System

Abstract

An object of a system according to an embodiment is to effectively manage a daily exercise amount and a calorie consumption of a user and provide appropriate advice.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a providing unit, and a collecting unit. The collection unit measures the daily exercise amount and the calorie consumption of the user. The analysis unit analyzes the data collected by the collection unit. The providing unit provides advice to the user based on the data analyzed by the analyzing unit. The collecting unit collects a plurality of user data by the collecting unit.SELECTED DRAWING: Figure 1

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 technologies have had the problem of not being able to effectively manage a user's daily exercise volume and calorie expenditure, and not being able to provide appropriate advice.

[0005] The system according to the embodiment aims to effectively manage the amount of daily exercise and calories burned of a user and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a collecting unit. The collection unit measures the user's daily exercise volume and calorie consumption. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice to the user based on the data analyzed by the analysis unit. The collecting unit collects multiple pieces of user data using the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively manage the amount of daily exercise and calories burned of the user and provide appropriate advice. [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 system according to an embodiment of the present invention links a smart device with a generation AI, measures a user's daily exercise volume and calorie consumption, collects more user data, and uses the generation AI to provide advice to help the user achieve greater heights. For example, a smart device measures a user's daily exercise volume and calorie consumption, and the generation AI analyzes the data. Based on the analysis results, the generation AI provides the user with advice to help them achieve greater heights. This allows the user to understand their daily exercise volume and calorie consumption and receive advice from the generation AI, enabling them to achieve greater heights. The system measures and analyzes the user's daily exercise volume and calorie consumption and provides advice, enabling the user to achieve greater heights. For example, the user can set specific goals for living a healthy life and work toward them. Following the advice provided by the generation AI can also help prevent insufficient or excessive exercise and maintain health.

[0029] An exercise support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a collection unit. The collection unit measures a user's daily exercise volume and calorie consumption. For example, the collection unit can measure the user's step count and heart rate using a smartwatch or fitness tracker and calculate the calorie consumption. The collection unit can also collect the user's exercise data and send it to a generation AI. The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit can analyze the user's exercise patterns and health status and detect signs of insufficient or excessive exercise. The analysis unit can also use the generation AI to evaluate the user's health status based on the exercise data. The provision unit uses the generation AI to provide advice to the user based on the data analyzed by the analysis unit. For example, the provision unit can use the generation AI to provide advice to the user such as "You should walk a little more today" or "You should exercise less today." The provision unit can also use the generation AI to suggest a future exercise plan to the user. As a result, the exercise support system according to the embodiment measures and analyzes the amount of exercise and calories burned by the user each day, and provides advice to the user, thereby enabling the user to aim for greater heights.

[0030] The collection unit can measure the user's daily exercise volume and calorie consumption using a smartwatch or fitness tracker. The smartwatch includes, for example, a heart rate measurement function and a GPS function. The fitness tracker includes, for example, a pedometer and a calorie calculation function. The collection unit, for example, uses a smartwatch to measure the user's steps and heart rate and calculate the calories consumed. The collection unit can also use a fitness tracker to measure the user's daily exercise volume and calculate the calories consumed. This allows the user's daily exercise volume and calories consumed to be accurately measured using a smartwatch or fitness tracker. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartwatch or fitness tracker into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the user's exercise pattern and health condition. The analysis unit, for example, analyzes the user's exercise pattern. For example, the analysis unit may analyze the frequency, intensity, and type of the user's exercise to identify the exercise pattern. The analysis unit can also analyze the user's health condition. For example, the analysis unit may analyze the user's heart rate, blood pressure, and weight to evaluate the health condition. The analysis unit can also use a generation AI to evaluate the health condition based on the user's exercise data. For example, the analysis unit may have the generation AI analyze the user's exercise data and detect signs of insufficient or excessive exercise. This allows the analysis of the user's exercise pattern and health condition to provide appropriate advice. 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 may input the user's exercise data into the generation AI and have the generation AI analyze the exercise pattern and health condition.

