system

Abstract

The system according to the embodiment aims to enable athletes to receive appropriate training and care without receiving specialized instruction. [Solution] A system according to an embodiment includes a recording unit, an analysis unit, and a provision unit. The recording unit records videos or photos. The analysis unit analyzes the information recorded by the recording unit. The provision unit provides advice based on the analysis results obtained by the analysis 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] Conventional technology has had the problem that it is difficult for athletes to receive professional instruction, making it difficult to provide appropriate training and care.

[0005] The system according to the embodiment aims to enable athletes to receive appropriate training and care without receiving specialized instruction. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, and a provision unit. The recording unit records videos or photos. The analysis unit analyzes the information recorded by the recording unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows athletes to receive appropriate training and care without receiving specialized instruction. [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 provides advice on training, diet, and care to athletes competing in individual sports to achieve their target records. This training support system allows athletes to record their daily practice sessions using videos and photos. AI analyzes these records and then suggests running style modifications and training menus to bring athletes closer to their target records. For example, when suggesting muscle strengthening, the system uses videos to clearly show which parts of the body should be strengthened and how. It also analyzes the athlete's current nutritional status and provides a meal menu for the competition. It also suggests areas and how to improve post-practice care. This system, which combines AI and sports science, helps athletes efficiently approach their target records. For example, the system features a feature in which the athlete records their daily practice sessions and the AI ​​analyzes them to provide specific advice. Video suggestions are particularly visually easy to understand and beneficial to athletes. This allows the training support system to efficiently help athletes approach their target records.

[0029] A training support system according to an embodiment includes a recording unit, an analysis unit, and a providing unit. The recording unit records the athlete's daily training progress using video or photographs. The video or photographs may include, but are not limited to, various resolutions, formats, and imaging devices. The recording unit may, for example, use a smartphone or camera to capture images of the training progress and save them as digital data. The recording unit may also record the training progress in real time. For example, the recording unit may continuously capture images of the athlete running and save the captured images as data for later analysis. The analysis unit analyzes the information recorded by the recording unit. Examples of analysis include, but are not limited to, image analysis algorithms and data preprocessing methods. The analysis unit may, for example, analyze the athlete's running style and running form to identify areas for improvement. The analysis unit may also analyze the athlete's muscular strength and physical fitness and generate data for proposing a training menu. The providing unit provides advice based on the analysis results obtained by the analysis unit. The advice may, for example, be provided in text format, audio format, or real-time format, but is not limited to these examples. The providing unit, for example, proposes a specific training menu to the athlete. The providing unit can also analyze the athlete's nutritional status and provide an appropriate meal menu. Furthermore, the providing unit can propose care methods to the athlete and support recovery after practice. For example, the providing unit proposes stretching methods and recovery techniques to the athlete. This allows the training support system according to the embodiment to efficiently support the athlete in approaching their target record. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may provide advice using an AI model that inputs the analysis results obtained by the analysis unit and outputs advice. Furthermore, the providing unit has a function for visually displaying the advice. For example, the providing unit may display the advice as a video or image, providing it in a format that is easy for the athlete to understand. The providing unit may also personalize the advice and make specific suggestions based on the athlete's characteristics and goals.This allows the provider to support the athlete in efficiently approaching their target record.

[0030] The providing unit can make suggestions for improving muscle strength. For example, the providing unit can suggest a training menu for improving the athlete's muscle strength. For example, the providing unit can analyze the athlete's muscle strength status and suggest exercises for strengthening specific muscle parts. The providing unit can also suggest effective methods for improving muscle strength based on the athlete's training history. For example, the providing unit can analyze past training data and create an optimal training plan. Furthermore, the providing unit can monitor the athlete's progress in improving muscle strength and adjust the training menu as needed according to the athlete's goals. For example, the providing unit can regularly evaluate the athlete's progress in improving muscle strength and update the training menu. This allows the providing unit to enable the athlete to efficiently improve muscle strength. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can suggest a training menu using an AI model that inputs the athlete's muscle strength status and outputs suggestions for improving muscle strength. Furthermore, the providing unit has a function for visually displaying the suggestions for improving muscle strength. For example, the information providing unit may display exercise methods as videos or images in a format that is easy for athletes to understand. The information providing unit may also personalize suggestions for muscle building and provide specific suggestions based on the characteristics and goals of each athlete. This allows the information providing unit to help athletes efficiently build muscle.

[0031] The providing unit can provide a meal menu. For example, the providing unit can analyze the nutritional status of an athlete and suggest an appropriate meal menu. For example, the providing unit can create a nutritionally balanced meal menu based on the athlete's dietary records. The providing unit can also calculate calories and select ingredients according to the athlete's goals. For example, the providing unit can suggest an appropriate calorie intake taking into account the athlete's weight and body fat percentage. Furthermore, the providing unit can suggest a meal menu that enhances specific nutrients according to the athlete's training content. For example, the providing unit can suggest a meal menu high in protein to an athlete aiming to increase muscle strength. This allows the providing unit to obtain an appropriate meal menu for the athlete. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can suggest a meal menu using an AI model that inputs the athlete's nutritional status and outputs a meal menu. Furthermore, the providing unit has a function to visually display the meal menu. For example, the providing unit can display the meal menu as images or text, providing it in a format that is easy for the athlete to understand. The provision unit can also personalize the meal menu and make specific suggestions based on the characteristics and goals of the athlete, thereby enabling the provision unit to provide the athlete with an appropriate meal menu.

[0032] The providing unit can make care suggestions. For example, the providing unit can suggest care methods for an athlete after practice. For example, the providing unit can analyze the athlete's physical condition and suggest appropriate stretching methods and recovery techniques. The providing unit can also suggest specific care methods for recovery taking into account the athlete's level of fatigue. For example, the providing unit can monitor the athlete's muscle condition and suggest appropriate massage methods. The providing unit also has a function to visually display the athlete's care methods. For example, the providing unit can display stretching methods and recovery techniques as videos or images in a format that is easy for the athlete to understand. The providing unit can also individualize the care methods and make specific suggestions based on the athlete's characteristics and goals. This allows the providing unit to obtain an appropriate care method for the athlete. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make care suggestions using an AI model that inputs the athlete's physical condition and outputs care methods. This allows the providing unit to obtain an appropriate care method for the athlete.

