system

Abstract

The system according to this embodiment aims to allow users to easily select baseball equipment that suits them. [Solution] The system according to the embodiment comprises a reception unit, a measurement unit, an analysis unit, a determination unit, and a provision unit. The reception unit receives user information. The measurement unit measures the hand size based on the information entered by the reception unit. The analysis unit analyzes the bat swing speed based on the information entered by the reception unit. The determination unit determines the length and weight of the bat, and the type, shape, and size of the glove based on the information obtained by the measurement unit and the analysis unit. The provision unit provides the information determined by the determination 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 persona chatbot control method performed by at least one processor, the method including: 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, when a user selects baseball supplies suitable for himself / herself, there is a problem that it is difficult to select based on appropriate information.

[0005] The system according to the embodiment aims to enable a user to easily select baseball supplies suitable for himself / herself.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a measurement unit, an analysis unit, a determination unit, and a provision unit. The reception unit receives user information. The measurement unit measures the hand size based on the information entered by the reception unit. The analysis unit analyzes the bat swing speed based on the information entered by the reception unit. The determination unit determines the length and weight of the bat, and the type, shape, and size of the glove based on the information obtained by the measurement unit and the analysis unit. The provision unit provides the information determined by the determination unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily select baseball equipment that suits them. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 baseball equipment selection support system according to an embodiment of the present invention is a system that provides information to help a user choose baseball equipment that is suitable for them. The baseball equipment selection support system takes information such as the user's height, weight, hand size (registered with a mobile phone camera), fielding position, and bat swing speed (image analysis AI) as input by the user. Next, the baseball equipment selection support system determines the length and weight of the bat, the type, shape, and size of the glove, etc., based on this information. This allows the user to choose the baseball equipment that is best suited to them. For example, the user inputs their height, weight, and hand size. The hand size is taken with a mobile phone camera and registered with the baseball equipment selection support system. For example, the user holds their palm up to the camera, and the baseball equipment selection support system analyzes the image to measure the hand size. Next, the user inputs their fielding position and bat swing speed. The bat swing speed is determined by taking a video of the user swinging the bat with a camera, and the image analysis AI analyzes the speed. For example, the user takes a video of themselves swinging the bat, and the baseball equipment selection support system analyzes the video to measure the swing speed. The baseball equipment selection support system uses this information to determine the optimal bat length and weight, glove type, shape, and size for the user. For example, it suggests the appropriate bat length and weight based on the user's height, weight, and swing speed. It also suggests the appropriate glove type, shape, and size based on hand size and fielding position. This system allows users to choose the baseball equipment that is best suited to them. It is particularly useful for parents who are not familiar with baseball when choosing suitable equipment for their children. For example, in youth baseball, parents can use the baseball equipment selection support system to choose the best equipment without consulting a store clerk. It is also convenient for busy people as it allows them to choose the best equipment quickly. Furthermore, the baseball equipment selection support system contributes to the growth of the baseball equipment market. For example, the global baseball equipment market was approximately US$3.6 billion in 2022 and is expected to reach US$5.1 billion by 2031. By using the baseball equipment selection support system, it is possible to expand the purchase share of baseball equipment and promote market growth. In this way, the baseball equipment selection support system can select and provide the optimal baseball equipment based on the user's information.

[0029] The baseball equipment selection support system according to this embodiment comprises a reception unit, a measurement unit, an analysis unit, a decision unit, and a provision unit. The reception unit inputs user information. User information includes, but is not limited to, height, weight, hand size, fielding position, and bat swing speed. For example, the reception unit can acquire an image for measuring hand size by having the user hold their palm up to a camera. The reception unit can also acquire data for measuring bat swing speed by filming a video of the user swinging a bat. The measurement unit measures hand size based on the information input by the reception unit. For example, the measurement unit measures hand size by analyzing an image of the hand taken with a mobile phone camera. For example, the measurement unit can measure the length and width of the hand using image analysis technology. The measurement unit can also accurately measure hand size by analyzing the shape and characteristics of the hand. The analysis unit analyzes bat swing speed based on the information input by the reception unit. For example, the analysis unit measures bat swing speed by analyzing a video of the user swinging a bat. For example, the analysis unit can measure the speed and angle of a swing using video analysis technology. The analysis unit can also analyze the timing and force applied during the swing to accurately measure the swing speed. The determination unit determines the length and weight of the bat, and the type, shape, and size of the glove, based on the information obtained by the measurement and analysis units. For example, the determination unit can determine the appropriate bat length and weight based on the user's height, weight, and swing speed. For example, it can suggest a standard bat length and weight based on the user's physical information. Furthermore, the determination unit determines the appropriate glove type, shape, and size based on hand size and fielding position. For example, it can suggest glove types such as infielders' gloves or outfielders' gloves based on hand size. The provision unit provides the information determined by the determination unit to the user. For example, the provision unit can display the determined bat length and weight, and glove type, shape, and size to the user. For example, the provision unit can provide information through web applications or mobile applications. It can also provide information through printed materials or email.As a result, the baseball equipment selection support system according to this embodiment can select and provide the most suitable baseball equipment based on user information.

[0030] The reception desk inputs user information. This information may include, but is not limited to, height, weight, hand size, fielding position, and bat swing speed. For example, the reception desk can capture an image of the user's palm in front of a camera to measure hand size. It can also capture a video of the user swinging a bat to obtain data for measuring bat swing speed. Furthermore, the reception desk provides a user-friendly interface to efficiently collect user information. For example, it allows users to easily input information through an application on a smartphone or tablet. The application includes features to guide the user to the correct position and angle when holding their palm in front of the camera, assisting in accurate measurement of hand size. Similarly, when measuring bat swing speed, it provides instructions to accurately capture the user's bat-swinging motion, ensuring smooth video recording. This allows the reception desk to collect user information accurately and efficiently, improving the overall accuracy of the system. Additionally, the reception desk securely manages the collected information and takes appropriate measures to protect privacy. For example, collected data is encrypted, and security measures are implemented to prevent unauthorized access from external sources. This allows users to provide information with peace of mind.

[0031] The measurement unit measures hand size based on information entered by the reception unit. For example, the measurement unit measures hand size by analyzing images of hands taken with a mobile phone camera. For example, the measurement unit can measure the length and width of a hand using image analysis technology. The measurement unit can also accurately measure hand size by analyzing the shape and characteristics of the hand. Specifically, as an image analysis technology, it employs a hand shape recognition algorithm using deep learning. This algorithm learns from a large amount of hand image data and can recognize the shape and characteristics of a hand with high accuracy. For example, it can analyze not only the length and width of the hand, but also detailed information such as the length of the fingers and the position of the joints. As a result, the measurement unit can accurately measure the user's hand size and use this information to help select appropriate baseball equipment. Furthermore, the measurement unit improves measurement accuracy by using multiple images to measure hand size. For example, by analyzing hand images taken from different angles and reconstructing the three-dimensional shape of the hand, it can perform more accurate size measurements. In addition, when measuring the user's hand size, the measurement unit applies image processing technology to minimize the influence of ambient light and background. As a result, the measurement unit can achieve highly accurate hand size measurement even in various environments.