[0032] The providing unit can provide specific exercise advice to the user. The providing unit, for example, uses a generating AI to provide specific exercise advice to the user. For example, the providing unit can have the generating AI provide advice to the user such as "You should walk a little more today" or "You should exercise less today." The providing unit can also use the generating AI to suggest future exercise plans to the user. For example, the providing unit can have the generating AI provide advice to the user such as "Increase your jogging next week" or "Intensify your strength training next month." By providing specific exercise advice, the user can obtain guidelines to aim for higher levels. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input analysis results to the generating AI, which can then generate advice.

[0033] The collection unit can collect multiple user data and analyze common exercise patterns and health conditions. The collection unit, for example, collects a large amount of user data. For example, the collection unit can collect user data from multiple smart devices. The collection unit can also transmit the user data to the generation AI. The analysis unit uses the generation AI to analyze common exercise patterns and health conditions. For example, the analysis unit allows the generation AI to analyze a large amount of user data and find common exercise patterns and health conditions. By collecting a large amount of user data, more accurate advice can be provided. Some or all of the above-mentioned processing in the collection unit and analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from multiple smart devices into the generation AI, and the generation AI can analyze common exercise patterns and health conditions.

[0034] The providing unit can propose a future exercise plan. The providing unit, for example, uses a generating AI to propose a future exercise plan to the user. For example, the providing unit uses the generating AI to provide advice to the user, such as "Increase jogging next week" or "Intensify strength training next month." The providing unit can also set long-term exercise goals for the user, using the generating AI. For example, the providing unit uses the generating AI to provide advice to the user, such as "Try running a marathon in six months" or "Reduce your body fat percentage in one year." This makes it easier for the user to set long-term goals by proposing a future exercise plan. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input analysis results to the generating AI, which then proposes a future exercise plan.

[0035] The collection unit can analyze the user's past exercise history and select the optimal collection method. The collection unit, for example, analyzes the user's past exercise history. For example, the collection unit selects the optimal collection method based on the type and frequency of exercise the user has performed in the past. The collection unit can also collect exercise data for specific time periods from the user's past exercise history. The collection unit can also analyze the user's past exercise history and collect data for time periods when exercise is most effective. In this way, the optimal collection method can be selected by analyzing the user's past exercise history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past exercise history data into a generation AI, which can select the optimal collection method.

[0036] When collecting exercise data, the collection unit can filter the data based on the user's current health condition and lifestyle. The collection unit, for example, filters the data based on the user's current health condition. For example, the collection unit selects data to be collected based on the user's heart rate and blood pressure. The collection unit can also filter the data taking into account the user's lifestyle. For example, the collection unit selects data to be collected taking into account the user's work schedule and sleep patterns. The collection unit can also adjust the type and frequency of data to be collected depending on the user's health condition and lifestyle. This allows for more accurate data to be collected by filtering data according to the user's health condition and lifestyle. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and lifestyle to a generation AI, which then filters the data.

[0037] When collecting exercise data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects the exercise data using voice recognition technology. For example, the collection unit converts the user's voice into text data using voice recognition software. Alternatively, if the user uses text input, the collection unit can also collect the exercise data using text analysis technology. For example, the collection unit analyzes the text data entered by the user and saves it as exercise data. Alternatively, if the user uses image input, the collection unit can also collect the exercise data using image recognition technology. For example, the collection unit analyzes images taken by the user and saves them as exercise data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into a generation AI, which then selects the optimal collection means.

[0038] When collecting exercise data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data based on the user's geographical location information. For example, the collection unit identifies the user's current location using GPS data and collects highly relevant exercise data. The collection unit can also prioritize collecting outdoor exercise data when the user is in a park. The collection unit can also prioritize collecting gym exercise data when the user is in a gym. The collection unit can also prioritize collecting home exercise data when the user is at home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to a generation AI, which can then prioritize collecting highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting exercise data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit collects exercise data shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related exercise data. The collection unit can also collect related exercise data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI, which then collects related data.

