system

Abstract

The system according to the embodiment aims to provide a pace distribution that suits the user while running. [Solution] A system according to an embodiment includes a registration unit, a location determination unit, a heart rate monitoring unit, a pace calculation unit, and an audio notification unit. The registration unit registers user data. The location determination unit determines the current location in cooperation with a GPS based on the data registered by the registration unit. The heart rate monitoring unit monitors the heart rate based on the current location determined by the location determination unit. The pace calculation unit calculates the pace based on the heart rate monitored by the heart rate monitoring unit. The audio notification unit audibly notifies the pace calculated by the pace calculation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to determine the appropriate pace for oneself while running.

[0005] The system according to the embodiment aims to provide a pace distribution that suits the user while running. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a location determination unit, a heart rate monitoring unit, a pace calculation unit, and an audio notification unit. The registration unit registers user data. The location determination unit determines the current location in cooperation with a GPS based on the data registered by the registration unit. The heart rate monitoring unit monitors the heart rate based on the current location determined by the location determination unit. The pace calculation unit calculates the pace based on the heart rate monitored by the heart rate monitoring unit. The audio notification unit audibly notifies the pace calculated by the pace calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a user with a tailored pace while running. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​watch system according to an embodiment of the present invention is designed for people who are unsure of their optimal pace when running. This AI watch system registers data such as the user's gender, height, weight, and blood information. During a run, it connects with GPS to determine the runner's current location, monitors the runner's heart rate, and provides an accurate pace via voice. For example, a user registers data such as gender, height, weight, and blood information into the AI ​​watch. To do so, the user simply inputs basic physical information, such as gender (male), height (170 cm), weight (65 kg), and blood type (A). This information is stored in the AI ​​watch. Next, the AI ​​watch connects with GPS during a run. The AI ​​watch tracks the runner's current location in real time and measures the distance and time traveled. For example, if a runner wants to run a 5-kilometer distance, the AI ​​watch measures the distance from the runner's current location to their destination and displays the running time in real time. Furthermore, the AI ​​watch monitors the runner's heart rate and provides an accurate pace via voice. The AI ​​watch monitors the runner's heart rate and calculates the appropriate pace. For example, if a runner's heart rate is too high, the AI ​​watch will voice instructions such as "Slow down your pace." Conversely, if their heart rate is too low, it will instruct them to "Increase your pace." This allows runners to run at a pace that suits them. For example, beginner runners can continue running without straining themselves, while advanced runners can train efficiently. The AI ​​watch can also accumulate runner data and suggest a running plan for the next run based on past running data. This allows runners to continuously optimize their pace. This allows the AI ​​watch system to support efficient training by allowing runners to run at a pace that suits them.

[0029] The AI ​​watch system according to the embodiment includes a registration unit, a location determination unit, a heart rate monitoring unit, a pace calculation unit, and an audio notification unit. The registration unit registers user data. The user data includes, but is not limited to, gender, height, weight, and blood information. The registration unit provides an interface for the user to input data such as gender, height, weight, and blood information. For example, the user can input this data via a smartphone app. The registration unit also has a function for saving the input data in the AI ​​watch. The location determination unit works in conjunction with a GPS to determine the runner's current location in real time. The location determination unit acquires the runner's current location using, for example, a GPS module. For example, the location determination unit can display the runner's current location in real time. The location determination unit also has a function for measuring the runner's running distance and running time. For example, if a runner wants to run a 5-kilometer distance, the location determination unit measures the distance from the runner's current location to the destination and displays the running time in real time. The heart rate monitoring unit monitors the runner's heart rate. The heart rate monitoring unit measures the runner's heart rate using, for example, a heart rate sensor. For example, the heart rate monitoring unit can display the runner's heart rate in real time. The heart rate monitoring unit also has a function to accumulate the runner's heart rate data. For example, the heart rate monitoring unit can record the runner's heart rate data in chronological order and refer to past data. The pace calculation unit calculates an appropriate pace based on the runner's heart rate. The pace calculation unit has, for example, an algorithm that receives heart rate data as input and calculates an appropriate pace. For example, the pace calculation unit calculates a pace to slow down if the runner's heart rate is too high. The pace calculation unit can also calculate a pace to increase if the heart rate is too low. The audio notification unit announces the calculated pace by voice. For example, the audio notification unit announces the calculated pace by voice using voice synthesis technology. For example, the audio notification unit can announce instructions such as "Please slow down your pace" or "Please increase your pace" by voice.This allows the AI ​​watch system according to the embodiment to allow the runner to run at a pace that suits them. Some or all of the above-described processing by the audio notification unit may be performed using AI, or may be performed without AI. For example, the audio notification unit may provide audio notification using an AI model for audibly communicating the calculated pace.

[0030] The AI ​​watch system includes a data accumulation unit. The data accumulation unit accumulates runner data and proposes a next running plan based on past running data. The data accumulation unit, for example, uses a database to store the runner's data. For example, the data accumulation unit chronologically records the runner's running distance, running time, heart rate data, and the like. The data accumulation unit also has a function for analyzing past running data and proposing a next running plan. For example, the data accumulation unit can propose a target distance and target time for the next run based on the runner's past data. This allows the runner to continuously optimize their pace. Some or all of the above-described processing in the data accumulation unit may be performed using, for example, AI, or may be performed without AI. For example, the data accumulation unit may analyze the runner's data and accumulate data using an AI model for proposing a next running plan.

[0031] The registration unit can register data including gender, height, weight, and blood information. For example, the registration unit provides an interface for the user to input data such as gender, height, weight, and blood information. For example, the user can input this data through a smartphone app. The registration unit also has a function to save the input data in the AI ​​watch. For example, the registration unit can save the data entered by the user in a database for later reference. This allows the user's basic physical information to be registered. Some or all of the above-mentioned processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the data entered by the user into an AI model and check the consistency of the data.

[0032] The position determination unit can determine the runner's current location in real time in cooperation with a GPS. The position determination unit, for example, acquires the runner's current location using a GPS module. For example, the position determination unit can display the runner's current location in real time. The position determination unit also has a function of measuring the runner's running distance and running time. For example, if a runner wants to run a 5km distance, the position determination unit measures the distance from the current location to the destination and displays the running time in real time. This makes it possible to determine the runner's current location in real time. Some or all of the above-mentioned processing in the position determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the position determination unit can input GPS data into an AI model to determine the runner's current location with high accuracy.

[0033] The heart rate monitoring unit can monitor the runner's heart rate. The heart rate monitoring unit measures the runner's heart rate using, for example, a heart rate sensor. For example, the heart rate monitoring unit can display the runner's heart rate in real time. The heart rate monitoring unit also has a function of accumulating the runner's heart rate data. For example, the heart rate monitoring unit can record the runner's heart rate data in chronological order and refer to past data. This makes it possible to monitor the runner's heart rate. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input heart rate sensor data into an AI model to detect abnormalities in the heart rate.

[0034] The pace calculation unit can calculate a pace based on the runner's heart rate. The pace calculation unit, for example, has an algorithm that receives heart rate data as input and calculates an appropriate pace. For example, if the runner's heart rate is too high, the pace calculation unit calculates to slow down the pace. Alternatively, if the heart rate is too low, the pace calculation unit can calculate to increase the pace. In this way, an appropriate pace can be calculated based on the runner's heart rate. Some or all of the above-mentioned processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input heart rate data into an AI model to calculate an optimal pace.