[0033] The analysis unit can analyze videos or photos. For example, the analysis unit analyzes videos or photos recording an athlete's training session. For example, the analysis unit analyzes the athlete's running style and form to identify areas for improvement. The analysis unit can also analyze the athlete's movements and generate data to suggest efficient training methods. For example, the analysis unit can analyze the athlete's movements in slow motion to identify areas for fine-tuning. The analysis unit also has a function to visually display the analysis results of the videos or photos. For example, the analysis unit can display the analysis results as graphs or charts in a format that is easy for the athlete to understand. The analysis unit can also individualize the analysis results of the videos or photos and make specific suggestions based on the athlete's characteristics and goals. This allows the analysis unit to analyze the athlete's training session in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model that inputs videos or photos recording the athlete's training session and outputs analysis results. This allows the analysis unit to analyze the athlete's training session in detail.

[0034] The analysis unit can analyze the nutritional status. The analysis unit analyzes the nutritional status based on, for example, the athlete's food records and blood test results. For example, the analysis unit analyzes the athlete's food records and generates data for proposing a nutritionally balanced meal menu. The analysis unit can also analyze the athlete's blood test results to identify deficiencies or excesses of specific nutrients. For example, the analysis unit can measure the concentrations of vitamins and minerals in the athlete's blood and suggest appropriate nutritional supplementation. The analysis unit also has a function to visually display the nutritional status analysis results. For example, the analysis unit can display the nutritional status analysis results as graphs or charts in a format that is easy for the athlete to understand. The analysis unit can also individualize the nutritional status analysis results and make specific suggestions based on the athlete's characteristics and goals. This allows the analysis unit to analyze the athlete's nutritional status in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the nutritional status using an AI model that inputs the athlete's food records and blood test results and outputs nutritional status analysis results. This allows the analysis unit to analyze the nutritional status of the athlete in detail.

[0035] The recording unit can select the optimal recording method by referring to the user's past practice history when recording. The recording unit, for example, uses a training log to refer to the user's past practice history. For example, the recording unit analyzes the training log, which records the user's past practice content and results, and selects the optimal recording method. The recording unit can also suggest a recording method suitable for a specific practice content based on the user's past practice history. For example, the recording unit prioritizes selecting a recording method that was effective for the user in the past. Furthermore, the recording unit has a function to analyze the user's past recording data and select the most efficient recording method. For example, the recording unit suggests the optimal recording method based on the user's past recording data. This allows the recording unit to select the optimal recording method based on the user's past practice history. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select a recording method using an AI model that inputs the user's past practice history and outputs the optimal recording method. This allows the recording unit to select the optimal recording method based on the user's past practice history.

[0036] The recording unit can adjust the recording frequency during recording, taking into account the user's current physical condition and fatigue level. The recording unit, for example, measures vital signs to evaluate the user's physical condition and fatigue level. For example, the recording unit measures the user's heart rate and blood pressure to evaluate the user's physical condition and fatigue level. The recording unit can also evaluate the user's physical condition and fatigue level based on the user's self-report. For example, the recording unit adjusts the recording frequency based on the user's self-reported physical condition and fatigue level. Furthermore, the recording unit has a function to adjust the recording frequency according to the user's physical condition and fatigue level. For example, if the user is tired, the recording unit reduces the recording frequency and prioritizes rest. Also, if the user is in good physical condition, the recording unit can increase the recording frequency to collect more detailed data. This allows the recording unit to adjust the recording frequency according to the user's physical condition and fatigue level. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can adjust the recording frequency using an AI model that inputs the user's vital sign data and outputs the recording frequency. This allows the recording unit to adjust the frequency of recording according to the user's physical condition and fatigue level.

[0037] When recording, the recording unit can prioritize recording highly relevant practice content by taking into account the user's geographical location information. The recording unit, for example, uses GPS data to acquire the user's geographical location information. For example, the recording unit identifies the user's current location based on the GPS data and records practice content corresponding to that location. Furthermore, when the user is at a specific training facility, the recording unit can also record practice content appropriate for that facility. For example, when the user is practicing outdoors, the recording unit records practice content appropriate for the weather and terrain. Furthermore, the recording unit has a function to prioritize recording highly relevant practice content based on the user's geographical location information. For example, when the user is practicing at home, the recording unit records practice content that can be performed indoors. This allows the recording unit to record highly relevant practice content based on the user's geographical location information. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select content to record using an AI model that inputs the user's geographical location information and outputs highly relevant practice content. This allows the recording unit to record highly relevant practice content based on the user's geographical location information.

[0038] The recording unit can analyze the user's social media activity and record related practice content during recording. For example, the recording unit analyzes the user's social media activity by analyzing the content of posts, the number of likes, comments, and the like. For example, the recording unit records related practice content based on the practice content shared by the user on social media. The recording unit can also record based on the practice content of trainers and athletes the user follows. For example, the recording unit analyzes trends in online communities in which the user participates and records related practice content. Furthermore, the recording unit has a function for suggesting optimal practice content based on the user's social media activity. For example, the recording unit analyzes the user's social media activity and suggests the most effective practice method. This allows the recording unit to record related practice content based on the user's social media activity. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can select content to record using an AI model that inputs the user's social media activity data and outputs related practice content. This allows the recording unit to record related practice content based on the user's social media activity.

