system

Abstract

The system according to the embodiment aims to enable amateur runners to find the best training course and receive appropriate support during training. [Solution] A system according to an embodiment includes an input unit, a suggestion unit, a notification unit, and an analysis unit. The input unit inputs the type of training or target time, distance, and location. The suggestion unit suggests a course based on the information input by the input unit. The notification unit provides audio notifications during training based on the course suggested by the suggestion unit. The analysis unit provides data analysis and advice after training based on the information notified by the notification unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for amateur runners to find the best training course or receive appropriate support during training.

[0005] The system according to the embodiment aims to enable amateur runners to find the best training course and receive appropriate support during training. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a suggestion unit, a notification unit, and an analysis unit. The input unit inputs the type of training or the target time, distance, and location. The suggestion unit suggests a course based on the information input by the input unit. The notification unit provides audio notification during training based on the course suggested by the suggestion unit. The analysis unit provides data analysis and advice after training based on the information notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment allows amateur runners to find the best training course and receive appropriate support during training. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training support system according to an embodiment of the present invention is designed for amateur runners. This training support system allows runners to register the type of training they will be doing, their target time, distance, and location in advance, and then AI suggests optimal courses and start / finish points. During training, the device supports runners by providing a voice notification of their running distance and pace based on the provided information. This eliminates the need to check their smartwatch each time, allowing them to focus on their training. For example, runners input their training type (e.g., jogging, interval training, etc.), target time, running distance, and training location into the system. The AI ​​then analyzes this information and suggests optimal courses and start / finish points. For example, it can set a pace based on the target time and a course based on the running distance. During training, the device measures the runner's running distance and pace in real time and notifies them via voice. For example, it could say, "Your current running distance is 5 kilometers. Your pace is 5 minutes 30 seconds per kilometer." This eliminates the need for runners to check their smartwatch, allowing them to focus on their training. Furthermore, after training, the AI ​​analyzes the runner's running data and provides advice for their next training session. For example, specific advice such as "Try to increase your pace a little next time" can be given. This system allows amateur runners to train efficiently and receive support to achieve their goals. In this way, the training support system can efficiently support runners' training and help them achieve their goals.

[0029] A training support system according to an embodiment includes an input unit, a suggestion unit, a notification unit, and an analysis unit. A runner inputs a training type, target time, distance, and location into the input unit. For example, the runner can select a training type such as jogging or interval training and input a target time, running distance, and training location. The suggestion unit uses AI to suggest an optimal course based on the information input by the input unit. For example, the suggestion unit distributes a pace according to the target time and sets a course according to the running distance. The suggestion unit can analyze the runner's input information using an AI algorithm and suggest an optimal course. The notification unit provides audio notifications during training based on the course suggested by the suggestion unit. For example, the notification unit measures the runner's running distance and pace in real time and provides audio notifications. For example, the notification unit provides audio notifications such as, "Your current running distance is 5 kilometers. Your pace is 5 minutes 30 seconds per kilometer." The analysis unit analyzes data and provides advice after training based on the information provided by the notification unit. For example, the analysis unit analyzes the runner's running data and provides advice for the next training session. The analysis unit provides specific advice such as, for example, "Try to increase your pace a little next time." In this way, the training support system according to the embodiment can efficiently support the runner's training and help him or her achieve his or her goal.

[0030] The suggestion unit can be equipped with an algorithm that suggests a course using AI. The suggestion unit uses an AI algorithm to analyze the runner's input information and suggest an optimal course. For example, the suggestion unit may distribute the runner's pace according to the target time and set the course according to the running distance. The suggestion unit can use an AI algorithm to analyze the runner's input information and suggest an optimal course. In this way, the use of AI improves the accuracy of the optimal course suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the runner's input information into AI and have the AI ​​execute an algorithm that suggests an optimal course.

[0031] The notification unit can provide real-time audio notification of the runner's distance or pace during training. The notification unit measures the runner's distance and pace during training in real time and provides audio notification. For example, the notification unit provides audio notification such as, "Current distance traveled is 5 kilometers. Pace is 5 minutes 30 seconds per kilometer." The notification unit enables the runner to concentrate on training by providing real-time audio notification. This allows the runner to concentrate on training by providing real-time audio notification. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the runner's distance and pace into AI and have the AI ​​execute an algorithm for providing audio notification.

[0032] The analysis unit can analyze the running data after training and provide advice for the next training session. The analysis unit can analyze the runner's running data after training and provide advice for the next training session. For example, the analysis unit can provide specific advice such as, "Try to increase your pace a little next time." The analysis unit can analyze the runner's running data after training and provide advice for the next training session. This allows specific advice for the next training session to be obtained. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the runner's running data into AI and cause the AI ​​to execute an algorithm that provides advice for the next training session.