[0035] The voice notification unit can notify the calculated pace by voice. The voice notification unit can notify the calculated pace by voice, for example, using voice synthesis technology. For example, the voice notification unit can notify instructions such as "Please slow down your pace" or "Please speed up your pace" by voice. In this way, the calculated pace can be notified by voice. Some or all of the above-mentioned processing in the voice notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice notification unit can provide voice notification using an AI model for notifying the calculated pace by voice.

[0036] The data accumulation unit accumulates data on the runner and can propose a next running plan based on past running data. The data accumulation unit, for example, uses a database to store the runner's data. For example, the data accumulation unit chronologically records the runner's running distance, running time, heart rate data, etc. The data accumulation unit also has a function to analyze past running data and propose a next running plan. For example, the data accumulation unit can propose a target distance and target time for the next run based on the runner's past data. This allows the runner to continuously optimize their pace. Some or all of the above-mentioned processing in the data accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the data accumulation unit may analyze the runner's data and accumulate data using an AI model for proposing a next running plan.

[0037] The registration unit can analyze the user's past data registration history and select the optimal registration method. For example, the registration unit can prioritize and suggest registration methods (such as voice input or manual input) that the user has frequently used in the past. The registration unit can also analyze the time periods during which the user has previously registered data and suggest the optimal registration time. Furthermore, the registration unit can automatically complete specific data items from the user's past registration history. This makes it possible to select the optimal registration method based on the user's past data registration history. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's past data registration history into the generation AI and cause the generation AI to execute instructions to select the optimal registration method.

[0038] The registration unit can filter data based on the user's current health condition and exercise history when registering the data. The registration unit can prompt the user to register appropriate data based on, for example, the user's current heart rate and blood pressure. The registration unit can also refer to the user's past exercise history and preferentially register related data items. Furthermore, the registration unit can filter and register specific data items according to the user's health condition. This makes it possible to filter data based on the user's health condition and exercise history. Some or all of the above-mentioned processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's health data into the generation AI and cause the generation AI to execute filtering instructions.

[0039] When registering data, the registration unit can prioritize registering highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the registration unit can prioritize registering data related to that area. Furthermore, when the user is traveling, the registration unit can prioritize registering data related to the travel destination. Furthermore, when the user is at home, the registration unit can also prioritize registering data related to daily life. This makes it possible to prioritize registering highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to prioritize registering highly relevant data.

[0040] The registration unit can analyze the user's social media activities and register related data when registering data. For example, the registration unit can automatically register exercise data shared by the user on social media. The registration unit can also register data based on information about fitness influencers the user follows on social media. Furthermore, the registration unit can analyze the content of the user's social media posts and register related data. This makes it possible to register related data based on the user's social media activities. Some or all of the above-described processing in the registration unit can be performed using, or without, AI, for example. For example, the registration unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to register related data.

[0041] When acquiring location information, the location determination unit can suggest an optimal route by referring to the user's past running history. For example, the location determination unit can suggest an optimal running route based on routes the user has run in the past. The location determination unit can also suggest a route that avoids crowded areas based on the user's past running history. Furthermore, the location determination unit can analyze the user's past running history and suggest the most efficient route. This makes it possible to suggest an optimal route based on the user's past running history. Some or all of the above-mentioned processing in the location determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the location determination unit can input the user's past running data into the generation AI and cause the generation AI to execute instructions to suggest an optimal route.

[0042] When acquiring location information, the location determination unit can adjust the accuracy of the location information based on the user's current exercise intensity. For example, when the user is performing high-intensity exercise, the location determination unit can increase the accuracy of the location information. Furthermore, when the user is performing low-intensity exercise, the location determination unit can decrease the accuracy of the location information. Furthermore, the location determination unit can adjust the frequency of acquiring location information according to the user's exercise intensity. This allows the accuracy of the location information to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the location determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the location determination unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the accuracy of the location information.

[0043] The location ascertaining unit can propose an optimal route by taking into account the user's geographical location information when acquiring the location information. For example, if the user is in a specific area, the location ascertaining unit can propose an optimal route related to that area. Furthermore, if the user is traveling, the location ascertaining unit can propose an optimal route related to the travel destination. Furthermore, if the user is at home, the location ascertaining unit can also propose an optimal route related to daily life. This makes it possible to propose an optimal route based on the user's geographical location information. Some or all of the above-described processing in the location ascertaining unit may be performed using, for example, AI, or may be performed without using AI. For example, the location ascertaining unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to propose an optimal route.

[0044] When acquiring location information, the location identification unit can analyze the user's social media activities and acquire related location information. The location identification unit can, for example, suggest an optimal route based on location information shared by the user on social media. The location identification unit can also suggest an optimal route based on information about places the user follows on social media. The location identification unit can also analyze the content of the user's social media posts and acquire related location information. This makes it possible to acquire related location information based on the user's social media activities. Some or all of the above-described processing in the location identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the location identification unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to acquire related location information.

[0045] During heart rate monitoring, the heart rate monitoring unit can detect abnormalities by referring to the user's past heart rate data. For example, the heart rate monitoring unit detects abnormal heart rates based on the user's past heart rate data. The heart rate monitoring unit can also detect abnormal patterns from the user's past heart rate data. Furthermore, the heart rate monitoring unit can analyze the user's past heart rate data and detect abnormal fluctuations. This makes it possible to detect abnormalities based on the user's past heart rate data. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input the user's past heart rate data into a generation AI and cause the generation AI to execute instructions to detect abnormalities.

[0046] The heart rate monitoring unit can adjust the accuracy of heart rate monitoring based on the user's current exercise intensity during heart rate monitoring. For example, when the user is performing high-intensity exercise, the heart rate monitoring unit can increase the accuracy of heart rate monitoring. Also, when the user is performing low-intensity exercise, the heart rate monitoring unit can decrease the accuracy of heart rate monitoring. Furthermore, the heart rate monitoring unit can adjust the frequency of heart rate monitoring according to the user's exercise intensity. This allows the accuracy of heart rate monitoring to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the monitoring accuracy.

[0047] The heart rate monitoring unit can adjust the accuracy of heart rate monitoring by taking into account the user's geographical location information. For example, when the user is at a high altitude, the heart rate monitoring unit can increase the accuracy of heart rate monitoring. Also, when the user is at a low altitude, the heart rate monitoring unit can decrease the accuracy of heart rate monitoring. Furthermore, the heart rate monitoring unit can adjust the frequency of heart rate monitoring according to the user's geographical location information. This allows the accuracy of heart rate monitoring to be adjusted based on the user's geographical location information. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to adjust the monitoring accuracy.

[0048] During heart rate monitoring, the heart rate monitoring unit can analyze the user's social media activities and acquire related heart rate data. For example, the heart rate monitoring unit can acquire heart rate data based on exercise data shared by the user on social media. The heart rate monitoring unit can also acquire heart rate data based on information about fitness influencers the user follows on social media. Furthermore, the heart rate monitoring unit can analyze the content of the user's social media posts and acquire related heart rate data. This allows related heart rate data to be acquired based on the user's social media activities. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, or without, AI. For example, the heart rate monitoring unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to acquire related heart rate data.