[0040] When collecting exercise data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the collection method based on, for example, the user's past feedback. For example, the collection unit adjusts the type and frequency of data to be collected based on feedback provided by the user in the past. The collection unit can also preferentially collect specific exercise data based on the user's past feedback. The collection unit can also adjust the type and frequency of data to be collected by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI, which can customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data during analysis. The analysis unit, for example, evaluates the importance of the exercise data. For example, the analysis unit evaluates the importance of the exercise data based on the impact and urgency of the data. The analysis unit also adjusts the level of detail of the analysis based on the importance of the exercise data. For example, the analysis unit performs a detailed analysis on important exercise data. The analysis unit can also perform a concise analysis on less important exercise data. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the exercise data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the exercise data to a generation AI, which can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of exercise data. The analysis unit, for example, classifies the category of exercise data. For example, the analysis unit classifies the type of exercise or health condition. The analysis unit also applies different analysis algorithms depending on the category of exercise data. For example, the analysis unit applies a specific analysis algorithm to aerobic exercise data. The analysis unit can also apply a different analysis algorithm to strength training data. The analysis unit can also apply even different analysis algorithms to yoga and stretching data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of exercise data to a generation AI, which then applies different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also find specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the exercise data. The analysis unit, for example, evaluates the time of submission of the exercise data. For example, the analysis unit evaluates the time of submission of the exercise data based on the freshness and urgency of the data. The analysis unit also determines the priority of analysis based on the time of submission of the exercise data. For example, the analysis unit prioritizes analysis of the most recent exercise data. The analysis unit can also analyze older exercise data later. The analysis unit can also gradually adjust the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of the exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the exercise data to the generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the exercise data. The analysis unit, for example, evaluates the relevance of the exercise data. For example, the analysis unit evaluates the relevance of the exercise data based on data correlations or common characteristics. The analysis unit also adjusts the order of analysis based on the relevance of the exercise data. For example, the analysis unit prioritizes analysis of highly relevant exercise data. The analysis unit can also postpone analysis of less relevant exercise data. The analysis unit can also gradually adjust the order of analysis based on the relevance of the exercise data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the exercise data to a generation AI, which can then adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation, educational background, and experience. The analysis unit also adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into a generation AI, which then adjusts the use of technical terms in the analysis.

[0047] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the exercise data. The providing unit, for example, evaluates the importance of the exercise data. For example, the providing unit evaluates the importance of the exercise data based on the impact and urgency of the data. The providing unit also adjusts the level of detail of the advice based on the importance of the exercise data. For example, the providing unit provides detailed advice for important exercise data. The providing unit can also provide concise advice for less important exercise data. The providing unit can also gradually adjust the level of detail of the advice according to the importance of the exercise data. This enables efficient advice by adjusting the level of detail of the advice according to the importance of the exercise data. 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 input the importance of the exercise data to a generating AI, which can adjust the level of detail of the advice.

[0048] When providing advice, the providing unit can apply different advice algorithms depending on the category of exercise data. The providing unit, for example, classifies the category of exercise data. For example, the providing unit classifies the type of exercise and health condition. The providing unit also applies different advice algorithms depending on the category of exercise data. For example, the providing unit applies a specific advice algorithm to aerobic exercise data. The providing unit can also apply a different advice algorithm to strength training data. The providing unit can also apply even different advice algorithms to yoga and stretching data. In this way, applying different advice algorithms depending on the category of exercise data enables more accurate advice. Some or all of the above-mentioned 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 input the category of exercise data to a generation AI, which then applies different advice algorithms.

[0049] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, refers to the user's past advice results. For example, the providing unit improves the accuracy of current advice based on the user's past advice results. The providing unit can also find specific patterns from the user's past advice results and reflect them in the advice. The providing unit can also adjust the advice algorithm by referring to the user's past advice results. In this way, the accuracy of advice is improved by referring to the user's past advice results. 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 input the user's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0050] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the exercise data. The providing unit, for example, evaluates the time of submission of the exercise data. For example, the providing unit evaluates the time of submission of the exercise data based on the freshness and urgency of the data. The providing unit also determines the priority of advice based on the time of submission of the exercise data. For example, the providing unit provides advice based on the latest exercise data. The providing unit can also provide advice by delaying the submission of exercise data that was submitted earlier. The providing unit can also gradually adjust the priority of advice based on the time of submission. This enables efficient advice by determining the priority of advice based on the time of submission of the exercise data. Some or all of the above-mentioned 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 input the time of submission of the exercise data to a generating AI, which can then determine the priority of advice.