[0033] The input unit can automatically suggest the optimal practice type and target time for the user based on past training data. The input unit references past training data and automatically suggests the optimal practice type and target time for the user. For example, the input unit suggests the optimal practice type based on data of training the user has performed in the past. The input unit can also automatically set a target time from the user's past training data. The input unit can also analyze the user's past training data and suggest the optimal practice type and target time. This optimizes the user's training through suggestions based on past data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm that suggests the optimal practice type and target time.

[0034] The input unit can monitor the user's physical condition in real time and adjust the input content based on that. The input unit can monitor the user's physical condition (e.g., heart rate and fatigue level) in real time and adjust the input content based on that. For example, the input unit can monitor the user's heart rate in real time and suggest an appropriate type of training. The input unit can also monitor the user's fatigue level in real time and set a reasonable target time. The input unit can also monitor the user's physical condition in real time and suggest an optimal type of training and target time. This enables reasonable training by adjusting the input content based on the real-time physical condition. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's physical condition data into AI and cause the AI ​​to execute an algorithm that adjusts the input content.

[0035] The input unit can suggest local training events or courses related to the input content by taking into account the user's geographical location information. The input unit can suggest local training events or courses related to the input content by taking into account the user's geographical location information. For example, the input unit can suggest training events held nearby based on the user's current location. The input unit can also suggest optimal training courses based on the user's geographical location information. The input unit can also suggest local training events or courses based on the user's geographical location information. In this way, optimal training events or courses are provided to the user through suggestions based on the geographical location information. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's geographical location information to AI and cause the AI ​​to execute an algorithm that suggests local training events or courses.

[0036] The input unit can analyze the user's social media activity and automatically input related training information. The input unit analyzes the user's social media activity and automatically inputs related training information. For example, the input unit analyzes the user's social media activity and automatically inputs related training information. The input unit can also suggest optimal training information based on the user's social media activity. The input unit can also analyze the user's social media activity and automatically input related training information. This makes input work more efficient through automatic input based on social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that automatically inputs related training information.

[0037] The suggestion unit can customize the optimal course by referring to the user's past training data when making a suggestion. The suggestion unit customizes the optimal course by referring to the user's past training data when making a suggestion. For example, the suggestion unit customizes the optimal course based on the user's past training data. The suggestion unit can also suggest the optimal course from the user's past training data. The suggestion unit can also analyze the user's past training data and customize the optimal course. In this way, the optimal course is suggested to the user through customization based on the past training data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm to customize the optimal course.

[0038] The suggestion unit can optimize the route based on external data such as weather or traffic conditions when proposing the route. The suggestion unit optimizes the route taking into consideration external data such as weather and traffic conditions when proposing the route. For example, the suggestion unit proposes an optimal route based on weather information. The suggestion unit can also propose an optimal route based on traffic conditions. The suggestion unit can also propose an optimal route based on external data such as weather and traffic conditions. In this way, an optimal route is proposed to the user through optimization based on external data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input weather and traffic condition data into AI and cause the AI ​​to execute an algorithm that proposes an optimal route.

[0039] The suggestion unit, when making a suggestion, can prioritize local training courses by taking into account the user's geographical location information. The suggestion unit, when making a suggestion, prioritizes local training courses by taking into account the user's geographical location information. For example, the suggestion unit suggests an optimal training course based on the user's current location. The suggestion unit can also prioritize local training courses by taking into account the user's geographical location information. The suggestion unit can also suggest an optimal training course based on the user's geographical location information. In this way, the optimal local training course is provided to the user through suggestions based on the geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the user's geographical location information into AI and cause the AI ​​to execute an algorithm that prioritizes local training courses.

[0040] The suggestion unit can analyze the user's social media activity and suggest a related training course when making a suggestion. The suggestion unit can analyze the user's social media activity and suggest a related training course when making a suggestion. For example, the suggestion unit can analyze the user's social media activity and suggest a related training course. The suggestion unit can also suggest an optimal training course based on the user's social media activity. The suggestion unit can also analyze the user's social media activity and suggest a related training course. In this way, the optimal training course is provided to the user through suggestions based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that suggests a related training course.

[0041] The notification unit can adjust the notification content taking into account the user's real-time physical condition when providing a notification. The notification unit can adjust the notification content taking into account the user's real-time physical condition (e.g., heart rate and fatigue level) when providing a notification. For example, the notification unit can monitor the user's heart rate in real time and provide an appropriate voice notification. The notification unit can also monitor the user's fatigue level in real time and notify the user of a comfortable pace. The notification unit can also monitor the user's physical condition in real time and provide an optimal voice notification. This enables comfortable training by adjusting the notification content based on the real-time physical condition. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's physical condition data into AI and cause the AI ​​to execute an algorithm that adjusts the notification content.

[0042] The notification unit can provide notification at the optimal timing by referring to the user's past training data when providing notification. The notification unit can provide notification at the optimal timing by referring to the user's past training data when providing notification. For example, the notification unit provides audio notification at the optimal timing based on the user's past training data. The notification unit can also provide notification at the optimal timing based on the user's past training data. The notification unit can also analyze the user's past training data and provide audio notification at the optimal timing. This makes the user's training more efficient by providing notifications at the optimal timing based on the past training data. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm that provides notifications at the optimal timing.