[0049] When calculating the pace, the pace calculation unit can calculate the optimal pace by referring to the user's past running data. The pace calculation unit, for example, calculates the optimal pace based on the user's past running data. The pace calculation unit can also calculate an efficient pace from the user's past running data. Furthermore, the pace calculation unit can analyze the user's past running data and calculate the most effective pace. This makes it possible to calculate the optimal pace based on the user's past running data. Some or all of the above-mentioned processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input the user's past running data into the generation AI and cause the generation AI to execute instructions for calculating the optimal pace.

[0050] The pace calculation unit can adjust the accuracy of the calculation based on the user's current exercise intensity when calculating the pace. For example, the pace calculation unit can increase the accuracy of the pace calculation when the user is performing high-intensity exercise. Furthermore, the pace calculation unit can decrease the accuracy of the pace calculation when the user is performing low-intensity exercise. Furthermore, the pace calculation unit can adjust the frequency of pace calculation according to the user's exercise intensity. This makes it possible to adjust the accuracy of the pace calculation based on the user's exercise intensity. Some or all of the above-described processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the calculation accuracy.

[0051] The pace calculation unit can calculate an optimal pace by taking the user's geographical location information into consideration when calculating the pace. For example, the pace calculation unit calculates an appropriate pace when the user is at high altitude. The pace calculation unit can also calculate an appropriate pace when the user is at low altitude. Furthermore, the pace calculation unit can calculate an optimal pace according to the user's geographical location information. This makes it possible to calculate an optimal pace based on the user's geographical location information. Some or all of the above-described processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions for calculating an optimal pace.

[0052] When calculating the pace, the pace calculation unit can analyze the user's social media activities and obtain related pace data. The pace calculation unit can calculate the optimal pace based on, for example, running data shared by the user on social media. The pace calculation unit can also calculate the optimal pace based on information about fitness influencers the user follows on social media. The pace calculation unit can also analyze the content of the user's social media posts and obtain related pace data. This makes it possible to obtain related pace data based on the user's social media activities. Some or all of the above-described processing in the pace calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the pace calculation unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to obtain related pace data.

[0053] When providing a voice notification, the voice notification unit can select optimal notification content by referring to the user's past running data. For example, the voice notification unit selects optimal notification content based on the user's past running data. The voice notification unit can also select efficient notification content from the user's past running data. Furthermore, the voice notification unit can analyze the user's past running data and select the most effective notification content. This allows optimal notification content to be selected based on the user's past running data. Some or all of the above-described processing in the voice notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice notification unit can input the user's past running data into a generation AI and cause the generation AI to execute instructions to select optimal notification content.

[0054] The voice notification unit can adjust the content of the notification based on the user's current exercise intensity when providing the voice notification. For example, the voice notification unit can simplify the notification content when the user is performing high-intensity exercise. Furthermore, the voice notification unit can provide detailed notification content when the user is performing low-intensity exercise. Furthermore, the voice notification unit can adjust the notification content according to the user's exercise intensity. This allows the notification content to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the voice notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice notification unit can input the user's exercise intensity data into the generation AI and cause the generation AI to execute instructions to adjust the notification content.

[0055] The voice notification unit can select optimal notification content by taking into account the user's geographical location information when providing a voice notification. For example, if the user is in a specific area, the voice notification unit can provide notification content related to that area. Furthermore, if the user is traveling, the voice notification unit can provide notification content related to the user's travel destination. Furthermore, if the user is at home, the voice notification unit can also provide notification content related to the user's daily life. This makes it possible to select optimal notification content based on the user's geographical location information. Some or all of the above-described processing in the voice notification unit may be performed using, or without, AI, for example. For example, the voice notification unit can input the user's geographical location information to a generation AI and cause the generation AI to execute instructions to select optimal notification content.

[0056] The voice notification unit can analyze the user's social media activity and provide relevant notification content when providing a voice notification. The voice notification unit can provide notification content based on, for example, exercise data shared by the user on social media. The voice notification unit can also provide notification content based on information about fitness influencers the user follows on social media. Furthermore, the voice notification unit can analyze the user's social media posts and provide relevant notification content. This allows relevant notification content to be provided based on the user's social media activity. Some or all of the above-described processing in the voice notification unit can be performed using, or without, AI. For example, the voice notification unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to provide relevant notification content.

[0057] When accumulating data, the data accumulation unit can select the optimal data accumulation method by referring to the user's past running data. The data accumulation unit, for example, selects the optimal data accumulation method based on the user's past running data. The data accumulation unit can also select an efficient data accumulation method from the user's past running data. Furthermore, the data accumulation unit can analyze the user's past running data and select the most effective data accumulation method. This makes it possible to select the optimal data accumulation method based on the user's past running data. Some or all of the above-mentioned processing in the data accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the data accumulation unit can input the user's past running data into the generation AI and cause the generation AI to execute instructions to select the optimal data accumulation method.

[0058] The data accumulation unit can adjust the accuracy of the accumulated data based on the user's current exercise intensity when accumulating data. For example, the data accumulation unit can increase the accuracy of the accumulated data when the user is performing high-intensity exercise. Furthermore, the data accumulation unit can decrease the accuracy of the accumulated data when the user is performing low-intensity exercise. Furthermore, the data accumulation unit can adjust the accuracy of the accumulated data according to the user's exercise intensity. This allows the accuracy of the accumulated data to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the data accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the data accumulation unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the accuracy of the accumulated data.

[0059] When storing data, the data storage unit can select the optimal data storage method by taking into account the user's geographical location information. For example, when the user is in a specific area, the data storage unit can prioritize storing data related to that area. Furthermore, when the user is traveling, the data storage unit can prioritize storing data related to the travel destination. Furthermore, when the user is at home, the data storage unit can also prioritize storing data related to daily life. This makes it possible to select the optimal data storage method based on the user's geographical location information. Some or all of the above-mentioned processing in the data storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the data storage unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to select the optimal data storage method.

[0060] The data accumulation unit can analyze the user's social media activities and accumulate related data when accumulating data. For example, the data accumulation unit accumulates data based on exercise data shared by the user on social media. The data accumulation unit can also accumulate data based on information about fitness influencers the user follows on social media. Furthermore, the data accumulation unit can analyze the content of the user's social media posts and accumulate related data. This allows related data to be accumulated based on the user's social media activities. Some or all of the above-described processing in the data accumulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the data accumulation unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to accumulate related data.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The AI ​​watch system can also include a form analysis unit that analyzes the user's running form. The form analysis unit can analyze the user's running form using a camera and sensors and notify the user of improvements via a voice notification unit. For example, the form analysis unit can analyze the user's foot strike position and knee angle and provide advice on maintaining proper form. The form analysis unit can also record changes in the user's running form over time and compare them with past data to identify areas for improvement. Furthermore, the form analysis unit can analyze the user's running form in real time and provide instant feedback. This allows the user to maintain an efficient running form and reduce the risk of injury.

[0063] The AI ​​watch system can also include a diet management unit that manages the user's dietary data. The diet management unit can record the contents of the user's meals and analyze their impact on running performance. For example, the diet management unit can record the user's calorie and nutrient intake and suggest appropriate meals before and after running. The diet management unit can also accumulate the user's dietary data and suggest dietary improvements based on past data. Furthermore, the diet management unit can link the user's dietary data with running data and analyze the relationship between diet and running performance. This allows the user to optimize the balance between diet and running and improve performance.

