf partner history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The adjustment unit is, During the adjustment process, the system analyzes the user's past golf opponent history to select the optimal adjustment method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned selection unit is It estimates the user's emotions and adjusts the selection process based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned selection unit is When selecting a golf course, the system analyzes the user's past golf course selection history to determine the optimal selection method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned selection unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned selection unit is When making a selection, the system will consider the user's geographical location to determine the most optimal selection method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned selection unit is When making a selection, the system analyzes the user's social media activity to suggest ways to make that selection. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 desk that accepts users' golf schedules, An analysis unit that analyzes the information received by the reception unit, A search unit searches for a golf partner based on the information analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the user with golf partners found by the search unit. A system characterized by the following features.

2. It includes a coordination unit that searches for new members in the event of a sudden decrease in the number of members. The system according to feature 1.

3. It features a selection section where users can choose their preferred golf course. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes information such as the user's schedule, golf skill level, and preferred golf course. The system according to feature 1.

5. The aforementioned search unit, Search for the optimal golf partner based on the analysis results The system according to feature 1.

6. The aforementioned supply unit is, Provide search results to users. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of golf appointment bookings based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past golf reservation history and selects the most suitable reservation method. The system according to feature 1.

9. The aforementioned reception unit is When registering for a golf appointment, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes golf appointments based on those emotions. The system according to feature 1.

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