[0032] The analysis unit analyzes bat swing speed based on information entered by the reception unit. For example, the analysis unit measures bat swing speed by analyzing a video of the user swinging the bat. For example, the analysis unit can measure the speed and angle of the swing using video analysis technology. The analysis unit can also accurately measure the swing speed by analyzing the timing and force applied to the swing. Specifically, as video analysis technology, it employs a motion analysis algorithm using machine learning. This algorithm learns from a large amount of swing video data and can analyze the speed and angle of the swing with high accuracy. For example, it measures the time from the start to the end of the swing and calculates the swing speed based on the distance the bat travels during that time. In addition, by analyzing the trajectory and angle of the swing, it can evaluate the efficiency of the swing and the amount of force applied. As a result, the analysis unit can accurately measure the user's swing speed and use this information to help select appropriate baseball equipment. Furthermore, based on the analysis results of the swing video, the analysis unit can also suggest areas for improvement in the user's swing form. For example, if the swing trajectory is unstable or the amount of force applied is inappropriate, it will provide specific improvement methods to support the user in improving their swing form. This allows the analysis unit to not only measure the user's swing speed but also contribute to improving their swing form, thereby supporting overall performance improvement.

[0033] The decision unit determines the length and weight of the bat, and the type, shape, and size of the glove, based on the information obtained by the measurement and analysis units. For example, the decision unit determines the appropriate bat length and weight based on the user's height, weight, and swing speed. For example, the decision unit can suggest a standard bat length and weight based on the user's physical information. The decision unit also determines the appropriate glove type, shape, and size based on the user's hand size and defensive position. For example, the decision unit can suggest glove types such as infielders' gloves or outfielders' gloves based on the user's hand size. Furthermore, the decision unit can also make customized suggestions according to the user's playing style and preferences. For example, it can select a bat that matches the user's playing style, such as a heavier bat for power hitters or a lighter bat for speed-oriented players. Regarding gloves, it can also suggest shapes and materials specialized for specific defensive positions to help maximize the user's performance. In this way, the decision unit can select the optimal baseball equipment based on the user's physical information and playing style, meeting the user's needs. Furthermore, the decision-making unit can utilize past data and statistical information to provide more accurate recommendations. For example, it can refer to data from other users with similar physical characteristics and playing styles, and make recommendations based on successful case studies. This allows the decision-making unit to provide users with highly reliable recommendations, resulting in a high level of satisfaction in selecting baseball equipment.

[0034] The supply unit provides users with information determined by the decision unit. For example, the supply unit can display to users the determined length and weight of bats, and the type, shape, and size of gloves. For example, the supply unit can provide information through web applications and mobile applications. It can also provide information through printed materials and email. Furthermore, the supply unit can provide trial services and demonstration opportunities so that users can actually try out the selected baseball equipment. For example, it can hold events at partner sports shops and training facilities where users can actually use the selected bats and gloves. The supply unit can also collect user feedback and understand the evaluation and areas for improvement of the selected baseball equipment. This allows the supply unit to make continuous improvements to increase user satisfaction. In addition, the supply unit also has functions to support the purchase process of the selected baseball equipment. For example, it can make it easy for users to purchase the selected baseball equipment through web applications and mobile applications. It can also quickly obtain the selected baseball equipment by collaborating with partner sports shops and online stores. This allows the service provider to offer users consistent support, from providing information on selected baseball equipment to completing the purchase process, thereby improving user convenience.

[0035] The measurement unit can measure the size of a hand by analyzing an image of a hand taken with a mobile phone camera. For example, the measurement unit can measure the length and width of a hand using image analysis technology. The measurement unit can also accurately measure the size of a hand by analyzing its shape and characteristics. This allows for accurate measurement of hand size using a mobile phone camera. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input an image of a hand taken with a mobile phone camera into a generating AI and have the generating AI perform the measurement of the hand size.

[0036] The analysis unit can analyze a video of a user swinging a bat and measure the bat swing speed. For example, the analysis unit can analyze a video of a user swinging a bat and measure the bat swing speed. For example, the analysis unit can measure the speed and angle of the swing using video analysis technology. The analysis unit can also analyze the timing and force of the swing and accurately measure the swing speed. This allows for accurate measurement of the bat swing speed through video analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a video of a user swinging a bat into a generating AI and have the generating AI perform the measurement of the bat swing speed.

[0037] The decision unit can determine the appropriate bat length and weight based on the user's height, weight, and swing speed. For example, the decision unit can suggest a standard bat length and weight based on the user's physical information. This allows the user to select the optimal bat based on their physical information. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data on the user's height, weight, and swing speed into a generating AI and have the generating AI determine the bat length and weight.

[0038] The decision unit can determine the appropriate type, shape, and size of glove based on hand size and fielding position. For example, the decision unit can suggest glove types such as infielders' gloves or outfielders' gloves based on hand size. This allows the user to select the optimal glove based on their hand size and fielding position. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input hand size and fielding position data into a generating AI and have the generating AI perform the determination of the glove type, shape, and size.

[0039] The service provider can provide users with the determined bat length and weight, and the type, shape, and size of their gloves. The service provider can, for example, display the determined bat length and weight, and the type, shape, and size of their gloves to the user. For example, the service provider can provide information through web applications or mobile applications. The service provider can also provide information through printed materials or email. This allows the service provider to provide users with information on the most suitable baseball equipment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the determined bat length and weight, and the type, shape, and size of their gloves into a generating AI and have the generating AI execute the method of providing the information.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. This improves user convenience by providing the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception desk can customize input fields based on the user's current activities and areas of interest when they enter information. For example, if the user is interested in sports, the reception desk will prioritize displaying sports-related input fields. It can also simplify and display work-related input fields if the user is at work. Furthermore, if the user is traveling, it can prioritize displaying travel-related input fields. This improves input efficiency by providing input fields tailored to the user's activities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the user's current activities and areas of interest into a generating AI and have the generating AI customize the input fields.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception desk can prioritize inputting information related to that region. Furthermore, if the user is traveling, the reception desk can prioritize inputting information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize inputting information related to their home. This allows for the priority input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant information.

[0043] The reception unit can analyze the user's social media activity and input relevant information when information is entered. For example, the reception unit can suggest relevant input fields based on information the user has shared on social media. The reception unit can also prompt the user to input relevant information based on the accounts the user follows on social media. Furthermore, the reception unit can analyze the user's social media activity history and prompt the user to input relevant information. This allows the reception unit to prompt the user to input relevant information based on their social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0044] The measurement unit can select the optimal measurement method when measuring hand size by referring to the user's past measurement data. For example, the measurement unit can suggest the optimal measurement method based on the user's past measurement data. The measurement unit can also select a highly accurate measurement method from the user's past measurement data. Furthermore, the measurement unit can analyze the user's past measurement data and suggest the most efficient measurement method. This improves the accuracy of the measurement by providing the optimal measurement method based on past measurement data. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past measurement data into a generating AI and have the generating AI select the optimal measurement method.

[0045] The measurement unit can customize the measurement algorithm based on the shape and characteristics of the user's hand when measuring hand size. For example, the measurement unit can select the optimal measurement algorithm based on the shape of the user's hand. The measurement unit can also customize the measurement algorithm considering the characteristics of the user's hand. Furthermore, the measurement unit can optimize the measurement algorithm based on the size and shape of the user's hand. This improves the accuracy of the measurement by customizing the measurement algorithm based on the shape and characteristics of the user's hand. Some or all of the above processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the shape and characteristics of the user's hand into a generating AI and have the generating AI perform the customization of the measurement algorithm.