[0064] The AI ​​watch system can also be equipped with a sleep management unit that manages the user's sleep data. The sleep management unit can record the user's sleep patterns and analyze their impact on running performance. For example, the sleep management unit can record the user's sleep duration and quality and suggest appropriate sleep durations. The sleep management unit can also accumulate the user's sleep data and suggest areas for improvement based on past data. Furthermore, the sleep management unit can link the user's sleep data with running data and analyze the relationship between sleep and running performance. This allows the user to optimize the balance between sleep and running and improve their performance.

[0065] The AI ​​watch system can also be equipped with a stress management unit that manages the user's stress level. The stress management unit can monitor the user's stress level and analyze its impact on running performance. For example, the stress management unit can measure the user's heart rate variability and electrodermal response to evaluate the stress level. The stress management unit can also accumulate the user's stress data and suggest ways to improve stress based on past data. Furthermore, the stress management unit can link the user's stress data with running data and analyze the relationship between stress and running performance. This allows the user to optimize the balance between stress and running and improve their performance.

[0066] The AI ​​watch system can also be equipped with a hydration management unit that manages the user's hydration. The hydration management unit can record the user's hydration intake and analyze its impact on running performance. For example, the hydration management unit can record the amount of water the user has consumed and suggest appropriate hydration. The hydration management unit can also accumulate the user's hydration data and suggest improvements to hydration based on past data. Furthermore, the hydration management unit can link the user's hydration data with running data to analyze the relationship between hydration and running performance. This allows the user to optimize the balance between hydration and running and improve their performance.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The registration unit registers the user's data. The user's data includes gender, height, weight, blood information, etc. The registration unit provides an interface for the user to input this data and stores the input data in the AI ​​watch. Step 2: The location determination unit works in conjunction with the GPS to determine the runner's current location in real time. The location determination unit uses a GPS module to obtain the runner's current location and has the ability to measure the distance and time traveled. Step 3: The heart rate monitoring unit monitors the runner's heart rate. The heart rate monitoring unit uses a heart rate sensor to measure the runner's heart rate and display it in real time. It also stores heart rate data and allows users to refer to past data. Step 4: The pace calculator calculates the appropriate pace based on the runner's heart rate. The pace calculator takes heart rate data as input and has an algorithm that calculates the appropriate pace. If the heart rate is too high, the pace should be slowed down; if the heart rate is too low, the pace should be increased. Step 5: The voice notification unit announces the calculated pace by voice. The voice notification unit can announce instructions such as "Please slow down your pace" or "Please speed up your pace" by using voice synthesis technology.

[0069] (Example 2) The AI ​​watch system according to an embodiment of the present invention is designed for people who are unsure of their optimal pace when running. This AI watch system registers data such as the user's gender, height, weight, and blood information. During a run, it connects with GPS to determine the runner's current location, monitors the runner's heart rate, and provides an accurate pace via voice. For example, a user registers data such as gender, height, weight, and blood information into the AI ​​watch. To do so, the user simply inputs basic physical information, such as gender (male), height (170 cm), weight (65 kg), and blood type (A). This information is stored in the AI ​​watch. Next, the AI ​​watch connects with GPS during a run. The AI ​​watch tracks the runner's current location in real time and measures the distance and time traveled. For example, if a runner wants to run a 5-kilometer distance, the AI ​​watch measures the distance from the runner's current location to their destination and displays the running time in real time. Furthermore, the AI ​​watch monitors the runner's heart rate and provides an accurate pace via voice. The AI ​​watch monitors the runner's heart rate and calculates the appropriate pace. For example, if a runner's heart rate is too high, the AI ​​watch will voice instructions such as "Slow down your pace." Conversely, if their heart rate is too low, it will instruct them to "Increase your pace." This allows runners to run at a pace that suits them. For example, beginner runners can continue running without straining themselves, while advanced runners can train efficiently. The AI ​​watch can also accumulate runner data and suggest a running plan for the next run based on past running data. This allows runners to continuously optimize their pace. This allows the AI ​​watch system to support efficient training by allowing runners to run at a pace that suits them.

[0070] The AI ​​watch system according to the embodiment includes a registration unit, a location determination unit, a heart rate monitoring unit, a pace calculation unit, and an audio notification unit. The registration unit registers user data. The user data includes, but is not limited to, gender, height, weight, and blood information. The registration unit provides an interface for the user to input data such as gender, height, weight, and blood information. For example, the user can input this data via a smartphone app. The registration unit also has a function for saving the input data in the AI ​​watch. The location determination unit works in conjunction with a GPS to determine the runner's current location in real time. The location determination unit acquires the runner's current location using, for example, a GPS module. For example, the location determination unit can display the runner's current location in real time. The location determination unit also has a function for measuring the runner's running distance and running time. For example, if a runner wants to run a 5-kilometer distance, the location determination unit measures the distance from the runner's current location to the destination and displays the running time in real time. The heart rate monitoring unit monitors the runner's heart rate. The heart rate monitoring unit measures the runner's heart rate using, for example, a heart rate sensor. For example, the heart rate monitoring unit can display the runner's heart rate in real time. The heart rate monitoring unit also has a function to accumulate the runner's heart rate data. For example, the heart rate monitoring unit can record the runner's heart rate data in chronological order and refer to past data. The pace calculation unit calculates an appropriate pace based on the runner's heart rate. The pace calculation unit has, for example, an algorithm that receives heart rate data as input and calculates an appropriate pace. For example, the pace calculation unit calculates a pace to slow down if the runner's heart rate is too high. The pace calculation unit can also calculate a pace to increase if the heart rate is too low. The audio notification unit announces the calculated pace by voice. For example, the audio notification unit announces the calculated pace by voice using voice synthesis technology. For example, the audio notification unit can announce instructions such as "Please slow down your pace" or "Please increase your pace" by voice.This allows the AI ​​watch system according to the embodiment to allow the runner to run at a pace that suits them. Some or all of the above-described processing by the audio notification unit may be performed using AI, or may be performed without AI. For example, the audio notification unit may provide audio notification using an AI model for audibly communicating the calculated pace.

[0071] The AI ​​watch system includes a data accumulation unit. The data accumulation unit accumulates runner data and proposes a next running plan based on past running data. The data accumulation unit, for example, uses a database to store the runner's data. For example, the data accumulation unit chronologically records the runner's running distance, running time, heart rate data, and the like. The data accumulation unit also has a function for analyzing past running data and proposing a next running plan. For example, the data accumulation unit can propose a target distance and target time for the next run based on the runner's past data. This allows the runner to continuously optimize their pace. Some or all of the above-described processing in the data accumulation unit may be performed using, for example, AI, or may be performed without AI. For example, the data accumulation unit may analyze the runner's data and accumulate data using an AI model for proposing a next running plan.

[0072] The registration unit can register data including gender, height, weight, and blood information. For example, the registration unit provides an interface for the user to input data such as gender, height, weight, and blood information. For example, the user can input this data through a smartphone app. The registration unit also has a function to save the input data in the AI ​​watch. For example, the registration unit can save the data entered by the user in a database for later reference. This allows the user's basic physical information to be registered. Some or all of the above-mentioned processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the data entered by the user into an AI model and check the consistency of the data.