[0043] The notification unit can provide a notification including local information by taking into account the user's geographical location information when providing a notification. The notification unit can provide a notification including local information by taking into account the user's geographical location information when providing a notification. For example, the notification unit can provide local training information based on the user's current location. The notification unit can also provide optimal training information based on the user's geographical location information. The notification unit can also provide a notification including local information based on the user's geographical location information. In this way, optimal local information is provided to the user through notifications based on the geographical location information. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's geographical location information into AI and cause the AI ​​to execute an algorithm that provides a notification including local information.

[0044] The notification unit can analyze the user's social media activity and notify the user of related information at the time of notification. The notification unit can analyze the user's social media activity and notify the user of related information at the time of notification. For example, the notification unit can analyze the user's social media activity and notify the user of related training information. The notification unit can also notify the user of optimal training information based on the user's social media activity. The notification unit can also analyze the user's social media activity and notify the user of related information. In this way, optimal information is provided to the user through notifications based on social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that notifies the user of related information.

[0045] The analysis unit can optimize the analysis algorithm by referring to the user's past training data during analysis. The analysis unit can optimize the analysis algorithm by referring to the user's past training data during analysis. For example, the analysis unit optimizes the analysis algorithm based on the user's past training data. The analysis unit can also propose an optimal analysis algorithm from the user's past training data. The analysis unit can also analyze the user's past training data and optimize the optimal analysis algorithm. This improves the accuracy of the analysis results through optimization based on the past training data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm that optimizes the analysis algorithm.

[0046] The analysis unit can correct the analysis results by taking external data into consideration during analysis. The analysis unit corrects the analysis results by taking external data (e.g., weather and traffic conditions) into consideration during analysis. For example, the analysis unit corrects the training results based on weather data. The analysis unit can also correct the training results based on traffic condition data. The analysis unit can also correct the analysis results based on external data such as weather and traffic conditions. This improves the accuracy of the analysis results through corrections based on external data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input weather and traffic condition data into AI and have the AI ​​execute an algorithm to correct the analysis results.

[0047] During analysis, the analysis unit can prioritize analyzing local training data taking into account the user's geographical location information. During analysis, the analysis unit prioritizes analyzing local training data taking into account the user's geographical location information. For example, the analysis unit prioritizes analyzing local training data based on the user's current location. The analysis unit can also analyze optimal training data based on the user's geographical location information. The analysis unit can also prioritize analyzing local training data based on the user's geographical location information. In this way, optimal training data is provided to the user through analysis based on the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to AI and cause the AI ​​to execute an algorithm that prioritizes analyzing local training data.

[0048] The analysis unit can analyze the user's social media activity during the analysis and reflect the related data in the analysis. The analysis unit can analyze the user's social media activity during the analysis and reflect the related data in the analysis. For example, the analysis unit can analyze the user's social media activity and reflect the related training data in the analysis. The analysis unit can also analyze optimal training data based on the user's social media activity. The analysis unit can also analyze the user's social media activity and reflect the related data in the analysis. This allows the analysis based on the social media activity to provide the user with optimal analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that reflects the related data in the analysis.

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

[0050] The suggestion unit can refer to the user's past training data and learn the user's training patterns. For example, the suggestion unit can analyze the frequency and intensity of the user's past training and suggest an optimal training plan. The suggestion unit can also evaluate the user's training progress based on the user's past training data and adjust the training plan as needed. Furthermore, the suggestion unit can use the user's past training data to predict the effectiveness of training and provide feedback to the user. This maximizes the effectiveness of training through suggestions based on the user's past training data.

[0051] The suggestion unit can optimize the training plan by referring to the user's dietary data. For example, the suggestion unit can analyze the calories and nutrients ingested by the user and adjust the intensity and content of the training. The suggestion unit can also provide advice on meals before and after training based on the user's dietary data. Furthermore, the suggestion unit can also use the user's dietary data to suggest a meal plan to maximize the effectiveness of the training. This improves the effectiveness of the training by optimizing the training plan based on the user's dietary data.

[0052] The suggestion unit can evaluate the safety of a training course by taking into account the user's geographical location information. For example, the suggestion unit can suggest a course with low traffic volume based on the user's current location. The suggestion unit can also prioritize suggesting areas with low crime rates based on the user's geographical location information. Furthermore, the suggestion unit can also suggest a safe course by taking into account weather and terrain information based on the user's geographical location information. In this way, the optimal training course can be provided to the user by evaluating safety based on the geographical location information.