[0039] During analysis, the analysis unit can adjust the analysis algorithm by referring to past analysis data. The analysis unit, for example, uses a database to refer to past analysis data. For example, the analysis unit obtains past analysis results from the database and selects an optimal analysis algorithm. The analysis unit can also improve the algorithm by utilizing the past analysis results as feedback. For example, the analysis unit analyzes past analysis data and adjusts parameters to improve analysis accuracy. Furthermore, the analysis unit has a function to optimize the analysis algorithm based on past analysis data. For example, the analysis unit proposes an optimal analysis algorithm based on the past analysis data. This allows the analysis unit to optimize the analysis algorithm based on the past analysis 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 optimize the analysis algorithm using an AI model that uses past analysis data as input and adjusts the analysis algorithm. This allows the analysis unit to optimize the analysis algorithm based on the past analysis data.

[0040] The analysis unit can improve the accuracy of the analysis by taking into account the user's practice history and physical condition data. The analysis unit, for example, uses a training log to reference the user's practice history. For example, the analysis unit analyzes the training log, which records the user's past practice content and results, to improve the accuracy of the analysis. The analysis unit can also adjust the analysis results by taking into account the user's physical condition data. For example, the analysis unit adjusts the analysis results based on the user's vital sign data. Furthermore, the analysis unit has a function of integrating the user's practice history and physical condition data to perform more accurate analysis. For example, the analysis unit proposes an optimal analysis algorithm based on the user's practice history and physical condition data. This allows the analysis unit to improve the accuracy of the analysis based on the user's practice history and physical condition 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 AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that inputs the user's practice history and physical condition data and outputs analysis results. This allows the analysis unit to improve the accuracy of the analysis based on the user's practice history and physical condition data.

[0041] The analysis unit can take the user's geographical distribution into consideration when performing the analysis. For example, the analysis unit aggregates data by region to evaluate the user's geographical distribution. For example, if the user practices in a specific region, the analysis unit can perform the analysis taking into account the characteristics of that region. Furthermore, if the user practices in different regions, the analysis unit can integrate data for each region and perform the analysis. For example, the analysis unit can propose a region-specific training method based on the user's geographical distribution. Furthermore, the analysis unit has a function of proposing an optimal analysis algorithm based on the user's geographical distribution. For example, the analysis unit can propose an optimal analysis method based on the user's geographical distribution. This allows the analysis unit to perform the analysis based on the user's geographical distribution. 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 perform the analysis using an AI model that inputs the user's geographical distribution data and outputs analysis results. This allows the analysis unit to perform the analysis based on the user's geographical distribution.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related literature and data during analysis. The analysis unit, for example, uses academic papers and databases to refer to related literature and data. For example, the analysis unit improves the analysis algorithm by referring to the latest research papers. The analysis unit can also complement the analysis results by referring to related databases. For example, the analysis unit incorporates the opinions of experts to improve the accuracy of the analysis. Furthermore, the analysis unit has a function to propose an optimal analysis algorithm based on related literature and data. For example, the analysis unit proposes an optimal analysis method based on related literature and data. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature and 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 improve the accuracy of the analysis by using an AI model that inputs related literature and data and outputs analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature and data.

[0043] When providing advice, the providing unit can provide optimal advice by referring to the user's past practice history. The providing unit, for example, uses a training log to refer to the user's past practice history. For example, the providing unit analyzes the training log, which records the user's past practice content and results, and provides optimal advice. The providing unit can also provide effective advice based on the user's past practice data. For example, the providing unit analyzes past practice data and creates an optimal training plan. Furthermore, the providing unit has a function to provide specific advice by referring to the user's past practice history. For example, the providing unit suggests optimal advice based on the user's past practice history. This allows the providing unit to provide optimal advice based on the user's past practice history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that inputs the user's past practice history and outputs optimal advice. This allows the providing unit to provide optimal advice based on the user's past practice history.

[0044] When providing advice, the providing unit can customize the content of the advice taking into account the user's current physical condition and fatigue level. The providing unit, for example, measures vital signs to evaluate the user's physical condition and fatigue level. For example, the providing unit measures the user's heart rate and blood pressure to evaluate the user's physical condition and fatigue level. The providing unit can also evaluate the user's physical condition and fatigue level based on the user's self-report. For example, the providing unit customizes the content of the advice based on the user's self-reported physical condition and fatigue level. Furthermore, the providing unit has a function to customize the content of the advice according to the user's physical condition and fatigue level. For example, the providing unit can suggest light training if the user is tired. Also, the providing unit can suggest hard training if the user is in good physical condition. This allows the providing unit to customize the content of the advice according to the user's physical condition and fatigue level. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the advice using an AI model that inputs the user's vital sign data and outputs the content of the advice. This allows the providing unit to customize the content of the advice according to the user's physical condition and fatigue level.

[0045] When providing advice, the providing unit can provide optimal advice taking into account the user's geographical location information. The providing unit, for example, uses GPS data to acquire the user's geographical location information. For example, the providing unit identifies the user's current location based on the GPS data and provides advice according to that location. Furthermore, if the user is in a specific training facility, the providing unit can provide advice appropriate to that facility. For example, if the user is practicing outdoors, the providing unit provides advice according to the weather and terrain. Furthermore, the providing unit has a function to suggest optimal advice based on the user's geographical location information. For example, if the user is practicing at home, the providing unit provides advice that can be performed indoors. This allows the providing unit to provide optimal advice based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that inputs the user's geographical location information and outputs optimal advice. This allows the providing unit to provide optimal advice based on the user's geographical location information.

[0046] When providing advice, the providing unit can analyze the user's social media activity and adjust the content of the advice. For example, the providing unit analyzes the user's social media activity by analyzing the content of posts, the number of likes, comments, etc. For example, the providing unit provides relevant advice based on the practice content shared by the user on social media. The providing unit can also provide advice based on the advice of trainers and athletes followed by the user. For example, the providing unit analyzes trends in online communities in which the user participates and provides relevant advice. Furthermore, the providing unit has a function of suggesting optimal advice based on the user's social media activity. For example, the providing unit analyzes the user's social media activity and suggests the most effective advice. This allows the providing unit to adjust the content of the advice based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the content of the advice using an AI model that inputs the user's social media activity data and outputs relevant advice. This allows the providing unit to adjust the content of the advice based on the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When analyzing a user's practice data, the analysis unit can adjust the analysis results by taking weather data into account. For example, it can analyze practice data in rainy weather separately from practice data in sunny weather. It can also correct the analysis results by taking environmental factors such as temperature and humidity into account. It can also evaluate performance under specific weather conditions and suggest appropriate training menus. This allows the analysis unit to analyze the user's practice data more accurately.