[0073] The position determination unit can determine the runner's current location in real time in cooperation with a GPS. The position determination unit, for example, acquires the runner's current location using a GPS module. For example, the position determination unit can display the runner's current location in real time. The position determination unit also has a function of measuring the runner's running distance and running time. For example, if a runner wants to run a 5km distance, the position determination unit measures the distance from the current location to the destination and displays the running time in real time. This makes it possible to determine the runner's current location in real time. Some or all of the above-mentioned processing in the position determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the position determination unit can input GPS data into an AI model to determine the runner's current location with high accuracy.

[0074] The heart rate monitoring unit can monitor the runner's heart rate. The heart rate monitoring unit measures the runner's heart rate using, for example, a heart rate sensor. For example, the heart rate monitoring unit can display the runner's heart rate in real time. The heart rate monitoring unit also has a function of accumulating the runner's heart rate data. For example, the heart rate monitoring unit can record the runner's heart rate data in chronological order and refer to past data. This makes it possible to monitor the runner's heart rate. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input heart rate sensor data into an AI model to detect abnormalities in the heart rate.

[0075] The pace calculation unit can calculate a pace based on the runner's heart rate. The pace calculation unit, for example, has an algorithm that receives heart rate data as input and calculates an appropriate pace. For example, if the runner's heart rate is too high, the pace calculation unit calculates to slow down the pace. Alternatively, if the heart rate is too low, the pace calculation unit can calculate to increase the pace. In this way, an appropriate pace can be calculated based on the runner's heart rate. Some or all of the above-mentioned processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input heart rate data into an AI model to calculate an optimal pace.

[0076] The voice notification unit can notify the calculated pace by voice. The voice notification unit can notify the calculated pace by voice, for example, using voice synthesis technology. For example, the voice notification unit can notify instructions such as "Please slow down your pace" or "Please speed up your pace" by voice. In this way, the calculated pace can be notified by voice. Some or all of the above-mentioned processing in the voice notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice notification unit can provide voice notification using an AI model for notifying the calculated pace by voice.

[0077] The data accumulation unit accumulates data on the runner and can propose a next running plan based on past running data. The data accumulation unit, for example, uses a database to store the runner's data. For example, the data accumulation unit chronologically records the runner's running distance, running time, heart rate data, etc. The data accumulation unit also has a function to analyze past running data and propose a next running plan. For example, the data accumulation unit can propose a target distance and target time for the next run based on the runner's past data. This allows the runner to continuously optimize their pace. Some or all of the above-mentioned processing in the data accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the data accumulation unit may analyze the runner's data and accumulate data using an AI model for proposing a next running plan.

[0078] The registration unit can estimate the user's emotions and adjust the timing of data registration based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prompt the user to register data during a time when the user is able to relax. Furthermore, if the user is relaxed, the registration unit can prompt the user to register data immediately. Furthermore, if the user is in a hurry, the registration unit can set a reminder to register data later. This allows the timing of data registration to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the registration unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the timing of data registration.

[0079] The registration unit can analyze the user's past data registration history and select the optimal registration method. For example, the registration unit can prioritize and suggest registration methods (such as voice input or manual input) that the user has frequently used in the past. The registration unit can also analyze the time periods during which the user has previously registered data and suggest the optimal registration time. Furthermore, the registration unit can automatically complete specific data items from the user's past registration history. This makes it possible to select the optimal registration method based on the user's past data registration history. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's past data registration history into the generation AI and cause the generation AI to execute instructions to select the optimal registration method.

[0080] The registration unit can filter data based on the user's current health condition and exercise history when registering the data. The registration unit can prompt the user to register appropriate data based on, for example, the user's current heart rate and blood pressure. The registration unit can also refer to the user's past exercise history and preferentially register related data items. Furthermore, the registration unit can filter and register specific data items according to the user's health condition. This makes it possible to filter data based on the user's health condition and exercise history. Some or all of the above-mentioned processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's health data into the generation AI and cause the generation AI to execute filtering instructions.

[0081] The registration unit can estimate the user's emotions and determine the priority of data to be registered based on the estimated user emotions. For example, when the user is feeling stressed, the registration unit can prioritize registering important data items. Furthermore, when the user is relaxed, the registration unit can register detailed data items. Furthermore, when the user is in a hurry, the registration unit can prioritize registering the minimum number of data items. This allows data prioritization based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions for determining the priority of the data.

[0082] When registering data, the registration unit can prioritize registering highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the registration unit can prioritize registering data related to that area. Furthermore, when the user is traveling, the registration unit can prioritize registering data related to the travel destination. Furthermore, when the user is at home, the registration unit can also prioritize registering data related to daily life. This makes it possible to prioritize registering highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to prioritize registering highly relevant data.

[0083] The registration unit can analyze the user's social media activities and register related data when registering data. For example, the registration unit can automatically register exercise data shared by the user on social media. The registration unit can also register data based on information about fitness influencers the user follows on social media. Furthermore, the registration unit can analyze the content of the user's social media posts and register related data. This makes it possible to register related data based on the user's social media activities. Some or all of the above-described processing in the registration unit can be performed using, or without, AI, for example. For example, the registration unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to register related data.

[0084] The location ascertaining unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user's emotions. For example, when the user is relaxed, the location ascertaining unit can periodically acquire location information. Furthermore, when the user is in a hurry, the location ascertaining unit can frequently acquire location information. Furthermore, when the user is stressed, the location ascertaining unit can reduce the frequency of acquiring location information. This allows the timing of acquiring location information to be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the location ascertaining unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the location ascertaining unit can input the user's emotion data to the generation AI and cause the generation AI to execute instructions to adjust the timing of acquiring location information.

[0085] When acquiring location information, the location determination unit can suggest an optimal route by referring to the user's past running history. For example, the location determination unit can suggest an optimal running route based on routes the user has run in the past. The location determination unit can also suggest a route that avoids crowded areas based on the user's past running history. Furthermore, the location determination unit can analyze the user's past running history and suggest the most efficient route. This makes it possible to suggest an optimal route based on the user's past running history. Some or all of the above-mentioned processing in the location determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the location determination unit can input the user's past running data into the generation AI and cause the generation AI to execute instructions to suggest an optimal route.

[0086] When acquiring location information, the location determination unit can adjust the accuracy of the location information based on the user's current exercise intensity. For example, when the user is performing high-intensity exercise, the location determination unit can increase the accuracy of the location information. Furthermore, when the user is performing low-intensity exercise, the location determination unit can decrease the accuracy of the location information. Furthermore, the location determination unit can adjust the frequency of acquiring location information according to the user's exercise intensity. This allows the accuracy of the location information to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the location determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the location determination unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the accuracy of the location information.

[0087] The location grasping unit can estimate the user's emotions and adjust the display method of the location information based on the estimated user's emotions. For example, if the user is nervous, the location grasping unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the location grasping unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the location grasping unit can provide a display method that focuses on the main points. This allows the display method of the location information to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location grasping unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the location grasping unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the display method of the location information.

[0088] The location ascertaining unit can propose an optimal route by taking into account the user's geographical location information when acquiring the location information. For example, if the user is in a specific area, the location ascertaining unit can propose an optimal route related to that area. Furthermore, if the user is traveling, the location ascertaining unit can propose an optimal route related to the travel destination. Furthermore, if the user is at home, the location ascertaining unit can also propose an optimal route related to daily life. This makes it possible to propose an optimal route based on the user's geographical location information. Some or all of the above-described processing in the location ascertaining unit may be performed using, for example, AI, or may be performed without using AI. For example, the location ascertaining unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to propose an optimal route.