[0046] The measurement unit can select the optimal measurement method when measuring hand size, taking into account the user's geographical location information. For example, if the user is in a specific region, the measurement unit can suggest a measurement method suitable for that region. Furthermore, if the user is traveling, the measurement unit can suggest a measurement method suitable for their travel destination. Additionally, if the user is at home, the measurement unit can suggest a measurement method suitable for their home. This allows the measurement unit to provide the optimal measurement method based on the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal measurement method.

[0047] The measurement unit can analyze the user's social media activity and acquire relevant measurement data when measuring hand size. For example, the measurement unit can acquire relevant measurement data based on information shared by the user on social media. It can also acquire relevant measurement data based on accounts followed by the user on social media. Furthermore, the measurement unit can analyze the user's social media activity history and acquire relevant measurement data. This allows for the acquisition of relevant measurement data based on the user's social media activity. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant measurement data.

[0048] The analysis unit can select the optimal analysis method by referring to the user's past swing data when analyzing bat swing speed. For example, the analysis unit can propose the optimal analysis method based on data previously analyzed by the user. The analysis unit can also select a highly accurate analysis method from the user's past swing data. Furthermore, the analysis unit can analyze the user's past swing data and propose the most efficient analysis method. This improves the accuracy of the analysis by providing the optimal analysis method based on past swing data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past swing data into a generating AI and have the generating AI select the optimal analysis method.

[0049] The analysis unit can customize the analysis algorithm based on the user's swing style and characteristics when analyzing bat swing speed. For example, the analysis unit can select the optimal analysis algorithm based on the user's swing style. The analysis unit can also customize the analysis algorithm considering the user's swing characteristics. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's swing speed and style. This improves the accuracy of the analysis by customizing the analysis algorithm based on the user's swing style and characteristics. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's swing style and characteristics into a generating AI and have the generating AI perform the customization of the analysis algorithm.

[0050] The analysis unit can select the optimal analysis method when analyzing bat swing speed, taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit can suggest an analysis method suitable for that region. Furthermore, if the user is traveling, the analysis unit can suggest an analysis method suitable for their travel destination. Additionally, if the user is at home, the analysis unit can suggest an analysis method suitable for their home. This allows the system to provide the optimal analysis method based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0051] The analysis unit can analyze the user's social media activity and obtain relevant analytical data when analyzing bat swing speed. For example, the analysis unit can obtain relevant analytical data based on information shared by the user on social media. The analysis unit can also obtain relevant analytical data based on accounts followed by the user on social media. Furthermore, the analysis unit can analyze the user's social media activity history and obtain relevant analytical data. This allows the analysis unit to obtain relevant analytical data based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant analytical data.

[0052] The decision unit can select the optimal selection method when selecting bats or gloves by referring to the user's past selection data. For example, the decision unit can propose the optimal selection method based on data selected by the user in the past. The decision unit can also select a highly accurate selection method from the user's past selection data. Furthermore, the decision unit can analyze the user's past selection data and propose the most efficient selection method. This improves the accuracy of selection by providing the optimal selection method based on past selection data. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's past selection data into a generating AI and have the generating AI perform the selection of the optimal selection method.

[0053] The decision unit can customize the selection algorithm based on the user's body type and characteristics when selecting bats and gloves. For example, the decision unit can select the optimal selection algorithm based on the user's body type. The decision unit can also customize the selection algorithm considering the user's characteristics. Furthermore, the decision unit can optimize the selection algorithm based on the user's body type and characteristics. This improves the accuracy of the selection by customizing the selection algorithm based on the user's body type and characteristics. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data on the user's body type and characteristics into a generating AI and have the generating AI perform the customization of the selection algorithm.

[0054] The decision unit can select the optimal selection method for bats and gloves by considering the user's geographical location information. For example, if the user is in a specific region, the decision unit can suggest a selection method suitable for that region. Furthermore, if the user is traveling, the decision unit can suggest a selection method suitable for their travel destination. Additionally, if the user is at home, the decision unit can suggest a selection method suitable for their home. This allows the system to provide the optimal selection method based on the user's geographical location information. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal selection method.

[0055] The decision-making unit can analyze the user's social media activity and obtain relevant selection data when selecting bats and gloves. For example, the decision-making unit can obtain relevant selection data based on information shared by the user on social media. It can also obtain relevant selection data based on accounts followed by the user on social media. Furthermore, the decision-making unit can analyze the user's social media activity history and obtain relevant selection data. This allows the system to obtain relevant selection data based on the user's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant selection data.

[0056] The information delivery unit can select the optimal delivery method by referring to the user's past delivery data when providing information. For example, the information delivery unit can propose the optimal delivery method based on the data the user has previously provided. The information delivery unit can also select a highly accurate delivery method from the user's past delivery data. Furthermore, the information delivery unit can analyze the user's past delivery data and propose the most efficient delivery method. By providing the optimal delivery method based on past delivery data, the ease of receiving information can be improved. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past delivery data into a generating AI and have the generating AI select the optimal delivery method.

[0057] The information delivery unit can customize the delivery algorithm based on the user's interests and characteristics when providing information. For example, the delivery unit can select the optimal delivery algorithm based on the user's interests. The delivery unit can also customize the delivery algorithm considering the user's characteristics. Furthermore, the delivery unit can optimize the delivery algorithm based on the user's interests and characteristics. By customizing the delivery algorithm based on the user's interests and characteristics, the ease of receiving information can be improved. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data on the user's interests and characteristics into a generating AI and have the generating AI perform the customization of the delivery algorithm.

[0058] The information delivery unit can select the optimal delivery method by considering the user's geographical location when providing information. For example, if the user is in a specific region, the service delivery unit can suggest a delivery method suitable for that region. Furthermore, if the user is traveling, the service delivery unit can suggest a delivery method suitable for their travel destination. Additionally, if the user is at home, the service delivery unit can suggest a delivery method suitable for their home. This allows the service delivery unit to provide the optimal delivery method based on the user's geographical location. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0059] The information provider can analyze the user's social media activity and obtain relevant information data when providing information. For example, the information provider can obtain relevant information data based on information shared by the user on social media. The information provider can also obtain relevant information data based on accounts followed by the user on social media. Furthermore, the information provider can analyze the user's social media activity history and obtain relevant information data. This allows the information provider to obtain relevant information data based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information data.

[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 baseball equipment selection support system can analyze a user's past selection history and provide optimal suggestions. For example, it can make optimal suggestions based on data from bats and gloves the user has selected in the past. It can also provide highly accurate suggestions based on the user's past selection history. Furthermore, it can analyze the user's past selection history to provide the most efficient suggestions. This improves user satisfaction by providing optimal suggestions based on past selection history. The suggestions can be adjusted, for example, by inputting the user's past selection data into a generating AI and having the AI ​​select the most optimal suggestions.

[0062] The baseball equipment selection support system can customize its suggestion algorithm based on the user's body type and characteristics. For example, it can select the optimal suggestion algorithm based on the user's body type. It can also customize the suggestion algorithm considering the user's characteristics. Furthermore, it can optimize the suggestion algorithm based on the user's body type and characteristics. This allows for improved accuracy of suggestions by customizing the suggestion algorithm based on the user's body type and characteristics. Adjusting the suggestion content can be done, for example, by inputting data on the user's body type and characteristics into the generating AI and having the generating AI perform the customization of the suggestion algorithm.

[0063] The baseball equipment selection support system can provide optimal suggestions by considering the user's geographical location. For example, if the user is in a specific region, it will provide suggestions suitable for that region. If the user is traveling, it can provide suggestions suitable for their travel destination. Furthermore, if the user is at home, it can provide suggestions suitable for their home location. This allows for improved user satisfaction by providing optimal suggestions based on the user's geographical location. The suggestions can be adjusted, for example, by inputting the user's geographical location into the generating AI, which then selects the most suitable suggestions.