[0049] When recording the user's practice data, the recording unit can also record the ambient sounds of the practice location. For example, when practicing outdoors, the recording unit can record the sound of the wind and birds chirping. When practicing indoors, the recording unit can also record background sounds to recreate the practice environment. Furthermore, the recording unit can analyze the effect of specific sounds on practice and suggest the optimal practice environment. This allows the recording unit to record the user's practice data in more detail.

[0050] When analyzing a user's practice data, the analysis unit can adjust the analysis results by taking the user's sleep data into account. For example, it can correct the practice data by taking into account the effects of sleep deprivation. It can also evaluate the impact of good quality sleep on performance and suggest an appropriate training menu. It can also analyze the user's sleep patterns and suggest optimal practice times. This allows the analysis unit to analyze the user's practice data more accurately.

[0051] The recording unit can simultaneously record the user's heart rate data when recording the user's practice data. For example, it can record heart rate fluctuations during practice and evaluate the intensity of the practice. It can also analyze the effectiveness of practice based on the heart rate data and suggest an appropriate training menu. It can also use the heart rate data to monitor the user's physical condition and adjust the practice menu as needed. This allows the recording unit to record the user's practice data in more detail.

[0052] When analyzing a user's practice data, the analysis unit can adjust the analysis results by taking the user's dietary data into account. For example, it can evaluate the impact that the intake of specific nutrients has on performance. It can also analyze the effectiveness of practice based on dietary data and suggest appropriate training menus. It can also analyze the user's eating patterns and suggest optimal meal timings. This allows the analysis unit to analyze the user's practice data more accurately.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The recording unit records the athlete's daily training status using video or photographs. For example, the training status can be captured using a smartphone or camera and saved as digital data. The recording unit can also record the training status in real time. For example, the athlete's running status can be continuously captured and saved as data for later analysis. Step 2: The analysis unit analyzes the information recorded by the recording unit. This analysis includes, for example, image analysis algorithms and data preprocessing methods. The analysis unit analyzes the athlete's running style and form to identify areas for improvement. It can also analyze the athlete's muscle strength and physical condition and generate data to suggest training menus. Step 3: The provider provides advice based on the analysis results obtained by the analyzer. Advice may be provided in text, audio, or real-time formats, for example. The provider proposes specific training menus to the athlete. It can also analyze the athlete's nutritional status and provide appropriate meal menus. It can also suggest care methods for the athlete and support recovery after practice. For example, it can suggest stretching methods and recovery techniques. Some or all of the processing in the provider may be performed using AI, and it also has the ability to visually display advice. For example, advice may be displayed as a video or image, providing it in a format that is easy for the athlete to understand. It can also personalize advice to provide specific suggestions tailored to the athlete's characteristics and goals.

[0055] (Example 2) A training support system according to an embodiment of the present invention provides advice on training, diet, and care to athletes competing in individual sports to achieve their target records. This training support system allows athletes to record their daily practice sessions using videos and photos. AI analyzes these records and then suggests running style modifications and training menus to bring athletes closer to their target records. For example, when suggesting muscle strengthening, the system uses videos to clearly show which parts of the body should be strengthened and how. It also analyzes the athlete's current nutritional status and provides a meal menu for the competition. It also suggests areas and methods of post-practice care. This system, which combines AI and sports science, helps athletes efficiently approach their target records. For example, the system features a feature in which the athlete records their daily practice sessions and the AI ​​analyzes them to provide specific advice. Video suggestions are particularly visually easy to understand and beneficial to athletes. This allows the training support system to efficiently help athletes approach their target records.

[0056] A training support system according to an embodiment includes a recording unit, an analysis unit, and a providing unit. The recording unit records the athlete's daily training progress using video or photographs. The video or photographs may include, but are not limited to, various resolutions, formats, and imaging devices. The recording unit may, for example, use a smartphone or camera to capture images of the training progress and save them as digital data. The recording unit may also record the training progress in real time. For example, the recording unit may continuously capture images of the athlete running and save the captured images as data for later analysis. The analysis unit analyzes the information recorded by the recording unit. Examples of analysis include, but are not limited to, image analysis algorithms and data preprocessing methods. The analysis unit may, for example, analyze the athlete's running style and running form to identify areas for improvement. The analysis unit may also analyze the athlete's muscular strength and physical fitness and generate data for proposing a training menu. The providing unit provides advice based on the analysis results obtained by the analysis unit. The advice may, for example, be provided in text format, audio format, or real-time format, but is not limited to these examples. The providing unit, for example, proposes a specific training menu to the athlete. The providing unit can also analyze the athlete's nutritional status and provide an appropriate meal menu. Furthermore, the providing unit can propose care methods to the athlete and support recovery after practice. For example, the providing unit proposes stretching methods and recovery techniques to the athlete. This allows the training support system according to the embodiment to efficiently support the athlete in approaching their target record. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may provide advice using an AI model that inputs the analysis results obtained by the analysis unit and outputs advice. Furthermore, the providing unit has a function for visually displaying the advice. For example, the providing unit may display the advice as a video or image, providing it in a format that is easy for the athlete to understand. The providing unit may also personalize the advice and make specific suggestions based on the athlete's characteristics and goals.This allows the provider to support the athlete in efficiently approaching their target record.