[0089] When acquiring location information, the location identification unit can analyze the user's social media activities and acquire related location information. The location identification unit can, for example, suggest an optimal route based on location information shared by the user on social media. The location identification unit can also suggest an optimal route based on information about places the user follows on social media. The location identification unit can also analyze the content of the user's social media posts and acquire related location information. This makes it possible to acquire related location information based on the user's social media activities. Some or all of the above-described processing in the location identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the location identification unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to acquire related location information.

[0090] The heart rate monitoring unit can estimate the user's emotions and adjust the heart rate monitoring frequency based on the estimated user emotions. For example, the heart rate monitoring unit periodically monitors the heart rate when the user is relaxed. Furthermore, the heart rate monitoring unit can frequently monitor the heart rate when the user is in a hurry. Furthermore, the heart rate monitoring unit can reduce the heart rate monitoring frequency when the user is stressed. This allows the heart rate monitoring frequency to be adjusted based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the heart rate monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the heart rate monitoring frequency.

[0091] During heart rate monitoring, the heart rate monitoring unit can detect abnormalities by referring to the user's past heart rate data. For example, the heart rate monitoring unit detects abnormal heart rates based on the user's past heart rate data. The heart rate monitoring unit can also detect abnormal patterns from the user's past heart rate data. Furthermore, the heart rate monitoring unit can analyze the user's past heart rate data and detect abnormal fluctuations. This makes it possible to detect abnormalities based on the user's past heart rate data. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input the user's past heart rate data into a generation AI and cause the generation AI to execute instructions to detect abnormalities.

[0092] The heart rate monitoring unit can adjust the accuracy of heart rate monitoring based on the user's current exercise intensity during heart rate monitoring. For example, when the user is performing high-intensity exercise, the heart rate monitoring unit can increase the accuracy of heart rate monitoring. Also, when the user is performing low-intensity exercise, the heart rate monitoring unit can decrease the accuracy of heart rate monitoring. Furthermore, the heart rate monitoring unit can adjust the frequency of heart rate monitoring according to the user's exercise intensity. This allows the accuracy of heart rate monitoring to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the monitoring accuracy.

[0093] The heart rate monitoring unit can estimate the user's emotions and adjust the display method of the heart rate data based on the estimated user emotions. For example, if the user is nervous, the heart rate monitoring unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the heart rate monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the heart rate monitoring unit can provide a display method that focuses on the main points. This allows the display method of the heart rate data to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the heart rate monitoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the heart rate monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the display method of the heart rate data.

[0094] The heart rate monitoring unit can adjust the accuracy of heart rate monitoring by taking into account the user's geographical location information. For example, when the user is at a high altitude, the heart rate monitoring unit can increase the accuracy of heart rate monitoring. Also, when the user is at a low altitude, the heart rate monitoring unit can decrease the accuracy of heart rate monitoring. Furthermore, the heart rate monitoring unit can adjust the frequency of heart rate monitoring according to the user's geographical location information. This allows the accuracy of heart rate monitoring to be adjusted based on the user's geographical location information. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the heart rate monitoring unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to adjust the monitoring accuracy.

[0095] During heart rate monitoring, the heart rate monitoring unit can analyze the user's social media activities and acquire related heart rate data. For example, the heart rate monitoring unit can acquire heart rate data based on exercise data shared by the user on social media. The heart rate monitoring unit can also acquire heart rate data based on information about fitness influencers the user follows on social media. Furthermore, the heart rate monitoring unit can analyze the content of the user's social media posts and acquire related heart rate data. This allows related heart rate data to be acquired based on the user's social media activities. Some or all of the above-described processing in the heart rate monitoring unit may be performed using, or without, AI. For example, the heart rate monitoring unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to acquire related heart rate data.

[0096] The pace calculation unit can estimate the user's emotions and adjust the pace calculation algorithm based on the estimated user emotions. For example, if the user is relaxed, the pace calculation unit can calculate a relaxed pace. Furthermore, if the user is in a hurry, the pace calculation unit can calculate a fast pace. Furthermore, if the user is feeling stressed, the pace calculation unit can calculate a steady pace. This allows the pace calculation algorithm to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the pace calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pace calculation unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the pace calculation algorithm.

[0097] When calculating the pace, the pace calculation unit can calculate the optimal pace by referring to the user's past running data. The pace calculation unit, for example, calculates the optimal pace based on the user's past running data. The pace calculation unit can also calculate an efficient pace from the user's past running data. Furthermore, the pace calculation unit can analyze the user's past running data and calculate the most effective pace. This makes it possible to calculate the optimal pace based on the user's past running data. Some or all of the above-mentioned processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input the user's past running data into the generation AI and cause the generation AI to execute instructions for calculating the optimal pace.

[0098] The pace calculation unit can adjust the accuracy of the calculation based on the user's current exercise intensity when calculating the pace. For example, the pace calculation unit can increase the accuracy of the pace calculation when the user is performing high-intensity exercise. Furthermore, the pace calculation unit can decrease the accuracy of the pace calculation when the user is performing low-intensity exercise. Furthermore, the pace calculation unit can adjust the frequency of pace calculation according to the user's exercise intensity. This makes it possible to adjust the accuracy of the pace calculation based on the user's exercise intensity. Some or all of the above-described processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the calculation accuracy.

[0099] The pace calculation unit can estimate the user's emotions and adjust the display method of the pace calculation results based on the estimated user emotions. For example, if the user is nervous, the pace calculation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the pace calculation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the pace calculation unit can provide a display method that focuses on the main points. This allows the display method of the pace calculation results to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the pace calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pace calculation unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the display method of the pace calculation results.

[0100] The pace calculation unit can calculate an optimal pace by taking the user's geographical location information into consideration when calculating the pace. For example, the pace calculation unit calculates an appropriate pace when the user is at high altitude. The pace calculation unit can also calculate an appropriate pace when the user is at low altitude. Furthermore, the pace calculation unit can calculate an optimal pace according to the user's geographical location information. This makes it possible to calculate an optimal pace based on the user's geographical location information. Some or all of the above-described processing in the pace calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pace calculation unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions for calculating an optimal pace.

[0101] When calculating the pace, the pace calculation unit can analyze the user's social media activities and obtain related pace data. The pace calculation unit can calculate the optimal pace based on, for example, running data shared by the user on social media. The pace calculation unit can also calculate the optimal pace based on information about fitness influencers the user follows on social media. The pace calculation unit can also analyze the content of the user's social media posts and obtain related pace data. This makes it possible to obtain related pace data based on the user's social media activities. Some or all of the above-described processing in the pace calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the pace calculation unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to obtain related pace data.

[0102] The voice notification unit can estimate the user's emotions and adjust the timing of the voice notification based on the estimated user's emotions. For example, the voice notification unit can periodically provide voice notifications when the user is relaxed. Furthermore, the voice notification unit can frequently provide voice notifications when the user is in a hurry. Furthermore, the voice notification unit can reduce the frequency of voice notifications when the user is stressed. This allows the timing of voice notifications to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the voice notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the voice notification unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the timing of voice notifications.