[0051] When providing advice, the providing unit can adjust the order of advice based on the relevance of the exercise data. The providing unit, for example, evaluates the relevance of the exercise data. For example, the providing unit evaluates the relevance of the exercise data based on data correlations or common characteristics. The providing unit also adjusts the order of advice based on the relevance of the exercise data. For example, the providing unit provides advice based on highly relevant exercise data. The providing unit can also provide advice after less relevant exercise data. The providing unit can also gradually adjust the order of advice based on the relevance of the exercise data. This enables efficient advice by adjusting the order of advice based on the relevance of the exercise data. Some or all of the above-mentioned 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 input the relevance of the exercise data to a generating AI, which can adjust the order of advice.

[0052] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise. For example, the providing unit evaluates the user's level of expertise based on the user's occupation, educational background, and experience. The providing unit also adjusts the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, the providing unit provides advice that uses a lot of technical terms. If the user does not have technical expertise, the providing unit can also provide concise and easy-to-understand advice. The providing unit can also adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easier to understand. 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 input the user's level of expertise to a generation AI, which can adjust the use of technical terms in the advice.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When analyzing the user's exercise data, the analysis unit can also analyze the user's dietary data. For example, the analysis unit collects data on the user's calorie and nutrient intake and analyzes it in combination with the exercise data. The analysis unit can also analyze the user's eating patterns and provide dietary advice to maximize the effects of exercise. Furthermore, the analysis unit can suggest meals suitable for post-exercise recovery based on the user's dietary data. This allows the user to manage their health from both an exercise and diet perspective.

[0055] The provider can implement a reward system to maintain the user's motivation based on the user's exercise data. For example, the provider can award badges or points when the user achieves the exercise goal they set. The provider can also provide special offers or discount coupons when the user accumulates a certain number of points. Furthermore, the provider can provide a function to compete with friends and family based on the user's exercise data to increase motivation. This allows the user to continue exercising while having fun.

[0056] The collection unit may also collect the user's sleep data when collecting the user's exercise data. For example, the collection unit may use a smartwatch or fitness tracker to measure the user's sleep time and sleep quality. The collection unit may also adjust the timing and intensity of exercise based on the user's sleep data. Furthermore, the collection unit may analyze the user's sleep data and provide advice on improving sleep quality. This allows the user to manage their health in terms of both exercise and sleep.

[0057] The providing unit can monitor the user's health condition based on the user's exercise data and issue an alert if an abnormality is detected. For example, if the user's heart rate or blood pressure indicates an abnormal value, the providing unit can issue an alert recommending that the user see a doctor. The providing unit can also predict the risk of injury during exercise based on the user's exercise data and suggest preventive measures. Furthermore, the providing unit can regularly check the user's health condition to check for any abnormalities. This allows the user to detect health risks early and take appropriate measures.

[0058] When analyzing the user's exercise data, the analysis unit can also analyze the user's lifestyle data. For example, the analysis unit can adjust the timing and intensity of exercise taking into account the user's work schedule and home situation. The analysis unit can also provide advice to maximize the effectiveness of exercise based on the user's lifestyle data. Furthermore, the analysis unit can analyze the user's lifestyle data and make suggestions for balancing exercise and life. This allows the user to create an exercise plan that suits their lifestyle.

[0059] When analyzing the user's exercise data, the analysis unit can build a predictive model based on the user's past exercise data and predict future exercise performance. For example, the analysis unit can analyze the user's past exercise data to identify trends in exercise performance. The analysis unit can also use the predictive model to evaluate the user's likelihood of achieving goals set by the user. Furthermore, the analysis unit can provide advice for optimizing the user's exercise plan based on the predictive model. This allows the user to predict future exercise performance and create an effective exercise plan.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit measures the user's daily exercise volume and calories burned. For example, the collection unit can use a smartwatch or fitness tracker to measure the user's steps and heart rate and calculate calories burned. The collection unit can also collect the user's exercise data and send it to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit can analyze the user's exercise patterns and health condition to detect signs of insufficient or excessive exercise. The analysis unit can also use the generation AI to evaluate the user's health condition based on the user's exercise data. Step 3: The providing unit uses the generating AI to provide advice to the user based on the data analyzed by the analyzing unit. For example, the providing unit can provide advice to the user such as "You should walk a little more today" or "You should exercise less today." The providing unit can also use the generating AI to suggest future exercise plans to the user. Step 4: The collection unit collects data from multiple users. This allows the system to centrally manage the exercise data of multiple users, improving the accuracy of analysis and advice.