[0057] The providing unit can make suggestions for improving muscle strength. For example, the providing unit can suggest a training menu for improving the athlete's muscle strength. For example, the providing unit can analyze the athlete's muscle strength status and suggest exercises for strengthening specific muscle parts. The providing unit can also suggest effective methods for improving muscle strength based on the athlete's training history. For example, the providing unit can analyze past training data and create an optimal training plan. Furthermore, the providing unit can monitor the athlete's progress in improving muscle strength and adjust the training menu as needed according to the athlete's goals. For example, the providing unit can regularly evaluate the athlete's progress in improving muscle strength and update the training menu. This allows the providing unit to enable the athlete to efficiently improve muscle strength. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can suggest a training menu using an AI model that inputs the athlete's muscle strength status and outputs suggestions for improving muscle strength. Furthermore, the providing unit has a function for visually displaying the suggestions for improving muscle strength. For example, the information providing unit may display exercise methods as videos or images in a format that is easy for athletes to understand. The information providing unit may also personalize suggestions for muscle building and provide specific suggestions based on the characteristics and goals of each athlete. This allows the information providing unit to help athletes efficiently build muscle.

[0058] The providing unit can provide a meal menu. For example, the providing unit can analyze the nutritional status of an athlete and suggest an appropriate meal menu. For example, the providing unit can create a nutritionally balanced meal menu based on the athlete's dietary records. The providing unit can also calculate calories and select ingredients according to the athlete's goals. For example, the providing unit can suggest an appropriate calorie intake taking into account the athlete's weight and body fat percentage. Furthermore, the providing unit can suggest a meal menu that enhances specific nutrients according to the athlete's training content. For example, the providing unit can suggest a meal menu high in protein to an athlete aiming to increase muscle strength. This allows the providing unit to obtain an appropriate meal menu for the athlete. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can suggest a meal menu using an AI model that inputs the athlete's nutritional status and outputs a meal menu. Furthermore, the providing unit has a function to visually display the meal menu. For example, the providing unit can display the meal menu as images or text, providing it in a format that is easy for the athlete to understand. The provision unit can also personalize the meal menu and make specific suggestions based on the characteristics and goals of the athlete, thereby enabling the provision unit to provide the athlete with an appropriate meal menu.

[0059] The providing unit can make care suggestions. For example, the providing unit can suggest care methods for an athlete after practice. For example, the providing unit can analyze the athlete's physical condition and suggest appropriate stretching methods and recovery techniques. The providing unit can also suggest specific care methods for recovery taking into account the athlete's level of fatigue. For example, the providing unit can monitor the athlete's muscle condition and suggest appropriate massage methods. The providing unit also has a function to visually display the athlete's care methods. For example, the providing unit can display stretching methods and recovery techniques as videos or images in a format that is easy for the athlete to understand. The providing unit can also individualize the care methods and make specific suggestions based on the athlete's characteristics and goals. This allows the providing unit to obtain an appropriate care method for the athlete. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can make care suggestions using an AI model that inputs the athlete's physical condition and outputs care methods. This allows the providing unit to obtain an appropriate care method for the athlete.

[0060] The analysis unit can analyze videos or photos. For example, the analysis unit analyzes videos or photos recording an athlete's training session. For example, the analysis unit analyzes the athlete's running style and form to identify areas for improvement. The analysis unit can also analyze the athlete's movements and generate data to suggest efficient training methods. For example, the analysis unit can analyze the athlete's movements in slow motion to identify areas for fine-tuning. The analysis unit also has a function to visually display the analysis results of the videos or photos. For example, the analysis unit can display the analysis results as graphs or charts in a format that is easy for the athlete to understand. The analysis unit can also individualize the analysis results of the videos or photos and make specific suggestions based on the athlete's characteristics and goals. This allows the analysis unit to analyze the athlete's training session in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model that inputs videos or photos recording the athlete's training session and outputs analysis results. This allows the analysis unit to analyze the athlete's training session in detail.

[0061] The analysis unit can analyze the nutritional status. The analysis unit analyzes the nutritional status based on, for example, the athlete's food records and blood test results. For example, the analysis unit analyzes the athlete's food records and generates data for proposing a nutritionally balanced meal menu. The analysis unit can also analyze the athlete's blood test results to identify deficiencies or excesses of specific nutrients. For example, the analysis unit can measure the concentrations of vitamins and minerals in the athlete's blood and suggest appropriate nutritional supplementation. The analysis unit also has a function to visually display the nutritional status analysis results. For example, the analysis unit can display the nutritional status analysis results as graphs or charts in a format that is easy for the athlete to understand. The analysis unit can also individualize the nutritional status analysis results and make specific suggestions based on the athlete's characteristics and goals. This allows the analysis unit to analyze the athlete's nutritional status in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the nutritional status using an AI model that inputs the athlete's food records and blood test results and outputs nutritional status analysis results. This allows the analysis unit to analyze the nutritional status of the athlete in detail.

[0062] The recording unit can estimate the user's emotions and adjust the timing of recording based on the estimated user emotions. The recording unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the recording unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The recording unit can also analyze the user's voice to estimate the emotions. For example, the recording unit analyzes the tone and speed of the user's voice and calculates an emotion score. The recording unit also has a function to adjust the timing of recording based on the user's emotions. For example, if the user is feeling stressed, the recording unit adjusts the recording to a time when the user can relax. Furthermore, if the user is concentrating, the recording unit can reduce the frequency of recording to maintain the user's concentration. This allows the recording unit to adjust the timing of recording according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion. This allows the recording unit to adjust the timing of recording according to the user's emotion.

[0063] The recording unit can select the optimal recording method by referring to the user's past practice history when recording. The recording unit, for example, uses a training log to refer to the user's past practice history. For example, the recording unit analyzes the training log, which records the user's past practice content and results, and selects the optimal recording method. The recording unit can also suggest a recording method suitable for a specific practice content based on the user's past practice history. For example, the recording unit prioritizes selecting a recording method that was effective for the user in the past. Furthermore, the recording unit has a function to analyze the user's past recording data and select the most efficient recording method. For example, the recording unit suggests the optimal recording method based on the user's past recording data. This allows the recording unit to select the optimal recording method based on the user's past practice history. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select a recording method using an AI model that inputs the user's past practice history and outputs the optimal recording method. This allows the recording unit to select the optimal recording method based on the user's past practice history.