[0103] When providing a voice notification, the voice notification unit can select optimal notification content by referring to the user's past running data. For example, the voice notification unit selects optimal notification content based on the user's past running data. The voice notification unit can also select efficient notification content from the user's past running data. Furthermore, the voice notification unit can analyze the user's past running data and select the most effective notification content. This allows optimal notification content to be selected based on the user's past running data. Some or all of the above-described processing in the voice notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice notification unit can input the user's past running data into a generation AI and cause the generation AI to execute instructions to select optimal notification content.

[0104] The voice notification unit can adjust the content of the notification based on the user's current exercise intensity when providing the voice notification. For example, the voice notification unit can simplify the notification content when the user is performing high-intensity exercise. Furthermore, the voice notification unit can provide detailed notification content when the user is performing low-intensity exercise. Furthermore, the voice notification unit can adjust the notification content according to the user's exercise intensity. This allows the notification content to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the voice notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice notification unit can input the user's exercise intensity data into the generation AI and cause the generation AI to execute instructions to adjust the notification content.

[0105] The voice notification unit can estimate the user's emotions and adjust the content of the voice notification based on the estimated user's emotions. For example, if the user is nervous, the voice notification unit can provide a notification in a calm voice. Furthermore, if the user is relaxed, the voice notification unit can provide a notification in a cheerful voice. Furthermore, if the user is in a hurry, the voice notification unit can provide a quick and concise voice notification. This allows the content of the voice notification to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the voice notification unit may be performed using AI, for example, or without AI. For example, the voice notification unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the content of the voice notification.

[0106] The voice notification unit can select optimal notification content by taking into account the user's geographical location information when providing a voice notification. For example, if the user is in a specific area, the voice notification unit can provide notification content related to that area. Furthermore, if the user is traveling, the voice notification unit can provide notification content related to the user's travel destination. Furthermore, if the user is at home, the voice notification unit can also provide notification content related to the user's daily life. This makes it possible to select optimal notification content based on the user's geographical location information. Some or all of the above-described processing in the voice notification unit may be performed using, or without, AI, for example. For example, the voice notification unit can input the user's geographical location information to a generation AI and cause the generation AI to execute instructions to select optimal notification content.

[0107] The voice notification unit can analyze the user's social media activity and provide relevant notification content when providing a voice notification. The voice notification unit can provide notification content based on, for example, exercise data shared by the user on social media. The voice notification unit can also provide notification content based on information about fitness influencers the user follows on social media. Furthermore, the voice notification unit can analyze the user's social media posts and provide relevant notification content. This allows relevant notification content to be provided based on the user's social media activity. Some or all of the above-described processing in the voice notification unit can be performed using, or without, AI. For example, the voice notification unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to provide relevant notification content.

[0108] The data accumulation unit can estimate the user's emotions and adjust the frequency of data accumulation based on the estimated user emotions. For example, the data accumulation unit can periodically accumulate data when the user is relaxed. Furthermore, the data accumulation unit can frequently accumulate data when the user is in a hurry. Furthermore, the data accumulation unit can reduce the frequency of data accumulation when the user is stressed. This allows the frequency of data accumulation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data accumulation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data accumulation unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the frequency of data accumulation.

[0109] When accumulating data, the data accumulation unit can select the optimal data accumulation method by referring to the user's past running data. The data accumulation unit, for example, selects the optimal data accumulation method based on the user's past running data. The data accumulation unit can also select an efficient data accumulation method from the user's past running data. Furthermore, the data accumulation unit can analyze the user's past running data and select the most effective data accumulation method. This makes it possible to select the optimal data accumulation method based on the user's past running data. Some or all of the above-mentioned processing in the data accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the data accumulation unit can input the user's past running data into the generation AI and cause the generation AI to execute instructions to select the optimal data accumulation method.

[0110] The data accumulation unit can adjust the accuracy of the accumulated data based on the user's current exercise intensity when accumulating data. For example, the data accumulation unit can increase the accuracy of the accumulated data when the user is performing high-intensity exercise. Furthermore, the data accumulation unit can decrease the accuracy of the accumulated data when the user is performing low-intensity exercise. Furthermore, the data accumulation unit can adjust the accuracy of the accumulated data according to the user's exercise intensity. This allows the accuracy of the accumulated data to be adjusted based on the user's exercise intensity. Some or all of the above-described processing in the data accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the data accumulation unit can input the user's exercise intensity data to the generation AI and cause the generation AI to execute instructions to adjust the accuracy of the accumulated data.

[0111] The data storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user emotions. For example, if the user is nervous, the data storage unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the data storage unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data storage unit can provide a display method that focuses on the main points. This allows the display method of the stored data to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data storage unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the data storage unit can input the user's emotion data into the generation AI and cause the generation AI to execute instructions to adjust the display method of the stored data.

[0112] When storing data, the data storage unit can select the optimal data storage method by taking into account the user's geographical location information. For example, when the user is in a specific area, the data storage unit can prioritize storing data related to that area. Furthermore, when the user is traveling, the data storage unit can prioritize storing data related to the travel destination. Furthermore, when the user is at home, the data storage unit can also prioritize storing data related to daily life. This makes it possible to select the optimal data storage method based on the user's geographical location information. Some or all of the above-mentioned processing in the data storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the data storage unit can input the user's geographical location information to the generation AI and cause the generation AI to execute instructions to select the optimal data storage method.

[0113] The data accumulation unit can analyze the user's social media activities and accumulate related data when accumulating data. For example, the data accumulation unit accumulates data based on exercise data shared by the user on social media. The data accumulation unit can also accumulate data based on information about fitness influencers the user follows on social media. Furthermore, the data accumulation unit can analyze the content of the user's social media posts and accumulate related data. This allows related data to be accumulated based on the user's social media activities. Some or all of the above-described processing in the data accumulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the data accumulation unit can input the user's social media data into a generation AI and cause the generation AI to execute instructions to accumulate related data. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, location determination unit, heart rate monitoring unit, pace calculation unit, audio notification unit, and data accumulation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting user data. The location determination unit acquires the runner's current location using, for example, the GPS module of the smart device 14. The heart rate monitoring unit measures the runner's heart rate using, for example, a heart rate sensor of the smart device 14. The pace calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and calculates an appropriate pace based on the heart rate data. The audio notification unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies the runner of the calculated pace by audio. The data accumulation unit stores the runner's data using, for example, the database 24 of the data processing device 12 and proposes a running plan for the next run. === Hard Collateral 1-2 === Each of the multiple elements, including the registration unit, location determination unit, heart rate monitoring unit, pace calculation unit, audio notification unit, and data accumulation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting user data. The location determination unit acquires the runner's current location using, for example, the GPS module of the smart glasses 214. The heart rate monitoring unit measures the runner's heart rate using, for example, the heart rate sensor of the smart glasses 214. The pace calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates an appropriate pace based on the heart rate data. The audio notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the runner of the calculated pace by audio. The data accumulation unit stores the runner's data using, for example, the database 24 of the data processing device 12 and proposes a running plan for the next run. === Hard Collateral 1-3 === Each of the multiple elements, including the registration unit, position determination unit, heart rate monitoring unit, pace calculation unit, audio notification unit, and data storage unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for inputting user data. The position determination unit acquires the runner's current location using, for example, the GPS module of the headset terminal 314. The heart rate monitoring unit measures the runner's heart rate using, for example, the heart rate sensor of the headset terminal 314. The pace calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and calculates an appropriate pace based on the heart rate data. The audio notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 and notifies the runner of the calculated pace by audio. The data storage unit stores the runner's data using, for example, the database 24 of the data processing device 12 and proposes a running plan for the next run. === Hard Collateral 1-4 === Each of the multiple elements, including the registration unit, position determination unit, heart rate monitoring unit, pace calculation unit, audio notification unit, and data accumulation unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting user data. The position determination unit acquires the runner's current location using, for example, a GPS module of the robot 414. The heart rate monitoring unit measures the runner's heart rate using, for example, a heart rate sensor of the robot 414. The pace calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and calculates an appropriate pace based on the heart rate data. The audio notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies the runner of the calculated pace by audio. The data accumulation unit stores the runner's data using, for example, the database 24 of the data processing device 12 and proposes a running plan for the next run.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The AI ​​watch system can also include a form analysis unit that analyzes the user's running form. The form analysis unit can analyze the user's running form using a camera and sensors and notify the user of improvements via a voice notification unit. For example, the form analysis unit can analyze the user's foot strike position and knee angle and provide advice on maintaining proper form. The form analysis unit can also record changes in the user's running form over time and compare them with past data to identify areas for improvement. Furthermore, the form analysis unit can analyze the user's running form in real time and provide instant feedback. This allows the user to maintain an efficient running form and reduce the risk of injury.