[0062] (Example 2) A system according to an embodiment of the present invention links a smart device with a generation AI, measures a user's daily exercise volume and calorie consumption, collects more user data, and uses the generation AI to provide advice to help the user achieve greater heights. For example, a smart device measures a user's daily exercise volume and calorie consumption, and the generation AI analyzes the data. Based on the analysis results, the generation AI provides the user with advice to help them achieve greater heights. This allows the user to understand their daily exercise volume and calorie consumption and receive advice from the generation AI, enabling them to achieve greater heights. The system measures and analyzes the user's daily exercise volume and calorie consumption and provides advice, enabling the user to achieve greater heights. For example, the user can set specific goals for living a healthy life and work toward them. Following the advice provided by the generation AI can also help prevent insufficient or excessive exercise and maintain health.

[0063] An exercise support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a collection unit. The collection unit measures a user's daily exercise volume and calorie consumption. For example, the collection unit can measure the user's step count and heart rate using a smartwatch or fitness tracker and calculate the calorie consumption. The collection unit can also collect the user's exercise data and send it to a generation AI. The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit can analyze the user's exercise patterns and health status and detect signs of insufficient or excessive exercise. The analysis unit can also use the generation AI to evaluate the user's health status based on the exercise data. The provision unit uses the generation AI to provide advice to the user based on the data analyzed by the analysis unit. For example, the provision unit can use the generation AI to provide advice to the user such as "You should walk a little more today" or "You should exercise less today." The provision unit can also use the generation AI to suggest a future exercise plan to the user. As a result, the exercise support system according to the embodiment measures and analyzes the amount of exercise and calories burned by the user each day, and provides advice to the user, thereby enabling the user to aim for greater heights.

[0064] The collection unit can measure the user's daily exercise volume and calorie consumption using a smartwatch or fitness tracker. The smartwatch includes, for example, a heart rate measurement function and a GPS function. The fitness tracker includes, for example, a pedometer and a calorie calculation function. The collection unit, for example, uses a smartwatch to measure the user's steps and heart rate and calculate the calories consumed. The collection unit can also use a fitness tracker to measure the user's daily exercise volume and calculate the calories consumed. This allows the user's daily exercise volume and calories consumed to be accurately measured using a smartwatch or fitness tracker. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartwatch or fitness tracker into a generation AI and have the generation AI analyze the data.

[0065] The analysis unit can analyze the user's exercise pattern and health condition. The analysis unit, for example, analyzes the user's exercise pattern. For example, the analysis unit may analyze the frequency, intensity, and type of the user's exercise to identify the exercise pattern. The analysis unit can also analyze the user's health condition. For example, the analysis unit may analyze the user's heart rate, blood pressure, and weight to evaluate the health condition. The analysis unit can also use a generation AI to evaluate the health condition based on the user's exercise data. For example, the analysis unit may have the generation AI analyze the user's exercise data and detect signs of insufficient or excessive exercise. This allows the analysis of the user's exercise pattern and health condition to provide appropriate advice. 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 may input the user's exercise data into the generation AI and have the generation AI analyze the exercise pattern and health condition.

[0066] The providing unit can provide specific exercise advice to the user. The providing unit, for example, uses a generating AI to provide specific exercise advice to the user. For example, the providing unit can have the generating AI provide advice to the user such as "You should walk a little more today" or "You should exercise less today." The providing unit can also use the generating AI to suggest future exercise plans to the user. For example, the providing unit can have the generating AI provide advice to the user such as "Increase your jogging next week" or "Intensify your strength training next month." By providing specific exercise advice, the user can obtain guidelines to aim for higher levels. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input analysis results to the generating AI, which can then generate advice.

[0067] The collection unit can collect multiple user data and analyze common exercise patterns and health conditions. The collection unit, for example, collects a large amount of user data. For example, the collection unit can collect user data from multiple smart devices. The collection unit can also transmit the user data to the generation AI. The analysis unit uses the generation AI to analyze common exercise patterns and health conditions. For example, the analysis unit allows the generation AI to analyze a large amount of user data and find common exercise patterns and health conditions. By collecting a large amount of user data, more accurate advice can be provided. Some or all of the above-mentioned processing in the collection unit and analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data obtained from multiple smart devices into the generation AI, and the generation AI can analyze common exercise patterns and health conditions.