[0064] The recording unit can adjust the recording frequency during recording, taking into account the user's current physical condition and fatigue level. The recording unit, for example, measures vital signs to evaluate the user's physical condition and fatigue level. For example, the recording unit measures the user's heart rate and blood pressure to evaluate the user's physical condition and fatigue level. The recording unit can also evaluate the user's physical condition and fatigue level based on the user's self-report. For example, the recording unit adjusts the recording frequency based on the user's self-reported physical condition and fatigue level. Furthermore, the recording unit has a function to adjust the recording frequency according to the user's physical condition and fatigue level. For example, if the user is tired, the recording unit reduces the recording frequency and prioritizes rest. Also, if the user is in good physical condition, the recording unit can increase the recording frequency to collect more detailed data. This allows the recording unit to adjust the recording frequency according to the user's physical condition and fatigue level. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can adjust the recording frequency using an AI model that inputs the user's vital sign data and outputs the recording frequency. This allows the recording unit to adjust the frequency of recording according to the user's physical condition and fatigue level.

[0065] The recording unit can estimate the user's emotions and prioritize the content to be recorded based on the estimated user emotions. The recording unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the recording unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The recording unit can also analyze the user's voice to estimate the emotions. For example, the recording unit can analyze the tone and speed of the user's voice and calculate an emotion score. The recording unit also has a function to prioritize the content to be recorded based on the user's emotions. For example, if the user is excited, the recording unit can prioritize recording important practice content. Also, if the user is relaxed, the recording unit can record detailed practice content. This allows the recording unit to prioritize the content to be recorded based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may input the user's facial expression data into a generating AI and have the generating AI estimate the emotion. This allows the recording unit to determine the priority of the content to be recorded according to the user's emotion.

[0066] When recording, the recording unit can prioritize recording highly relevant practice content by taking into account the user's geographical location information. The recording unit, for example, uses GPS data to acquire the user's geographical location information. For example, the recording unit identifies the user's current location based on the GPS data and records practice content corresponding to that location. Furthermore, when the user is at a specific training facility, the recording unit can also record practice content appropriate for that facility. For example, when the user is practicing outdoors, the recording unit records practice content appropriate for the weather and terrain. Furthermore, the recording unit has a function to prioritize recording highly relevant practice content based on the user's geographical location information. For example, when the user is practicing at home, the recording unit records practice content that can be performed indoors. This allows the recording unit to record highly relevant practice content based on the user's geographical location information. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select content to record using an AI model that inputs the user's geographical location information and outputs highly relevant practice content. This allows the recording unit to record highly relevant practice content based on the user's geographical location information.

[0067] The recording unit can analyze the user's social media activity and record related practice content during recording. For example, the recording unit analyzes the user's social media activity by analyzing the content of posts, the number of likes, comments, and the like. For example, the recording unit records related practice content based on the practice content shared by the user on social media. The recording unit can also record based on the practice content of trainers and athletes the user follows. For example, the recording unit analyzes trends in online communities in which the user participates and records related practice content. Furthermore, the recording unit has a function for suggesting optimal practice content based on the user's social media activity. For example, the recording unit analyzes the user's social media activity and suggests the most effective practice method. This allows the recording unit to record related practice content based on the user's social media activity. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit can select content to record using an AI model that inputs the user's social media activity data and outputs related practice content. This allows the recording unit to record related practice content based on the user's social media activity.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also analyze the user's voice to estimate the emotions. For example, the analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The analysis unit also has a function to adjust the analysis criteria based on the user's emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is in a hurry. This allows the analysis unit to adjust the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions. This allows the analysis unit to adjust the analysis criteria according to the user's emotions.

[0069] During analysis, the analysis unit can adjust the analysis algorithm by referring to past analysis data. The analysis unit, for example, uses a database to refer to past analysis data. For example, the analysis unit obtains past analysis results from the database and selects an optimal analysis algorithm. The analysis unit can also improve the algorithm by utilizing the past analysis results as feedback. For example, the analysis unit analyzes past analysis data and adjusts parameters to improve analysis accuracy. Furthermore, the analysis unit has a function to optimize the analysis algorithm based on past analysis data. For example, the analysis unit proposes an optimal analysis algorithm based on the past analysis data. This allows the analysis unit to optimize the analysis algorithm based on the past analysis 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 optimize the analysis algorithm using an AI model that uses past analysis data as input and adjusts the analysis algorithm. This allows the analysis unit to optimize the analysis algorithm based on the past analysis data.

[0070] The analysis unit can improve the accuracy of the analysis by taking into account the user's practice history and physical condition data. The analysis unit, for example, uses a training log to reference the user's practice history. For example, the analysis unit analyzes the training log, which records the user's past practice content and results, to improve the accuracy of the analysis. The analysis unit can also adjust the analysis results by taking into account the user's physical condition data. For example, the analysis unit adjusts the analysis results based on the user's vital sign data. Furthermore, the analysis unit has a function of integrating the user's practice history and physical condition data to perform more accurate analysis. For example, the analysis unit proposes an optimal analysis algorithm based on the user's practice history and physical condition data. This allows the analysis unit to improve the accuracy of the analysis based on the user's practice history and physical condition 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 AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that inputs the user's practice history and physical condition data and outputs analysis results. This allows the analysis unit to improve the accuracy of the analysis based on the user's practice history and physical condition data.

[0071] 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, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also analyze the user's voice to estimate the emotions. For example, the analysis unit can analyze the tone and speed of the user's voice and calculate an emotion score. The analysis unit also has a function to adjust the display method of the analysis results based on the user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotion.

[0072] The analysis unit can take the user's geographical distribution into consideration when performing the analysis. For example, the analysis unit aggregates data by region to evaluate the user's geographical distribution. For example, if the user practices in a specific region, the analysis unit can perform the analysis taking into account the characteristics of that region. Furthermore, if the user practices in different regions, the analysis unit can integrate data for each region and perform the analysis. For example, the analysis unit can propose a region-specific training method based on the user's geographical distribution. Furthermore, the analysis unit has a function of proposing an optimal analysis algorithm based on the user's geographical distribution. For example, the analysis unit can propose an optimal analysis method based on the user's geographical distribution. This allows the analysis unit to perform the analysis based on the user's geographical distribution. 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 perform the analysis using an AI model that inputs the user's geographical distribution data and outputs analysis results. This allows the analysis unit to perform the analysis based on the user's geographical distribution.