[0053] The notification unit can refer to the user's past training data and adjust the notification content according to the user's training progress. For example, if the user is approaching a goal, the notification unit can notify the user with an encouraging message. Alternatively, if the user is moving away from the goal, the notification unit can notify the user of areas for improvement. Furthermore, the notification unit can provide advice according to the user's training progress based on the user's past training data. This allows the user's training to be more efficient by adjusting the notification content based on the user's past training data.

[0054] The analysis unit can evaluate the effectiveness of training by referring to the user's dietary data. For example, the analysis unit can analyze the effectiveness of training based on the calories and nutrients ingested by the user. The analysis unit can also provide advice to maximize the effectiveness of training based on the user's dietary data. Furthermore, the analysis unit can use the user's dietary data to predict the effectiveness of training and provide feedback to the user. This improves the effectiveness of training by evaluating the training effect based on the user's dietary data.

[0055] The input unit can analyze the user's social media activity and automatically input related training information. For example, the input unit can automatically generate a training plan based on training data shared by the user on social media. The input unit can also automatically set training goals based on the user's social media activity. Furthermore, the input unit can analyze the user's social media activity and automatically input related training information. This makes input work more efficient through automatic input based on social media activity.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: In the input section, the runner inputs the type of training, target time, distance, and location. For example, the runner can select the type of training, such as jogging or interval training, and input the target time, running distance, and training location. Step 2: The suggestion unit uses AI to suggest the optimal course based on the information input by the input unit. For example, the suggestion unit may allocate a pace to match the target time or set a course according to the distance traveled. The suggestion unit can use an AI algorithm to analyze the runner's input information and suggest the optimal course. Step 3: The notification unit provides audio notifications during training based on the course proposed by the suggestion unit. For example, the notification unit measures the runner's running distance and pace in real time and provides audio notifications. For example, the notification unit provides audio notifications such as, "Your current running distance is 5 kilometers. Your pace is 5 minutes 30 seconds per kilometer." Step 4: The analysis unit analyzes the data and provides advice after the training based on the information notified by the notification unit. For example, the analysis unit analyzes the runner's running data and provides advice for the next training session. For example, the analysis unit provides specific advice such as, "Try to increase your pace a little next time."

[0058] (Example 2) A training support system according to an embodiment of the present invention is designed for amateur runners. This training support system allows runners to register the type of training they will be doing, their target time, distance, and location in advance, and then AI suggests optimal courses and start / finish points. During training, the device supports runners by providing a voice notification of their running distance and pace based on the provided information. This eliminates the need to check their smartwatch each time, allowing them to focus on their training. For example, runners input their training type (e.g., jogging, interval training, etc.), target time, running distance, and training location into the system. The AI ​​then analyzes this information and suggests optimal courses and start / finish points. For example, it can set a pace based on the target time and a course based on the running distance. During training, the device measures the runner's running distance and pace in real time and notifies them via voice. For example, it could say, "Your current running distance is 5 kilometers. Your pace is 5 minutes 30 seconds per kilometer." This eliminates the need for runners to check their smartwatch, allowing them to focus on their training. Furthermore, after training, the AI ​​analyzes the runner's running data and provides advice for their next training session. For example, specific advice such as "Try to increase your pace a little next time" can be given. This system allows amateur runners to train efficiently and receive support to achieve their goals. In this way, the training support system can efficiently support runners' training and help them achieve their goals.

[0059] A training support system according to an embodiment includes an input unit, a suggestion unit, a notification unit, and an analysis unit. A runner inputs a training type, target time, distance, and location into the input unit. For example, the runner can select a training type such as jogging or interval training and input a target time, running distance, and training location. The suggestion unit uses AI to suggest an optimal course based on the information input by the input unit. For example, the suggestion unit distributes a pace according to the target time and sets a course according to the running distance. The suggestion unit can analyze the runner's input information using an AI algorithm and suggest an optimal course. The notification unit provides audio notifications during training based on the course suggested by the suggestion unit. For example, the notification unit measures the runner's running distance and pace in real time and provides audio notifications. For example, the notification unit provides audio notifications such as, "Your current running distance is 5 kilometers. Your pace is 5 minutes 30 seconds per kilometer." The analysis unit analyzes data and provides advice after training based on the information provided by the notification unit. For example, the analysis unit analyzes the runner's running data and provides advice for the next training session. The analysis unit provides specific advice such as, for example, "Try to increase your pace a little next time." In this way, the training support system according to the embodiment can efficiently support the runner's training and help him or her achieve his or her goal.

[0060] The suggestion unit can be equipped with an algorithm that suggests a course using AI. The suggestion unit uses an AI algorithm to analyze the runner's input information and suggest an optimal course. For example, the suggestion unit may distribute the runner's pace according to the target time and set the course according to the running distance. The suggestion unit can use an AI algorithm to analyze the runner's input information and suggest an optimal course. In this way, the use of AI improves the accuracy of the optimal course suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the runner's input information into AI and have the AI ​​execute an algorithm that suggests an optimal course.