[0064] The baseball equipment selection support system can analyze a user's social media activity and provide relevant suggestions. For example, it can make relevant suggestions based on information the user has shared on social media. It can also make relevant suggestions based on the accounts the user follows on social media. Furthermore, it can analyze the user's social media activity history and provide relevant suggestions. This allows for improved user satisfaction by providing relevant suggestions based on the user's social media activity. The suggestions can be adjusted, for example, by inputting the user's social media activity data into a generating AI and having the AI ​​select relevant suggestions.

[0065] The baseball equipment selection support system can select the optimal information delivery method by referring to the user's past data. For example, it can suggest the optimal information delivery method based on the data the user has previously provided. It can also select a highly accurate information delivery method from the user's past data. Furthermore, it can analyze the user's past data and suggest the most efficient information delivery method. This improves the ease with which information can be received by providing the optimal information delivery method based on past data. Information delivery can be adjusted, for example, by inputting the user's past data into a generating AI and having the generating AI select the optimal information delivery method.

[0066] The baseball equipment selection support system can customize its information provision algorithm based on the user's interests and characteristics. For example, it can select the optimal information provision algorithm based on the user's interests. It can also customize the information provision algorithm considering the user's characteristics. Furthermore, it can optimize the information provision algorithm based on the user's interests and characteristics. By customizing the information provision algorithm based on the user's interests and characteristics, the ease with which information can be received can be improved. Information provision adjustments can be made, for example, by inputting data on the user's interests and characteristics into a generating AI and having the generating AI perform the customization of the information provision algorithm.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk enters the user's information. This information includes, for example, height, weight, hand size, fielding position, and bat swing speed. The reception desk can take an image of the user's palm held up to the camera to measure hand size. It can also record a video of the user swinging the bat to obtain data for measuring bat swing speed. Step 2: The measurement unit measures the hand size based on the information entered by the reception unit. For example, the measurement unit measures the hand size by analyzing an image of the hand taken with a mobile phone camera. By using image analysis technology to measure the length and width of the hand and analyzing the shape and characteristics of the hand, the hand size can be accurately measured. Step 3: The analysis unit analyzes the bat swing speed based on the information entered by the reception unit. For example, the analysis unit measures the bat swing speed by analyzing a video of the user swinging the bat. By using video analysis technology to measure the speed and angle of the swing, and analyzing the timing and force applied to the swing, the swing speed can be accurately measured. Step 4: The determination unit determines the length and weight of the bat, and the type, shape, and size of the glove, based on the information obtained by the measurement and analysis units. For example, the determination unit determines the appropriate bat length and weight based on the user's height, weight, and swing speed. It can also determine the appropriate glove type, shape, and size based on hand size and fielding position. Step 5: The providing unit provides the user with the information determined by the decision-making unit. For example, the providing unit displays to the user the determined length and weight of the bat, or the type, shape, and size of the glove. The information can be provided through web applications or mobile applications. It can also be provided through printed materials or email.

[0069] (Example of form 2) The baseball equipment selection support system according to an embodiment of the present invention is a system that provides information to help a user choose baseball equipment that is suitable for them. The baseball equipment selection support system takes information such as the user's height, weight, hand size (registered with a mobile phone camera), fielding position, and bat swing speed (image analysis AI) as input by the user. Next, the baseball equipment selection support system determines the length and weight of the bat, the type, shape, and size of the glove, etc., based on this information. This allows the user to choose the baseball equipment that is best suited to them. For example, the user inputs their height, weight, and hand size. The hand size is taken with a mobile phone camera and registered with the baseball equipment selection support system. For example, the user holds their palm up to the camera, and the baseball equipment selection support system analyzes the image to measure the hand size. Next, the user inputs their fielding position and bat swing speed. The bat swing speed is determined by taking a video of the user swinging the bat with a camera, and the image analysis AI analyzes the speed. For example, the user takes a video of themselves swinging the bat, and the baseball equipment selection support system analyzes the video to measure the swing speed. The baseball equipment selection support system uses this information to determine the optimal bat length and weight, glove type, shape, and size for the user. For example, it suggests the appropriate bat length and weight based on the user's height, weight, and swing speed. It also suggests the appropriate glove type, shape, and size based on hand size and fielding position. This system allows users to choose the baseball equipment that is best suited to them. It is particularly useful for parents who are not familiar with baseball when choosing suitable equipment for their children. For example, in youth baseball, parents can use the baseball equipment selection support system to choose the best equipment without consulting a store clerk. It is also convenient for busy people as it allows them to choose the best equipment quickly. Furthermore, the baseball equipment selection support system contributes to the growth of the baseball equipment market. For example, the global baseball equipment market was approximately US$3.6 billion in 2022 and is expected to reach US$5.1 billion by 2031. By using the baseball equipment selection support system, it is possible to expand the purchase share of baseball equipment and promote market growth. In this way, the baseball equipment selection support system can select and provide the optimal baseball equipment based on the user's information.

[0070] The baseball equipment selection support system according to this embodiment comprises a reception unit, a measurement unit, an analysis unit, a decision unit, and a provision unit. The reception unit inputs user information. User information includes, but is not limited to, height, weight, hand size, fielding position, and bat swing speed. For example, the reception unit can acquire an image for measuring hand size by having the user hold their palm up to a camera. The reception unit can also acquire data for measuring bat swing speed by filming a video of the user swinging a bat. The measurement unit measures hand size based on the information input by the reception unit. For example, the measurement unit measures hand size by analyzing an image of the hand taken with a mobile phone camera. For example, the measurement unit can measure the length and width of the hand using image analysis technology. The measurement unit can also accurately measure hand size by analyzing the shape and characteristics of the hand. The analysis unit analyzes bat swing speed based on the information input by the reception unit. For example, the analysis unit measures bat swing speed by analyzing a video of the user swinging a bat. For example, the analysis unit can measure the speed and angle of a swing using video analysis technology. The analysis unit can also analyze the timing and force applied during the swing to accurately measure the swing speed. The determination unit determines the length and weight of the bat, and the type, shape, and size of the glove, based on the information obtained by the measurement and analysis units. For example, the determination unit can determine the appropriate bat length and weight based on the user's height, weight, and swing speed. For example, it can suggest a standard bat length and weight based on the user's physical information. Furthermore, the determination unit determines the appropriate glove type, shape, and size based on hand size and fielding position. For example, it can suggest glove types such as infielders' gloves or outfielders' gloves based on hand size. The provision unit provides the information determined by the determination unit to the user. For example, the provision unit can display the determined bat length and weight, and glove type, shape, and size to the user. For example, the provision unit can provide information through web applications or mobile applications. It can also provide information through printed materials or email.As a result, the baseball equipment selection support system according to this embodiment can select and provide the most suitable baseball equipment based on user information.

[0071] The reception desk inputs user information. This information may include, but is not limited to, height, weight, hand size, fielding position, and bat swing speed. For example, the reception desk can capture an image of the user's palm in front of a camera to measure hand size. It can also capture a video of the user swinging a bat to obtain data for measuring bat swing speed. Furthermore, the reception desk provides a user-friendly interface to efficiently collect user information. For example, it allows users to easily input information through an application on a smartphone or tablet. The application includes features to guide the user to the correct position and angle when holding their palm in front of the camera, assisting in accurate measurement of hand size. Similarly, when measuring bat swing speed, it provides instructions to accurately capture the user's bat-swinging motion, ensuring smooth video recording. This allows the reception desk to collect user information accurately and efficiently, improving the overall accuracy of the system. Additionally, the reception desk securely manages the collected information and takes appropriate measures to protect privacy. For example, collected data is encrypted, and security measures are implemented to prevent unauthorized access from external sources. This allows users to provide information with peace of mind.