[0116] The AI ​​watch system can also include a diet management unit that manages the user's dietary data. The diet management unit can record the contents of the user's meals and analyze their impact on running performance. For example, the diet management unit can record the user's calorie and nutrient intake and suggest appropriate meals before and after running. The diet management unit can also accumulate the user's dietary data and suggest dietary improvements based on past data. Furthermore, the diet management unit can link the user's dietary data with running data and analyze the relationship between diet and running performance. This allows the user to optimize the balance between diet and running and improve performance.

[0117] The AI ​​watch system can also be equipped with a sleep management unit that manages the user's sleep data. The sleep management unit can record the user's sleep patterns and analyze their impact on running performance. For example, the sleep management unit can record the user's sleep duration and quality and suggest appropriate sleep durations. The sleep management unit can also accumulate the user's sleep data and suggest areas for improvement based on past data. Furthermore, the sleep management unit can link the user's sleep data with running data and analyze the relationship between sleep and running performance. This allows the user to optimize the balance between sleep and running and improve their performance.

[0118] The AI ​​watch system can also be equipped with a stress management unit that manages the user's stress level. The stress management unit can monitor the user's stress level and analyze its impact on running performance. For example, the stress management unit can measure the user's heart rate variability and electrodermal response to evaluate the stress level. The stress management unit can also accumulate the user's stress data and suggest ways to improve stress based on past data. Furthermore, the stress management unit can link the user's stress data with running data and analyze the relationship between stress and running performance. This allows the user to optimize the balance between stress and running and improve their performance.

[0119] The AI ​​watch system can also be equipped with a hydration management unit that manages the user's hydration. The hydration management unit can record the user's hydration intake and analyze its impact on running performance. For example, the hydration management unit can record the amount of water the user has consumed and suggest appropriate hydration. The hydration management unit can also accumulate the user's hydration data and suggest improvements to hydration based on past data. Furthermore, the hydration management unit can link the user's hydration data with running data to analyze the relationship between hydration and running performance. This allows the user to optimize the balance between hydration and running and improve their performance.

[0120] The AI ​​watch system can also estimate the user's emotions and adjust the running plan based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a running plan at a relaxing pace. If the user is relaxed, it can suggest a challenging running plan. Furthermore, if the user is in a hurry, it can suggest a short but effective running plan. This allows it to provide the optimal running plan based on the user's emotions.

[0121] The AI ​​watch system can also estimate the user's emotions and select music based on the estimated emotions. For example, if the user is relaxed, it can play relaxing music. If the user is feeling energetic, it can play upbeat music. Furthermore, if the user is feeling stressed, it can play relaxing music. In this way, it can provide the optimal music based on the user's emotions.

[0122] The AI ​​watch system can also estimate the user's emotions and tailor messages during a run based on the estimated emotions. For example, if the user is feeling stressed, it can display an encouraging message. If the user is feeling relaxed, it can display a positive message. If the user is in a hurry, it can display a concise message. This allows it to provide the most appropriate message based on the user's emotions.

[0123] The AI ​​watch system can also estimate the user's emotions and suggest post-run recovery plans based on the estimated emotions. For example, if the user is tired, it can suggest relaxing stretches or massages. If the user is feeling energetic, it can suggest light exercise. Furthermore, if the user is feeling stressed, it can suggest a relaxing recovery plan. This allows it to provide the optimal recovery plan based on the user's emotions.

[0124] The AI ​​watch system can also estimate the user's emotions and adjust the notification content during a run based on the estimated emotions. For example, if the user is relaxed, the notification content can be simplified. If the user is in a hurry, only important notifications can be displayed. Furthermore, if the user is stressed, the system can provide relaxing notification content. This allows the system to provide optimal notification content based on the user's emotions.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The registration unit registers the user's data. The user's data includes gender, height, weight, blood information, etc. The registration unit provides an interface for the user to input this data and stores the input data in the AI ​​watch. Step 2: The location determination unit works in conjunction with the GPS to determine the runner's current location in real time. The location determination unit uses a GPS module to obtain the runner's current location and has the ability to measure the distance and time traveled. Step 3: The heart rate monitoring unit monitors the runner's heart rate. The heart rate monitoring unit uses a heart rate sensor to measure the runner's heart rate and display it in real time. It also stores heart rate data and allows users to refer to past data. Step 4: The pace calculator calculates the appropriate pace based on the runner's heart rate. The pace calculator takes heart rate data as input and has an algorithm that calculates the appropriate pace. If the heart rate is too high, the pace should be slowed down; if the heart rate is too low, the pace should be increased. Step 5: The voice notification unit announces the calculated pace by voice. The voice notification unit can announce instructions such as "Please slow down your pace" or "Please speed up your pace" by using voice synthesis technology.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a registration unit for registering user data; a location determining unit that determines a current location in cooperation with a GPS based on the data registered by the registration unit; a heart rate monitoring unit that monitors a heart rate based on the current location determined by the location determination unit; a pace calculation unit that calculates a pace based on the heart rate monitored by the heart rate monitoring unit; a voice notification unit that notifies the pace calculated by the pace calculation unit by voice; A system characterized by:

2. Equipped with a data storage unit 2. The system of claim 1.

3. The registration unit Register data including gender, height, weight, and blood information 2. The system of claim 1.

4. The position grasping unit Linking with GPS to track runners' current locations in real time 2. The system of claim 1.

5. The heart rate monitoring unit Monitoring runners' heart rates 2. The system of claim 1.

6. The pace calculation unit Calculate pace based on the runner's heart rate 2. The system of claim 1.

7. The voice notification unit Calculated pace announced by voice 2. The system of claim 1.

8. The data storage unit Accumulates runner data and suggests next running plans based on past running data 3. The system of claim 2.

9. The registration unit Estimate the user's emotions and adjust the timing of data registration based on the estimated user emotions.

2. The system of claim 1.

Images