[0061] The notification unit can provide real-time audio notification of the runner's distance or pace during training. The notification unit measures the runner's distance and pace during training in real time and provides audio notification. For example, the notification unit provides audio notification such as, "Current distance traveled is 5 kilometers. Pace is 5 minutes 30 seconds per kilometer." The notification unit enables the runner to concentrate on training by providing real-time audio notification. This allows the runner to concentrate on training by providing real-time audio notification. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the runner's distance and pace into AI and have the AI ​​execute an algorithm for providing audio notification.

[0062] The analysis unit can analyze the running data after training and provide advice for the next training session. The analysis unit can analyze the runner's running data after training and provide advice for the next training session. For example, the analysis unit can provide specific advice such as, "Try to increase your pace a little next time." The analysis unit can analyze the runner's running data after training and provide advice for the next training session. This allows specific advice for the next training session to be obtained. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the runner's running data into AI and cause the AI ​​to execute an algorithm that provides advice for the next training session.

[0063] The input unit can estimate a user's emotions and adjust the design of the input interface based on the estimated user emotions. The input unit can estimate a user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, a subdued interface can be provided to reduce visual stress. Alternatively, if the user is having fun, a bright interface can be provided to make inputting more enjoyable. Alternatively, if the user is tired, a simple, highly visible interface can be provided to make inputting easier. This allows for a more comfortable inputting experience through an interface design tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI 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 input unit can be performed using, for example, an AI, or without an AI. For example, the input unit can input user emotion data into an AI and cause the AI ​​to execute an algorithm that adjusts the design of the input interface.

[0064] The input unit can automatically suggest the optimal practice type and target time for the user based on past training data. The input unit references past training data and automatically suggests the optimal practice type and target time for the user. For example, the input unit suggests the optimal practice type based on data of training the user has performed in the past. The input unit can also automatically set a target time from the user's past training data. The input unit can also analyze the user's past training data and suggest the optimal practice type and target time. This optimizes the user's training through suggestions based on past data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm that suggests the optimal practice type and target time.

[0065] The input unit can monitor the user's physical condition in real time and adjust the input content based on that. The input unit can monitor the user's physical condition (e.g., heart rate and fatigue level) in real time and adjust the input content based on that. For example, the input unit can monitor the user's heart rate in real time and suggest an appropriate type of training. The input unit can also monitor the user's fatigue level in real time and set a reasonable target time. The input unit can also monitor the user's physical condition in real time and suggest an optimal type of training and target time. This enables reasonable training by adjusting the input content based on the real-time physical condition. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's physical condition data into AI and cause the AI ​​to execute an algorithm that adjusts the input content.

[0066] The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is stressed, the input unit can prioritize displaying the most important input items. The input unit can also provide detailed input options when the user is relaxed. When the user is in a hurry, the input unit can prioritize displaying the most important input items to enable quick input. This enables efficient input by prioritizing input content according to 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 input unit can be performed using, for example, an AI. For example, the input unit can input user emotion data into an AI and cause the AI ​​to execute an algorithm for prioritizing input content.

[0067] The input unit can suggest local training events or courses related to the input content by taking into account the user's geographical location information. The input unit can suggest local training events or courses related to the input content by taking into account the user's geographical location information. For example, the input unit can suggest training events held nearby based on the user's current location. The input unit can also suggest optimal training courses based on the user's geographical location information. The input unit can also suggest local training events or courses based on the user's geographical location information. In this way, optimal training events or courses are provided to the user through suggestions based on the geographical location information. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's geographical location information to AI and cause the AI ​​to execute an algorithm that suggests local training events or courses.

[0068] The input unit can analyze the user's social media activity and automatically input related training information. The input unit analyzes the user's social media activity and automatically inputs related training information. For example, the input unit analyzes the user's social media activity and automatically inputs related training information. The input unit can also suggest optimal training information based on the user's social media activity. The input unit can also analyze the user's social media activity and automatically input related training information. This makes input work more efficient through automatic input based on social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that automatically inputs related training information.

[0069] The suggestion unit can estimate the user's emotions and adjust the way the suggestion content is presented based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the way the suggestion content is presented based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually stimulating suggestions when the user is excited. This makes the suggestions more effective by presenting the suggestions in a way that suits 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 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 suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input user emotion data into an AI and cause the AI ​​to execute an algorithm that adjusts the way the suggestion content is presented.

[0070] The suggestion unit can customize the optimal course by referring to the user's past training data when making a suggestion. The suggestion unit customizes the optimal course by referring to the user's past training data when making a suggestion. For example, the suggestion unit customizes the optimal course based on the user's past training data. The suggestion unit can also suggest the optimal course from the user's past training data. The suggestion unit can also analyze the user's past training data and customize the optimal course. In this way, the optimal course is suggested to the user through customization based on the past training data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm to customize the optimal course.