[0072] The measurement unit measures hand size based on information entered by the reception unit. For example, the measurement unit measures hand size by analyzing images of hands taken with a mobile phone camera. For example, the measurement unit can measure the length and width of a hand using image analysis technology. The measurement unit can also accurately measure hand size by analyzing the shape and characteristics of the hand. Specifically, as an image analysis technology, it employs a hand shape recognition algorithm using deep learning. This algorithm learns from a large amount of hand image data and can recognize the shape and characteristics of a hand with high accuracy. For example, it can analyze not only the length and width of the hand, but also detailed information such as the length of the fingers and the position of the joints. As a result, the measurement unit can accurately measure the user's hand size and use this information to help select appropriate baseball equipment. Furthermore, the measurement unit improves measurement accuracy by using multiple images to measure hand size. For example, by analyzing hand images taken from different angles and reconstructing the three-dimensional shape of the hand, it can perform more accurate size measurements. In addition, when measuring the user's hand size, the measurement unit applies image processing technology to minimize the influence of ambient light and background. As a result, the measurement unit can achieve highly accurate hand size measurement even in various environments.

[0073] The analysis unit analyzes bat swing speed based on information entered by the reception unit. For example, the analysis unit measures bat swing speed by analyzing a video of the user swinging the bat. For example, the analysis unit can measure the speed and angle of the swing using video analysis technology. The analysis unit can also accurately measure the swing speed by analyzing the timing and force applied to the swing. Specifically, as video analysis technology, it employs a motion analysis algorithm using machine learning. This algorithm learns from a large amount of swing video data and can analyze the speed and angle of the swing with high accuracy. For example, it measures the time from the start to the end of the swing and calculates the swing speed based on the distance the bat travels during that time. In addition, by analyzing the trajectory and angle of the swing, it can evaluate the efficiency of the swing and the amount of force applied. As a result, the analysis unit can accurately measure the user's swing speed and use this information to help select appropriate baseball equipment. Furthermore, based on the analysis results of the swing video, the analysis unit can also suggest areas for improvement in the user's swing form. For example, if the swing trajectory is unstable or the amount of force applied is inappropriate, it will provide specific improvement methods to support the user in improving their swing form. This allows the analysis unit to not only measure the user's swing speed but also contribute to improving their swing form, thereby supporting overall performance improvement.

[0074] The decision unit determines the length and weight of the bat, and the type, shape, and size of the glove, based on the information obtained by the measurement and analysis units. For example, the decision unit determines the appropriate bat length and weight based on the user's height, weight, and swing speed. For example, the decision unit can suggest a standard bat length and weight based on the user's physical information. The decision unit also determines the appropriate glove type, shape, and size based on the user's hand size and defensive position. For example, the decision unit can suggest glove types such as infielders' gloves or outfielders' gloves based on the user's hand size. Furthermore, the decision unit can also make customized suggestions according to the user's playing style and preferences. For example, it can select a bat that matches the user's playing style, such as a heavier bat for power hitters or a lighter bat for speed-oriented players. Regarding gloves, it can also suggest shapes and materials specialized for specific defensive positions to help maximize the user's performance. In this way, the decision unit can select the optimal baseball equipment based on the user's physical information and playing style, meeting the user's needs. Furthermore, the decision-making unit can utilize past data and statistical information to provide more accurate recommendations. For example, it can refer to data from other users with similar physical characteristics and playing styles, and make recommendations based on successful case studies. This allows the decision-making unit to provide users with highly reliable recommendations, resulting in a high level of satisfaction in selecting baseball equipment.

[0075] The supply unit provides users with information determined by the decision unit. For example, the supply unit can display to users the determined length and weight of bats, and the type, shape, and size of gloves. For example, the supply unit can provide information through web applications and mobile applications. It can also provide information through printed materials and email. Furthermore, the supply unit can provide trial services and demonstration opportunities so that users can actually try out the selected baseball equipment. For example, it can hold events at partner sports shops and training facilities where users can actually use the selected bats and gloves. The supply unit can also collect user feedback and understand the evaluation and areas for improvement of the selected baseball equipment. This allows the supply unit to make continuous improvements to increase user satisfaction. In addition, the supply unit also has functions to support the purchase process of the selected baseball equipment. For example, it can make it easy for users to purchase the selected baseball equipment through web applications and mobile applications. It can also quickly obtain the selected baseball equipment by collaborating with partner sports shops and online stores. This allows the service provider to offer users consistent support, from providing information on selected baseball equipment to completing the purchase process, thereby improving user convenience.

[0076] The measurement unit can measure the size of a hand by analyzing an image of a hand taken with a mobile phone camera. For example, the measurement unit can measure the length and width of a hand using image analysis technology. The measurement unit can also accurately measure the size of a hand by analyzing its shape and characteristics. This allows for accurate measurement of hand size using a mobile phone camera. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input an image of a hand taken with a mobile phone camera into a generating AI and have the generating AI perform the measurement of the hand size.

[0077] The analysis unit can analyze a video of a user swinging a bat and measure the bat swing speed. For example, the analysis unit can analyze a video of a user swinging a bat and measure the bat swing speed. For example, the analysis unit can measure the speed and angle of the swing using video analysis technology. The analysis unit can also analyze the timing and force of the swing and accurately measure the swing speed. This allows for accurate measurement of the bat swing speed through video analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a video of a user swinging a bat into a generating AI and have the generating AI perform the measurement of the bat swing speed.

[0078] The decision unit can determine the appropriate bat length and weight based on the user's height, weight, and swing speed. For example, the decision unit can suggest a standard bat length and weight based on the user's physical information. This allows the user to select the optimal bat based on their physical information. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data on the user's height, weight, and swing speed into a generating AI and have the generating AI determine the bat length and weight.

[0079] The decision unit can determine the appropriate type, shape, and size of glove based on hand size and fielding position. For example, the decision unit can suggest glove types such as infielders' gloves or outfielders' gloves based on hand size. This allows the user to select the optimal glove based on their hand size and fielding position. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input hand size and fielding position data into a generating AI and have the generating AI perform the determination of the glove type, shape, and size.

[0080] The service provider can provide users with the determined bat length and weight, and the type, shape, and size of their gloves. The service provider can, for example, display the determined bat length and weight, and the type, shape, and size of their gloves to the user. For example, the service provider can provide information through web applications or mobile applications. The service provider can also provide information through printed materials or email. This allows the service provider to provide users with information on the most suitable baseball equipment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the determined bat length and weight, and the type, shape, and size of their gloves into a generating AI and have the generating AI execute the method of providing the information.

[0081] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception desk can simplify the input and request minimal information. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This reduces the user's burden by adjusting the timing of information input 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 the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. This improves user convenience by providing the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0083] The reception desk can customize input fields based on the user's current activities and areas of interest when they enter information. For example, if the user is interested in sports, the reception desk will prioritize displaying sports-related input fields. It can also simplify and display work-related input fields if the user is at work. Furthermore, if the user is traveling, it can prioritize displaying travel-related input fields. This improves input efficiency by providing input fields tailored to the user's activities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the user's current activities and areas of interest into a generating AI and have the generating AI customize the input fields.