[0068] The providing unit can propose a future exercise plan. The providing unit, for example, uses a generating AI to propose a future exercise plan to the user. For example, the providing unit uses the generating AI to provide advice to the user, such as "Increase jogging next week" or "Intensify strength training next month." The providing unit can also set long-term exercise goals for the user, using the generating AI. For example, the providing unit uses the generating AI to provide advice to the user, such as "Try running a marathon in six months" or "Reduce your body fat percentage in one year." This makes it easier for the user to set long-term goals by proposing a future exercise plan. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input analysis results to the generating AI, which then proposes a future exercise plan.

[0069] The collection unit can estimate the user's emotions and adjust the timing of collecting exercise data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The collection unit can also adjust the timing of collecting exercise data based on the user's emotions. For example, if the user is feeling stressed, the collection unit collects exercise data during times when the user is relaxing. If the user is relaxed, the collection unit can also collect exercise data in real time. If the user is tired, the collection unit can collect data during resting periods and adjust the exercise load. This allows for more appropriate data collection by adjusting the timing of collecting exercise data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI, and the generation AI may adjust the collection timing based on the emotion.

[0070] The collection unit can analyze the user's past exercise history and select the optimal collection method. The collection unit, for example, analyzes the user's past exercise history. For example, the collection unit selects the optimal collection method based on the type and frequency of exercise the user has performed in the past. The collection unit can also collect exercise data for specific time periods from the user's past exercise history. The collection unit can also analyze the user's past exercise history and collect data for time periods when exercise is most effective. In this way, the optimal collection method can be selected by analyzing the user's past exercise history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past exercise history data into a generation AI, which can select the optimal collection method.

[0071] When collecting exercise data, the collection unit can filter the data based on the user's current health condition and lifestyle. The collection unit, for example, filters the data based on the user's current health condition. For example, the collection unit selects data to be collected based on the user's heart rate and blood pressure. The collection unit can also filter the data taking into account the user's lifestyle. For example, the collection unit selects data to be collected taking into account the user's work schedule and sleep patterns. The collection unit can also adjust the type and frequency of data to be collected depending on the user's health condition and lifestyle. This allows for more accurate data to be collected by filtering data according to the user's health condition and lifestyle. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and lifestyle to a generation AI, which then filters the data.

[0072] When collecting exercise data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects the exercise data using voice recognition technology. For example, the collection unit converts the user's voice into text data using voice recognition software. Alternatively, if the user uses text input, the collection unit can also collect the exercise data using text analysis technology. For example, the collection unit analyzes the text data entered by the user and saves it as exercise data. Alternatively, if the user uses image input, the collection unit can also collect the exercise data using image recognition technology. For example, the collection unit analyzes images taken by the user and saves them as exercise data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into a generation AI, which then selects the optimal collection means.

[0073] The collection unit can estimate the user's emotions and prioritize the exercise data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The collection unit can also prioritize the exercise data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting exercise data that has a relaxing effect. If the user is relaxed, the collection unit can prioritize collecting data with high exercise intensity. If the user is tired, the collection unit can prioritize collecting light exercise data. In this way, by prioritizing the exercise data according to the user's emotions, more important data can be collected preferentially. 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI, and the generation AI may determine the priority of the exercise data to be collected based on the emotion.

[0074] When collecting exercise data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data based on the user's geographical location information. For example, the collection unit identifies the user's current location using GPS data and collects highly relevant exercise data. The collection unit can also prioritize collecting outdoor exercise data when the user is in a park. The collection unit can also prioritize collecting gym exercise data when the user is in a gym. The collection unit can also prioritize collecting home exercise data when the user is at home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to a generation AI, which can then prioritize collecting highly relevant data.

[0075] The collection unit can analyze the user's social media activities and collect related data when collecting exercise data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit collects exercise data shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related exercise data. The collection unit can also collect related exercise data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI, which then collects related data.

[0076] When collecting exercise data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the collection method based on, for example, the user's past feedback. For example, the collection unit adjusts the type and frequency of data to be collected based on feedback provided by the user in the past. The collection unit can also preferentially collect specific exercise data based on the user's past feedback. The collection unit can also adjust the type and frequency of data to be collected by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI, which can customize the collection method.