[0071] The suggestion unit can optimize the route based on external data such as weather or traffic conditions when proposing the route. The suggestion unit optimizes the route taking into consideration external data such as weather and traffic conditions when proposing the route. For example, the suggestion unit proposes an optimal route based on weather information. The suggestion unit can also propose an optimal route based on traffic conditions. The suggestion unit can also propose an optimal route based on external data such as weather and traffic conditions. In this way, an optimal route is proposed to the user through optimization based on external data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input weather and traffic condition data into AI and cause the AI ​​to execute an algorithm that proposes an optimal route.

[0072] The suggestion unit can estimate the user's emotions and prioritize the suggested content based on the estimated user emotions. The suggestion unit can estimate the user's emotions and prioritize the suggested content based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize displaying the most important suggested content. The suggestion unit can also provide detailed suggested content when the user is relaxed. If the user is in a hurry, the suggestion unit can prioritize displaying the most important suggested content, allowing for quick suggestions. This makes the suggestions more effective by prioritizing them according to 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 suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input user emotion data into an AI and cause the AI ​​to execute an algorithm for prioritizing suggested content.

[0073] The suggestion unit, when making a suggestion, can prioritize local training courses by taking into account the user's geographical location information. The suggestion unit, when making a suggestion, prioritizes local training courses by taking into account the user's geographical location information. For example, the suggestion unit suggests an optimal training course based on the user's current location. The suggestion unit can also prioritize local training courses by taking into account the user's geographical location information. The suggestion unit can also suggest an optimal training course based on the user's geographical location information. In this way, the optimal local training course is provided to the user through suggestions based on the geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the user's geographical location information into AI and cause the AI ​​to execute an algorithm that prioritizes local training courses.

[0074] The suggestion unit can analyze the user's social media activity and suggest a related training course when making a suggestion. The suggestion unit can analyze the user's social media activity and suggest a related training course when making a suggestion. For example, the suggestion unit can analyze the user's social media activity and suggest a related training course. The suggestion unit can also suggest an optimal training course based on the user's social media activity. The suggestion unit can also analyze the user's social media activity and suggest a related training course. In this way, the optimal training course is provided to the user through suggestions based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that suggests a related training course.

[0075] The notification unit can estimate the user's emotions and adjust the tone and content of the voice notification based on the estimated user's emotions. The notification unit can estimate the user's emotions and adjust the tone and content of the voice notification based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide a voice notification in a calm voice. If the user is relaxed, the notification unit can provide a voice notification in a cheerful voice. If the user is in a hurry, the notification unit can provide a quick and concise voice notification. This makes the voice notification more effective by adapting the voice notification to 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 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 notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into an AI and cause the AI ​​to execute an algorithm that adjusts the tone and content of the voice notification.

[0076] The notification unit can adjust the notification content taking into account the user's real-time physical condition when providing a notification. The notification unit can adjust the notification content taking into account the user's real-time physical condition (e.g., heart rate and fatigue level) when providing a notification. For example, the notification unit can monitor the user's heart rate in real time and provide an appropriate voice notification. The notification unit can also monitor the user's fatigue level in real time and notify the user of a comfortable pace. The notification unit can also monitor the user's physical condition in real time and provide an optimal voice notification. This enables comfortable training by adjusting the notification content based on the real-time physical condition. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's physical condition data into AI and cause the AI ​​to execute an algorithm that adjusts the notification content.

[0077] The notification unit can provide notification at the optimal timing by referring to the user's past training data when providing notification. The notification unit can provide notification at the optimal timing by referring to the user's past training data when providing notification. For example, the notification unit provides audio notification at the optimal timing based on the user's past training data. The notification unit can also provide notification at the optimal timing based on the user's past training data. The notification unit can also analyze the user's past training data and provide audio notification at the optimal timing. This makes the user's training more efficient by providing notifications at the optimal timing based on the past training data. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm that provides notifications at the optimal timing.

[0078] The notification unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated user emotions. The notification unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated user emotions. For example, the notification unit can reduce the frequency of notifications when the user is stressed. The notification unit can also increase the frequency of notifications when the user is relaxed. The notification unit can also only notify important messages when the user is in a hurry. This makes notifications more effective by adjusting the notification frequency according to 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 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 notification unit can be performed using an AI, for example, or without an AI. For example, the notification unit can input user emotion data into an AI and have the AI ​​execute an algorithm to adjust the frequency of notifications.

[0079] The notification unit can provide a notification including local information by taking into account the user's geographical location information when providing a notification. The notification unit can provide a notification including local information by taking into account the user's geographical location information when providing a notification. For example, the notification unit can provide local training information based on the user's current location. The notification unit can also provide optimal training information based on the user's geographical location information. The notification unit can also provide a notification including local information based on the user's geographical location information. In this way, optimal local information is provided to the user through notifications based on the geographical location information. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's geographical location information into AI and cause the AI ​​to execute an algorithm that provides a notification including local information.