[0084] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of important information. If the user is relaxed, the reception desk may also prioritize the input of detailed information. Furthermore, if the user is in a hurry, the reception desk may also prioritize the input of only the most important information. In this way, by prioritizing the information to be entered according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception desk can prioritize inputting information related to that region. Furthermore, if the user is traveling, the reception desk can prioritize inputting information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize inputting information related to their home. This allows for the priority input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant information.

[0086] The reception unit can analyze the user's social media activity and input relevant information when information is entered. For example, the reception unit can suggest relevant input fields based on information the user has shared on social media. The reception unit can also prompt the user to input relevant information based on the accounts the user follows on social media. Furthermore, the reception unit can analyze the user's social media activity history and prompt the user to input relevant information. This allows the reception unit to prompt the user to input relevant information based on their social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0087] The measurement unit can estimate the user's emotions and adjust the hand size measurement method based on the estimated emotions. For example, if the user is nervous, the measurement unit can provide a simple and quick measurement method. If the user is relaxed, the measurement unit can also provide a detailed measurement method to improve accuracy. Furthermore, if the user is in a hurry, the measurement unit can prioritize measuring only the most important items. This improves the accuracy of the measurement by adjusting the hand size measurement method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 measurement unit may be performed using AI or not using AI. For example, the measurement unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The measurement unit can select the optimal measurement method when measuring hand size by referring to the user's past measurement data. For example, the measurement unit can suggest the optimal measurement method based on the user's past measurement data. The measurement unit can also select a highly accurate measurement method from the user's past measurement data. Furthermore, the measurement unit can analyze the user's past measurement data and suggest the most efficient measurement method. This improves the accuracy of the measurement by providing the optimal measurement method based on past measurement data. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past measurement data into a generating AI and have the generating AI select the optimal measurement method.

[0089] The measurement unit can customize the measurement algorithm based on the shape and characteristics of the user's hand when measuring hand size. For example, the measurement unit can select the optimal measurement algorithm based on the shape of the user's hand. The measurement unit can also customize the measurement algorithm considering the characteristics of the user's hand. Furthermore, the measurement unit can optimize the measurement algorithm based on the size and shape of the user's hand. This improves the accuracy of the measurement by customizing the measurement algorithm based on the shape and characteristics of the user's hand. Some or all of the above processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the shape and characteristics of the user's hand into a generating AI and have the generating AI perform the customization of the measurement algorithm.

[0090] The measurement unit can estimate the user's emotions and determine the priority of hand size measurements based on the estimated emotions. For example, if the user is stressed, the measurement unit will prioritize measuring important items. If the user is relaxed, the measurement unit can also prioritize measuring detailed items. Furthermore, if the user is in a hurry, the measurement unit can prioritize measuring only the most important items. This allows for the priority of measuring important items by determining the priority of hand size measurements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 measurement unit may be performed using AI or not using AI. For example, the measurement unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The measurement unit can select the optimal measurement method when measuring hand size, taking into account the user's geographical location information. For example, if the user is in a specific region, the measurement unit can suggest a measurement method suitable for that region. Furthermore, if the user is traveling, the measurement unit can suggest a measurement method suitable for their travel destination. Additionally, if the user is at home, the measurement unit can suggest a measurement method suitable for their home. This allows the measurement unit to provide the optimal measurement method based on the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal measurement method.

[0092] The measurement unit can analyze the user's social media activity and acquire relevant measurement data when measuring hand size. For example, the measurement unit can acquire relevant measurement data based on information shared by the user on social media. It can also acquire relevant measurement data based on accounts followed by the user on social media. Furthermore, the measurement unit can analyze the user's social media activity history and acquire relevant measurement data. This allows for the acquisition of relevant measurement data based on the user's social media activity. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant measurement data.

[0093] The analysis unit can estimate the user's emotions and adjust the bat swing speed analysis method based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and rapid analysis method. If the user is relaxed, the analysis unit can also provide a detailed analysis method to improve accuracy. Furthermore, if the user is in a hurry, the analysis unit can prioritize and analyze only the most important analysis items. This improves the accuracy of the analysis by adjusting the bat swing speed analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can select the optimal analysis method by referring to the user's past swing data when analyzing bat swing speed. For example, the analysis unit can propose the optimal analysis method based on data previously analyzed by the user. The analysis unit can also select a highly accurate analysis method from the user's past swing data. Furthermore, the analysis unit can analyze the user's past swing data and propose the most efficient analysis method. This improves the accuracy of the analysis by providing the optimal analysis method based on past swing data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past swing data into a generating AI and have the generating AI select the optimal analysis method.

[0095] The analysis unit can customize the analysis algorithm based on the user's swing style and characteristics when analyzing bat swing speed. For example, the analysis unit can select the optimal analysis algorithm based on the user's swing style. The analysis unit can also customize the analysis algorithm considering the user's swing characteristics. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's swing speed and style. This improves the accuracy of the analysis by customizing the analysis algorithm based on the user's swing style and characteristics. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's swing style and characteristics into a generating AI and have the generating AI perform the customization of the analysis algorithm.

[0096] The analysis unit can estimate the user's emotions and determine the priority of the bat swing speed analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing important analysis items. If the user is relaxed, the analysis unit can also prioritize analyzing detailed analysis items. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing only the most important analysis items. This allows for the prioritization of important analysis items by determining the priority of the bat swing speed analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0097] The analysis unit can select the optimal analysis method when analyzing bat swing speed, taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit can suggest an analysis method suitable for that region. Furthermore, if the user is traveling, the analysis unit can suggest an analysis method suitable for their travel destination. Additionally, if the user is at home, the analysis unit can suggest an analysis method suitable for their home. This allows the system to provide the optimal analysis method based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0098] The analysis unit can analyze the user's social media activity and obtain relevant analytical data when analyzing bat swing speed. For example, the analysis unit can obtain relevant analytical data based on information shared by the user on social media. The analysis unit can also obtain relevant analytical data based on accounts followed by the user on social media. Furthermore, the analysis unit can analyze the user's social media activity history and obtain relevant analytical data. This allows the analysis unit to obtain relevant analytical data based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant analytical data.

[0099] The decision unit can estimate the user's emotions and adjust the selection method for bats and gloves based on the estimated emotions. For example, if the user is nervous, the decision unit can provide a simple and quick selection method. If the user is relaxed, the decision unit can also provide a detailed selection method to improve accuracy. Furthermore, if the user is in a hurry, the decision unit can prioritize and select only the most important selection items. This improves the accuracy of the selection by adjusting the selection method for bats and gloves according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 decision unit may be performed using AI or not using AI. For example, the decision unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The decision unit can select the optimal selection method when selecting bats or gloves by referring to the user's past selection data. For example, the decision unit can propose the optimal selection method based on data selected by the user in the past. The decision unit can also select a highly accurate selection method from the user's past selection data. Furthermore, the decision unit can analyze the user's past selection data and propose the most efficient selection method. This improves the accuracy of selection by providing the optimal selection method based on past selection data. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's past selection data into a generating AI and have the generating AI perform the selection of the optimal selection method.