[0073] The analysis unit can improve the accuracy of the analysis by referring to related literature and data during analysis. The analysis unit, for example, uses academic papers and databases to refer to related literature and data. For example, the analysis unit improves the analysis algorithm by referring to the latest research papers. The analysis unit can also complement the analysis results by referring to related databases. For example, the analysis unit incorporates the opinions of experts to improve the accuracy of the analysis. Furthermore, the analysis unit has a function to propose an optimal analysis algorithm based on related literature and data. For example, the analysis unit proposes an optimal analysis method based on related literature and data. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature and 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 improve the accuracy of the analysis by using an AI model that inputs related literature and data and outputs analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature and data.

[0074] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The providing unit can also analyze the user's voice to estimate the emotions. For example, the providing unit can analyze the tone and speed of the user's voice and calculate an emotion score. The providing unit also has a function to adjust the way the advice is presented based on the user's emotions. For example, if the user is nervous, the providing unit provides advice in a calm manner. If the user is relaxed, the providing unit can also provide detailed advice. This allows the providing unit to adjust the way the advice is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate the user's emotions, thereby enabling the providing unit to adjust the way advice is expressed depending on the user's emotions.

[0075] When providing advice, the providing unit can provide optimal advice by referring to the user's past practice history. The providing unit, for example, uses a training log to refer to the user's past practice history. For example, the providing unit analyzes the training log, which records the user's past practice content and results, and provides optimal advice. The providing unit can also provide effective advice based on the user's past practice data. For example, the providing unit analyzes past practice data and creates an optimal training plan. Furthermore, the providing unit has a function to provide specific advice by referring to the user's past practice history. For example, the providing unit suggests optimal advice based on the user's past practice history. This allows the providing unit to provide optimal advice based on the user's past practice history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that inputs the user's past practice history and outputs optimal advice. This allows the providing unit to provide optimal advice based on the user's past practice history.

[0076] When providing advice, the providing unit can customize the content of the advice taking into account the user's current physical condition and fatigue level. The providing unit, for example, measures vital signs to evaluate the user's physical condition and fatigue level. For example, the providing unit measures the user's heart rate and blood pressure to evaluate the user's physical condition and fatigue level. The providing unit can also evaluate the user's physical condition and fatigue level based on the user's self-report. For example, the providing unit customizes the content of the advice based on the user's self-reported physical condition and fatigue level. Furthermore, the providing unit has a function to customize the content of the advice according to the user's physical condition and fatigue level. For example, the providing unit can suggest light training if the user is tired. Also, the providing unit can suggest hard training if the user is in good physical condition. This allows the providing unit to customize the content of the advice according to the user's physical condition and fatigue level. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the advice using an AI model that inputs the user's vital sign data and outputs the content of the advice. This allows the providing unit to customize the content of the advice according to the user's physical condition and fatigue level.

[0077] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The providing unit can also analyze the user's voice to estimate the emotions. For example, the providing unit can analyze the tone and speed of the user's voice and calculate an emotion score. The providing unit also has a function for determining the priority of advice based on the user's emotions. For example, the providing unit can prioritize important advice when the user is excited. The providing unit can also provide detailed advice when the user is relaxed. This allows the providing unit to determine the priority of advice based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate the user's emotions, thereby enabling the providing unit to determine the priority of advice according to the user's emotions.

[0078] When providing advice, the providing unit can provide optimal advice taking into account the user's geographical location information. The providing unit, for example, uses GPS data to acquire the user's geographical location information. For example, the providing unit identifies the user's current location based on the GPS data and provides advice according to that location. Furthermore, if the user is in a specific training facility, the providing unit can provide advice appropriate to that facility. For example, if the user is practicing outdoors, the providing unit provides advice according to the weather and terrain. Furthermore, the providing unit has a function to suggest optimal advice based on the user's geographical location information. For example, if the user is practicing at home, the providing unit provides advice that can be performed indoors. This allows the providing unit to provide optimal advice based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that inputs the user's geographical location information and outputs optimal advice. This allows the providing unit to provide optimal advice based on the user's geographical location information.

[0079] When providing advice, the providing unit can analyze the user's social media activity and adjust the content of the advice. For example, the providing unit analyzes the user's social media activity by analyzing the content of posts, the number of likes, comments, etc. For example, the providing unit provides relevant advice based on the practice content shared by the user on social media. The providing unit can also provide advice based on the advice of trainers and athletes followed by the user. For example, the providing unit analyzes trends in online communities in which the user participates and provides relevant advice. Furthermore, the providing unit has a function of suggesting optimal advice based on the user's social media activity. For example, the providing unit analyzes the user's social media activity and suggests the most effective advice. This allows the providing unit to adjust the content of the advice based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the content of the advice using an AI model that inputs the user's social media activity data and outputs relevant advice. This allows the providing unit to adjust the content of the advice based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the recording unit, analysis unit, and provision 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 recording unit records the athlete's practice status using the camera 42 of the smart device 14 and saves the recorded data in the storage 32 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the recorded data to identify the athlete's running style and form. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and suggests training menus and meal menus to the athlete based on the analysis results. The provision unit can also visually display advice using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the recording unit, analysis unit, and provision 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 recording unit records the athlete's practice status using the camera 42 of the smart glasses 214 and saves the recorded data in the storage 32 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the recorded data to identify the athlete's running style and form. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and suggests training menus and meal menus to the athlete based on the analysis results. The provision unit can also visually display advice using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the recording unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the recording unit records the athlete's practice status using the camera 42 of the headset terminal 314 and saves the data in the storage 32 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the recorded data to identify the athlete's running style and form. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and suggests training menus and meal menus to the athlete based on the analysis results. The provision unit can also visually display advice using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the recording unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit records the athlete's practice status using the camera 42 of the robot 414 and saves the data in the storage 32 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the recorded data to identify the athlete's running style and form. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and suggests training menus and meal menus to the athlete based on the analysis results. The provision unit can also visually display advice using the display of the robot 414.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The providing unit can estimate the user's emotions and adjust the timing of advice based on the estimated user's emotions. For example, if the user is feeling stressed, advice can be provided at a time when the user can relax. Also, if the user is concentrating, the frequency of advice can be reduced to maintain the user's concentration. Furthermore, if the user is excited, proactive advice can be provided to increase the user's motivation. This allows the providing unit to adjust the timing of advice according to the user's emotions.