[0080] The notification unit can analyze the user's social media activity and notify the user of related information at the time of notification. The notification unit can analyze the user's social media activity and notify the user of related information at the time of notification. For example, the notification unit can analyze the user's social media activity and notify the user of related training information. The notification unit can also notify the user of optimal training information based on the user's social media activity. The notification unit can also analyze the user's social media activity and notify the user of related information. In this way, optimal information is provided to the user through notifications based on social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that notifies the user of related information.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis results to be communicated more effectively using a display method that suits 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 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and cause the AI ​​to execute an algorithm that adjusts the display method of the analysis results.

[0082] The analysis unit can optimize the analysis algorithm by referring to the user's past training data during analysis. The analysis unit can optimize the analysis algorithm by referring to the user's past training data during analysis. For example, the analysis unit optimizes the analysis algorithm based on the user's past training data. The analysis unit can also propose an optimal analysis algorithm from the user's past training data. The analysis unit can also analyze the user's past training data and optimize the optimal analysis algorithm. This improves the accuracy of the analysis results through optimization based on the past training data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past training data into AI and cause the AI ​​to execute an algorithm that optimizes the analysis algorithm.

[0083] The analysis unit can correct the analysis results by taking external data into consideration during analysis. The analysis unit corrects the analysis results by taking external data (e.g., weather and traffic conditions) into consideration during analysis. For example, the analysis unit corrects the training results based on weather data. The analysis unit can also correct the training results based on traffic condition data. The analysis unit can also correct the analysis results based on external data such as weather and traffic conditions. This improves the accuracy of the analysis results through corrections based on external data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input weather and traffic condition data into AI and have the AI ​​execute an algorithm to correct the analysis results.

[0084] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying the most important analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. If the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results to enable quick analysis. This allows the analysis results to be communicated more effectively by prioritizing them according to 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 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 analysis unit can be performed using, for example, an AI. For example, the analysis unit can input user emotion data into an AI and cause the AI ​​to execute an algorithm for prioritizing analysis results.

[0085] During analysis, the analysis unit can prioritize analyzing local training data taking into account the user's geographical location information. During analysis, the analysis unit prioritizes analyzing local training data taking into account the user's geographical location information. For example, the analysis unit prioritizes analyzing local training data based on the user's current location. The analysis unit can also analyze optimal training data based on the user's geographical location information. The analysis unit can also prioritize analyzing local training data based on the user's geographical location information. In this way, optimal training data is provided to the user through analysis based on the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to AI and cause the AI ​​to execute an algorithm that prioritizes analyzing local training data.

[0086] The analysis unit can analyze the user's social media activity during the analysis and reflect the related data in the analysis. The analysis unit can analyze the user's social media activity during the analysis and reflect the related data in the analysis. For example, the analysis unit can analyze the user's social media activity and reflect the related training data in the analysis. The analysis unit can also analyze optimal training data based on the user's social media activity. The analysis unit can also analyze the user's social media activity and reflect the related data in the analysis. This allows the analysis based on the social media activity to provide the user with optimal analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI and cause the AI ​​to execute an algorithm that reflects the related data in the analysis. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, suggestion unit, notification unit, and analysis unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and the runner inputs the type of training, target time, distance, and location. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal course using AI. The notification unit is realized, for example, by the output device 40 of the smart device 14, and provides audio notification during training. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides data analysis and advice after training. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, suggestion unit, notification unit, and analysis unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, through which the runner inputs the type of training, target time, distance, and location. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal course using AI. The notification unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides audio notifications during training. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides data analysis and advice after training. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, suggestion unit, notification unit, and analysis unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, through which the runner inputs the type of training, target time, distance, and location. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal course using AI. The notification unit is realized, for example, by the speaker 240 of the headset-type terminal 314, and provides audio notifications during training. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides data analysis and advice after training. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, suggestion unit, notification unit, and analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, through which the runner inputs the type of training, target time, distance, and location. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the optimal course using AI. The notification unit is realized, for example, by the speaker 240 of the robot 414, and provides audio notifications during training. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides data analysis and advice after training.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The suggestion unit can refer to the user's past training data and learn the user's training patterns. For example, the suggestion unit can analyze the frequency and intensity of the user's past training and suggest an optimal training plan. The suggestion unit can also evaluate the user's training progress based on the user's past training data and adjust the training plan as needed. Furthermore, the suggestion unit can use the user's past training data to predict the effectiveness of training and provide feedback to the user. This maximizes the effectiveness of training through suggestions based on the user's past training data.

[0089] The suggestion unit can optimize the training plan by referring to the user's dietary data. For example, the suggestion unit can analyze the calories and nutrients ingested by the user and adjust the intensity and content of the training. The suggestion unit can also provide advice on meals before and after training based on the user's dietary data. Furthermore, the suggestion unit can also use the user's dietary data to suggest a meal plan to maximize the effectiveness of the training. This improves the effectiveness of the training by optimizing the training plan based on the user's dietary data.