[0101] The decision unit can customize the selection algorithm based on the user's body type and characteristics when selecting bats and gloves. For example, the decision unit can select the optimal selection algorithm based on the user's body type. The decision unit can also customize the selection algorithm considering the user's characteristics. Furthermore, the decision unit can optimize the selection algorithm based on the user's body type and characteristics. This improves the accuracy of the selection by customizing the selection algorithm based on the user's body type and characteristics. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input data on the user's body type and characteristics into a generating AI and have the generating AI perform the customization of the selection algorithm.

[0102] The decision unit can estimate the user's emotions and determine the priority of bat and glove selection based on the estimated emotions. For example, if the user is stressed, the decision unit will prioritize important selection items. If the user is relaxed, the decision unit can also prioritize detailed selection items. Furthermore, if the user is in a hurry, the decision unit can prioritize only the most important selection items. In this way, by determining the priority of bat and glove selection according to the user's emotions, important selection items can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using 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 decision unit may be performed using AI, or not using AI. For example, the decision unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0103] The decision unit can select the optimal selection method for bats and gloves by considering the user's geographical location information. For example, if the user is in a specific region, the decision unit can suggest a selection method suitable for that region. Furthermore, if the user is traveling, the decision unit can suggest a selection method suitable for their travel destination. Additionally, if the user is at home, the decision unit can suggest a selection method suitable for their home. This allows the system to provide the optimal selection method based on the user's geographical location information. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal selection method.

[0104] The decision-making unit can analyze the user's social media activity and obtain relevant selection data when selecting bats and gloves. For example, the decision-making unit can obtain relevant selection data based on information shared by the user on social media. It can also obtain relevant selection data based on accounts followed by the user on social media. Furthermore, the decision-making unit can analyze the user's social media activity history and obtain relevant selection data. This allows the system to obtain relevant selection data based on the user's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant selection data.

[0105] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a method of delivery that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a concise method of delivery. By adjusting the method of information delivery according to the user's emotions, the ease of receiving information can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The information delivery unit can select the optimal delivery method by referring to the user's past delivery data when providing information. For example, the information delivery unit can propose the optimal delivery method based on the data the user has previously provided. The information delivery unit can also select a highly accurate delivery method from the user's past delivery data. Furthermore, the information delivery unit can analyze the user's past delivery data and propose the most efficient delivery method. By providing the optimal delivery method based on past delivery data, the ease of receiving information can be improved. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past delivery data into a generating AI and have the generating AI select the optimal delivery method.

[0107] The information delivery unit can customize the delivery algorithm based on the user's interests and characteristics when providing information. For example, the delivery unit can select the optimal delivery algorithm based on the user's interests. The delivery unit can also customize the delivery algorithm considering the user's characteristics. Furthermore, the delivery unit can optimize the delivery algorithm based on the user's interests and characteristics. By customizing the delivery algorithm based on the user's interests and characteristics, the ease of receiving information can be improved. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data on the user's interests and characteristics into a generating AI and have the generating AI perform the customization of the delivery algorithm.

[0108] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider can prioritize providing important information. If the user is relaxed, the information provider can also prioritize providing detailed information. Furthermore, if the user is in a hurry, the information provider can prioritize providing only the most important information. This allows for the prioritization of important information by determining the priority of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The information delivery unit can select the optimal delivery method by considering the user's geographical location when providing information. For example, if the user is in a specific region, the service delivery unit can suggest a delivery method suitable for that region. Furthermore, if the user is traveling, the service delivery unit can suggest a delivery method suitable for their travel destination. Additionally, if the user is at home, the service delivery unit can suggest a delivery method suitable for their home. This allows the service delivery unit to provide the optimal delivery method based on the user's geographical location. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0110] The information provider can analyze the user's social media activity and obtain relevant information data when providing information. For example, the information provider can obtain relevant information data based on information shared by the user on social media. The information provider can also obtain relevant information data based on accounts followed by the user on social media. Furthermore, the information provider can analyze the user's social media activity history and obtain relevant information data. This allows the information provider to obtain relevant information data based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information data.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The baseball equipment selection support system can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is stressed, the system can provide simple and quick suggestions; if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, the system can prioritize only the most important suggestions. This allows for improved user satisfaction by providing suggestions tailored to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Suggestion adjustment can be achieved, for example, by inputting user facial expression data into the generative AI and having the generative AI perform emotion estimation.

[0113] The baseball equipment selection support system can analyze a user's past selection history and provide optimal suggestions. For example, it can make optimal suggestions based on data from bats and gloves the user has selected in the past. It can also provide highly accurate suggestions based on the user's past selection history. Furthermore, it can analyze the user's past selection history to provide the most efficient suggestions. This improves user satisfaction by providing optimal suggestions based on past selection history. The suggestions can be adjusted, for example, by inputting the user's past selection data into a generating AI and having the AI ​​select the most optimal suggestions.

[0114] The baseball equipment selection support system can customize its suggestion algorithm based on the user's body type and characteristics. For example, it can select the optimal suggestion algorithm based on the user's body type. It can also customize the suggestion algorithm considering the user's characteristics. Furthermore, it can optimize the suggestion algorithm based on the user's body type and characteristics. This allows for improved accuracy of suggestions by customizing the suggestion algorithm based on the user's body type and characteristics. Adjusting the suggestion content can be done, for example, by inputting data on the user's body type and characteristics into the generating AI and having the generating AI perform the customization of the suggestion algorithm.

[0115] The baseball equipment selection support system can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, important suggestions will be prioritized. If the user is relaxed, detailed suggestions may be prioritized. Furthermore, if the user is in a hurry, only the most important suggestions may be prioritized. In this way, important suggestions can be prioritized by determining the priority of suggestions according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Suggestion content can be adjusted, for example, by inputting the user's facial expression data into the generative AI and having the generative AI perform emotion estimation.

[0116] The baseball equipment selection support system can provide optimal suggestions by considering the user's geographical location. For example, if the user is in a specific region, it will provide suggestions suitable for that region. If the user is traveling, it can provide suggestions suitable for their travel destination. Furthermore, if the user is at home, it can provide suggestions suitable for their home location. This allows for improved user satisfaction by providing optimal suggestions based on the user's geographical location. The suggestions can be adjusted, for example, by inputting the user's geographical location into the generating AI, which then selects the most suitable suggestions.

[0117] The baseball equipment selection support system can analyze a user's social media activity and provide relevant suggestions. For example, it can make relevant suggestions based on information the user has shared on social media. It can also make relevant suggestions based on the accounts the user follows on social media. Furthermore, it can analyze the user's social media activity history and provide relevant suggestions. This allows for improved user satisfaction by providing relevant suggestions based on the user's social media activity. The suggestions can be adjusted, for example, by inputting the user's social media activity data into a generating AI and having the AI ​​select relevant suggestions.

[0118] The baseball equipment selection support system can estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible information presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the information presentation according to the user's emotions, the ease with which information can be received can be improved. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjusting the information presentation can be done, for example, by inputting the user's facial expression data into the generative AI and having the generative AI perform emotion estimation.

[0119] The baseball equipment selection support system can select the optimal information delivery method by referring to the user's past data. For example, it can suggest the optimal information delivery method based on the data the user has previously provided. It can also select a highly accurate information delivery method from the user's past data. Furthermore, it can analyze the user's past data and suggest the most efficient information delivery method. This improves the ease with which information can be received by providing the optimal information delivery method based on past data. Information delivery can be adjusted, for example, by inputting the user's past data into a generating AI and having the generating AI select the optimal information delivery method.