[0082] When analyzing a user's practice data, the analysis unit can adjust the analysis results by taking weather data into account. For example, it can analyze practice data in rainy weather separately from practice data in sunny weather. It can also correct the analysis results by taking environmental factors such as temperature and humidity into account. It can also evaluate performance under specific weather conditions and suggest appropriate training menus. This allows the analysis unit to analyze the user's practice data more accurately.

[0083] The providing unit can estimate the user's emotions and customize the content of advice based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide advice that gives a sense of security. If the user is feeling confident, the providing unit can suggest a challenging training menu. Furthermore, if the user is tired, the providing unit can provide advice that focuses on recovery. This allows the providing unit to customize the content of advice according to the user's emotions.

[0084] When recording the user's practice data, the recording unit can also record the ambient sounds of the practice location. For example, when practicing outdoors, the recording unit can record the sound of the wind and birds chirping. When practicing indoors, the recording unit can also record background sounds to recreate the practice environment. Furthermore, the recording unit can analyze the effect of specific sounds on practice and suggest the optimal practice environment. This allows the recording unit to record the user's practice data in more detail.

[0085] The providing unit can estimate the user's emotions and select the form of advice based on the estimated user's emotions. For example, if the user is relaxed, advice in video format can be provided. If the user is in a hurry, concise advice in text format can be provided. Furthermore, if the user prefers visual information, advice using graphs or charts can be provided. This allows the providing unit to select the form of advice according to the user's emotions.

[0086] When analyzing a user's practice data, the analysis unit can adjust the analysis results by taking the user's sleep data into account. For example, it can correct the practice data by taking into account the effects of sleep deprivation. It can also evaluate the impact of good quality sleep on performance and suggest an appropriate training menu. It can also analyze the user's sleep patterns and suggest optimal practice times. This allows the analysis unit to analyze the user's practice data more accurately.

[0087] The providing unit can estimate the user's emotions and adjust the frequency of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of advice can be reduced to reduce the burden. Also, if the user is highly motivated, the frequency of advice can be increased to strengthen support. Furthermore, if the user is relaxed, advice can be provided at an appropriate frequency. This allows the providing unit to adjust the frequency of advice according to the user's emotions.

[0088] The recording unit can simultaneously record the user's heart rate data when recording the user's practice data. For example, it can record heart rate fluctuations during practice and evaluate the intensity of the practice. It can also analyze the effectiveness of practice based on the heart rate data and suggest an appropriate training menu. It can also use the heart rate data to monitor the user's physical condition and adjust the practice menu as needed. This allows the recording unit to record the user's practice data in more detail.

[0089] When analyzing a user's practice data, the analysis unit can adjust the analysis results by taking the user's dietary data into account. For example, it can evaluate the impact that the intake of specific nutrients has on performance. It can also analyze the effectiveness of practice based on dietary data and suggest appropriate training menus. It can also analyze the user's eating patterns and suggest optimal meal timings. This allows the analysis unit to analyze the user's practice data more accurately.

[0090] The providing unit can estimate the user's emotion and adjust the tone of the advice based on the estimated user's emotion. For example, if the user is depressed, the advice can be provided in an encouraging tone. If the user is confident, the advice can be provided in a challenging tone. Furthermore, if the user is relaxed, the advice can be provided in a calm tone. This allows the providing unit to adjust the tone of the advice according to the user's emotion.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The recording unit records the athlete's daily training status using video or photographs. For example, the training status can be captured using a smartphone or camera and saved as digital data. The recording unit can also record the training status in real time. For example, the athlete's running status can be continuously captured and saved as data for later analysis. Step 2: The analysis unit analyzes the information recorded by the recording unit. This analysis includes, for example, image analysis algorithms and data preprocessing methods. The analysis unit analyzes the athlete's running style and form to identify areas for improvement. It can also analyze the athlete's muscle strength and physical condition and generate data to suggest training menus. Step 3: The provider provides advice based on the analysis results obtained by the analyzer. Advice may be provided in text, audio, or real-time formats, for example. The provider proposes specific training menus to the athlete. It can also analyze the athlete's nutritional status and provide appropriate meal menus. It can also suggest care methods for the athlete and support recovery after practice. For example, it can suggest stretching methods and recovery techniques. Some or all of the processing in the provider may be performed using AI, and it also has the ability to visually display advice. For example, advice may be displayed as a video or image, providing it in a format that is easy for the athlete to understand. It can also personalize advice to provide specific suggestions tailored to the athlete's characteristics and goals.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recording unit for recording videos or photos; an analysis unit that analyzes the information recorded by the recording unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The providing unit Providing muscle strengthening suggestions 2. The system of claim 1.

3. The providing unit Providing a meal menu 2. The system of claim 1.

4. The providing unit Make care suggestions 2. The system of claim 1.

5. The analysis unit Analyze a video or photo 2. The system of claim 1.

6. The analysis unit Conduct nutritional analysis 2. The system of claim 1.

7. The recording unit Estimate the user's emotions and adjust the timing of recording based on the estimated user emotions.

2. The system of claim 1.

8. The recording unit When recording, the appropriate recording method is selected by referring to the user's past practice history.

2. The system of claim 1.

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