[0090] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, the notification unit can delay the timing of notifications when the user is feeling stressed. The notification unit can also advance the timing of notifications when the user is relaxed. Furthermore, the notification unit can prioritize important notifications when the user is in a hurry. This makes notifications more effective by adjusting the timing of notifications according to the user's emotions.

[0091] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user's emotions. For example, the analysis unit can emphasize positive feedback when the user is nervous. The analysis unit can also provide detailed feedback when the user is relaxed. Furthermore, the analysis unit can provide concise feedback when the user is in a hurry. This allows the analysis results to be communicated more effectively by adjusting the feedback method according to the user's emotions.

[0092] The input unit can estimate the user's emotion and adjust the order of input contents based on the estimated user's emotion. For example, if the user is feeling stressed, the input unit can display the most important input item first. Also, if the user is relaxed, the input unit can postpone detailed input options. Furthermore, if the user is in a hurry, the input unit can display the most important input item first, allowing the user to input quickly. This allows efficient input by adjusting the order of input contents according to the user's emotion.

[0093] The suggestion unit can evaluate the safety of a training course by taking into account the user's geographical location information. For example, the suggestion unit can suggest a course with low traffic volume based on the user's current location. The suggestion unit can also prioritize suggesting areas with low crime rates based on the user's geographical location information. Furthermore, the suggestion unit can also suggest a safe course by taking into account weather and terrain information based on the user's geographical location information. In this way, the optimal training course can be provided to the user by evaluating safety based on the geographical location information.

[0094] The notification unit can refer to the user's past training data and adjust the notification content according to the user's training progress. For example, if the user is approaching a goal, the notification unit can notify the user with an encouraging message. Alternatively, if the user is moving away from the goal, the notification unit can notify the user of areas for improvement. Furthermore, the notification unit can provide advice according to the user's training progress based on the user's past training data. This allows the user's training to be more efficient by adjusting the notification content based on the user's past training data.

[0095] The analysis unit can evaluate the effectiveness of training by referring to the user's dietary data. For example, the analysis unit can analyze the effectiveness of training based on the calories and nutrients ingested by the user. The analysis unit can also provide advice to maximize the effectiveness of training based on the user's dietary data. Furthermore, the analysis unit can use the user's dietary data to predict the effectiveness of training and provide feedback to the user. This improves the effectiveness of training by evaluating the training effect based on the user's dietary data.

[0096] The input unit can analyze the user's social media activity and automatically input related training information. For example, the input unit can automatically generate a training plan based on training data shared by the user on social media. The input unit can also automatically set training goals based on the user's social media activity. Furthermore, the input unit can analyze the user's social media activity and automatically input related training information. This makes input work more efficient through automatic input based on social media activity.

[0097] The suggestion unit can estimate the user's emotions and adjust the level of detail of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide concise suggestions. If the user is feeling relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying the most important suggestions. This makes the suggestions more effective by adjusting the level of detail of the suggestions according to the user's emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: In the input section, the runner inputs the type of training, target time, distance, and location. For example, the runner can select the type of training, such as jogging or interval training, and input the target time, running distance, and training location. Step 2: The suggestion unit uses AI to suggest the optimal course based on the information input by the input unit. For example, the suggestion unit may allocate a pace to match the target time or set a course according to the distance traveled. The suggestion unit can use an AI algorithm to analyze the runner's input information and suggest the optimal course. Step 3: The notification unit provides audio notifications during training based on the course proposed by the suggestion unit. For example, the notification unit measures the runner's running distance and pace in real time and provides audio notifications. For example, the notification unit provides audio notifications such as, "Your current running distance is 5 kilometers. Your pace is 5 minutes 30 seconds per kilometer." Step 4: The analysis unit analyzes the data and provides advice after the training based on the information notified by the notification unit. For example, the analysis unit analyzes the runner's running data and provides advice for the next training session. For example, the analysis unit provides specific advice such as, "Try to increase your pace a little next time."

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section for inputting the type of training or target time, distance, and location; a suggestion unit that suggests a course based on the information input by the input unit; a notification unit that issues a voice notification during training based on the course suggested by the suggestion unit; an analysis unit that provides data analysis and advice after training based on the information notified by the notification unit; Equipped with A system characterized by:

2. The proposal unit Equipped with an algorithm that suggests courses using AI 2. The system of claim 1.

3. The notification unit Get real-time audio feedback on your distance or pace during training 2. The system of claim 1.

4. The analysis unit Analyzes running data after training and provides advice for the next training session 2. The system of claim 1.

5. The input unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

6. The input unit Based on past training data, the system automatically suggests the most suitable training type and target time for the user.

2. The system of claim 1.

7. The input unit Monitor the user's physical condition in real time and adjust input accordingly 2. The system of claim 1.

8. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

9. The input unit Considers your geographic location to suggest local training events and courses relevant to your input 2. The system of claim 1.

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