[0120] The baseball equipment selection support system can customize its information provision algorithm based on the user's interests and characteristics. For example, it can select the optimal information provision algorithm based on the user's interests. It can also customize the information provision algorithm considering the user's characteristics. Furthermore, it can optimize the information provision algorithm based on the user's interests and characteristics. By customizing the information provision algorithm based on the user's interests and characteristics, the ease with which information can be received can be improved. Information provision adjustments can be made, for example, by inputting data on the user's interests and characteristics into a generating AI and having the generating AI perform the customization of the information provision algorithm.

[0121] The baseball equipment selection support system can estimate the user's emotions and prioritize information provision based on those emotions. For example, if the user is stressed, important information will be provided first. If the user is relaxed, detailed information can be provided first. Furthermore, if the user is in a hurry, only the most important information can be provided first. In this way, by prioritizing information provision according to the user's emotions, important information can be provided first. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Information provision can be adjusted, for example, by inputting the user's facial expression data into the generative AI and having the generative AI perform emotion estimation.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk enters the user's information. This information includes, for example, height, weight, hand size, fielding position, and bat swing speed. The reception desk can take an image of the user's palm held up to the camera to measure hand size. It can also record a video of the user swinging the bat to obtain data for measuring bat swing speed. Step 2: The measurement unit measures the hand size based on the information entered by the reception unit. For example, the measurement unit measures the hand size by analyzing an image of the hand taken with a mobile phone camera. By using image analysis technology to measure the length and width of the hand and analyzing the shape and characteristics of the hand, the hand size can be accurately measured. Step 3: The analysis unit analyzes the bat swing speed based on the information entered by the reception unit. For example, the analysis unit measures the bat swing speed by analyzing a video of the user swinging the bat. By using video analysis technology to measure the speed and angle of the swing, and analyzing the timing and force applied to the swing, the swing speed can be accurately measured. Step 4: The determination unit determines the length and weight of the bat, and the type, shape, and size of the glove, based on the information obtained by the measurement and analysis units. For example, the determination unit determines the appropriate bat length and weight based on the user's height, weight, and swing speed. It can also determine the appropriate glove type, shape, and size based on hand size and fielding position. Step 5: The providing unit provides the user with the information determined by the decision-making unit. For example, the providing unit displays to the user the determined length and weight of the bat, or the type, shape, and size of the glove. The information can be provided through web applications or mobile applications. It can also be provided through printed materials or email.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the reception unit, measurement unit, analysis unit, determination unit, and provision 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 inputs user information using the camera 42 and microphone 38B of the smart device 14. The measurement unit measures hand size, for example, by the control unit 46A of the smart device 14. The analysis unit analyzes bat swing speed, for example, by the identification processing unit 290 of the data processing unit 12. The determination unit determines the length and weight of the bat, the type, shape, and size of the glove, for example, by the identification processing unit 290 of the data processing unit 12. The provision unit provides information to the user, for example, through the output device 40 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, measurement unit, analysis unit, determination unit, and provision 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 inputs user information using the camera 42 and microphone 238 of the smart glasses 214. The measurement unit measures hand size, for example, using the control unit 46A of the smart glasses 214. The analysis unit analyzes bat swing speed, for example, using the identification processing unit 290 of the data processing unit 12. The determination unit determines the length and weight of the bat, the type, shape, and size of the glove, for example, using the identification processing unit 290 of the data processing unit 12. The provision unit provides information to the user, for example, through the speaker 240 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, measurement unit, analysis unit, determination unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit inputs user information using the camera 42 and microphone 238 of the headset terminal 314. The measurement unit measures hand size, for example, by the control unit 46A of the headset terminal 314. The analysis unit analyzes bat swing speed, for example, by the identification processing unit 290 of the data processing unit 12. The determination unit determines the length and weight of the bat, the type, shape, and size of the glove, for example, by the identification processing unit 290 of the data processing unit 12. The provision unit provides information to the user, for example, through the display 343 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 various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the reception unit, measurement unit, analysis unit, determination unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit inputs user information using the camera 42 and microphone 238 of the robot 414. The measurement unit measures the size of the hand, for example, by the control unit 46A of the robot 414. The analysis unit analyzes the bat swing speed, for example, by the identification processing unit 290 of the data processing unit 12. The determination unit determines the length and weight of the bat, the type, shape, and size of the glove, for example, by the identification processing unit 290 of the data processing unit 12. The provision unit provides information to the user, for example, by the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A reception area where user information is entered, A measuring unit that measures hand size based on information entered by the reception unit, An analysis unit analyzes the bat swing speed based on the information input by the reception unit, A determination unit that determines the length and weight of the bat, the type, shape, and size of the glove based on the information obtained by the measurement unit and the analysis unit, The system includes a provisioning unit that provides the information determined by the determination unit to the user. A system characterized by the following features. (Note 2) The aforementioned measuring unit is Hand size is measured by analyzing images of hands taken with a mobile phone camera. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes videos of users swinging a bat to measure their bat swing speed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned determination unit, The appropriate bat length and weight are determined based on the user's height, weight, and swing speed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned determination unit, The appropriate type, shape, and size of glove are determined based on hand size and defensive position. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The system provides users with the determined bat length and weight, as well as the type, shape, and size of their glove. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, input fields are customized based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account 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 entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the hand size measurement method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned measuring unit is When measuring hand size, the system selects the optimal measurement method by referring to the user's past measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned measuring unit is When measuring hand size, the measurement algorithm is customized based on the shape and characteristics of the user's hand. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned measuring unit is The system estimates the user's emotions and prioritizes hand size measurements based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned measuring unit is When measuring hand size, the optimal measurement method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned measuring unit is During hand size measurement, the system analyzes the user's social media activity and obtains relevant measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the bat swing speed analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing bat swing speed, the system selects the optimal analysis method by referring to the user's past swing data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing bat swing speed, the analysis algorithm is customized based on the user's swing style and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of bat swing speed analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing bat swing speed, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During bat swing speed analysis, the system analyzes the user's social media activity and obtains relevant analytical data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, The system estimates the user's emotions and adjusts the selection method for bats and gloves based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned determination unit, When selecting bats and gloves, the system uses the user's past selection data to determine the optimal selection method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned determination unit, When selecting bats and gloves, the selection algorithm is customized based on the user's body type and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned determination unit, The system estimates the user's emotions and determines the priority of bat and glove selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned determination unit, When selecting bats and gloves, the optimal selection method is determined by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned determination unit, When selecting bats and gloves, we analyze users' social media activity and obtain relevant selection data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, the system selects the optimal delivery method by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, the algorithm for providing information is customized based on the user's interests and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where user information is entered, A measuring unit that measures hand size based on information entered by the reception unit, An analysis unit analyzes the bat swing speed based on the information input by the reception unit, A determination unit that determines the length and weight of the bat, the type, shape, and size of the glove based on the information obtained by the measurement unit and the analysis unit, The system includes a provisioning unit that provides the information determined by the determination unit to the user. A system characterized by the following features.

2. The aforementioned measuring unit is Hand size is measured by analyzing images of hands taken with a mobile phone camera. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes videos of users swinging a bat to measure their bat swing speed. The system according to feature 1.

4. The aforementioned determination unit, The appropriate bat length and weight are determined based on the user's height, weight, and swing speed. The system according to feature 1.

5. The aforementioned determination unit, The appropriate type, shape, and size of glove are determined based on hand size and defensive position. The system according to feature 1.

6. The aforementioned supply unit is, The system provides users with the determined bat length and weight, as well as the type, shape, and size of their glove. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

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