[0077] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotion. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI, and the generation AI may adjust the method of expression of the analysis based on the emotion.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data during analysis. The analysis unit, for example, evaluates the importance of the exercise data. For example, the analysis unit evaluates the importance of the exercise data based on the impact and urgency of the data. The analysis unit also adjusts the level of detail of the analysis based on the importance of the exercise data. For example, the analysis unit performs a detailed analysis on important exercise data. The analysis unit can also perform a concise analysis on less important exercise data. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the exercise data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the exercise data to a generation AI, which can adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of exercise data. The analysis unit, for example, classifies the category of exercise data. For example, the analysis unit classifies the type of exercise or health condition. The analysis unit also applies different analysis algorithms depending on the category of exercise data. For example, the analysis unit applies a specific analysis algorithm to aerobic exercise data. The analysis unit can also apply a different analysis algorithm to strength training data. The analysis unit can also apply even different analysis algorithms to yoga and stretching data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of exercise data to a generation AI, which then applies different analysis algorithms.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also find specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotion. The analysis unit can also adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. In this way, by adjusting the length of the analysis according to the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI, and the generation AI may adjust the length of analysis based on the emotion.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the exercise data. The analysis unit, for example, evaluates the time of submission of the exercise data. For example, the analysis unit evaluates the time of submission of the exercise data based on the freshness and urgency of the data. The analysis unit also determines the priority of analysis based on the time of submission of the exercise data. For example, the analysis unit prioritizes analysis of the most recent exercise data. The analysis unit can also analyze older exercise data later. The analysis unit can also gradually adjust the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of the exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the exercise data to the generation AI, and the generation AI can determine the priority of analysis.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the exercise data. The analysis unit, for example, evaluates the relevance of the exercise data. For example, the analysis unit evaluates the relevance of the exercise data based on data correlations or common characteristics. The analysis unit also adjusts the order of analysis based on the relevance of the exercise data. For example, the analysis unit prioritizes analysis of highly relevant exercise data. The analysis unit can also postpone analysis of less relevant exercise data. The analysis unit can also gradually adjust the order of analysis based on the relevance of the exercise data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the exercise data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the exercise data to a generation AI, which can then adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation, educational background, and experience. The analysis unit also adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into a generation AI, which then adjusts the use of technical terms in the analysis.

[0085] The providing unit can estimate the user's emotion and adjust the way the advice is presented based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. The providing unit can also adjust the way the advice is presented based on the estimated user's emotion. For example, the providing unit can provide detailed advice when the user is relaxed. The providing unit can also provide concise advice when the user is in a hurry. The providing unit can also provide visually stimulating advice when the user is excited. This allows the system to provide more appropriate advice by adjusting the way the advice is presented based on the user's emotion. 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI, and the generating AI may adjust the way the advice is presented based on the emotion.

[0086] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the exercise data. The providing unit, for example, evaluates the importance of the exercise data. For example, the providing unit evaluates the importance of the exercise data based on the impact and urgency of the data. The providing unit also adjusts the level of detail of the advice based on the importance of the exercise data. For example, the providing unit provides detailed advice for important exercise data. The providing unit can also provide concise advice for less important exercise data. The providing unit can also gradually adjust the level of detail of the advice according to the importance of the exercise data. This enables efficient advice by adjusting the level of detail of the advice according to the importance of the exercise data. 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 input the importance of the exercise data to a generating AI, which can adjust the level of detail of the advice.

[0087] When providing advice, the providing unit can apply different advice algorithms depending on the category of exercise data. The providing unit, for example, classifies the category of exercise data. For example, the providing unit classifies the type of exercise and health condition. The providing unit also applies different advice algorithms depending on the category of exercise data. For example, the providing unit applies a specific advice algorithm to aerobic exercise data. The providing unit can also apply a different advice algorithm to strength training data. The providing unit can also apply even different advice algorithms to yoga and stretching data. In this way, applying different advice algorithms depending on the category of exercise data enables more accurate advice. Some or all of the above-mentioned 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 input the category of exercise data to a generation AI, which then applies different advice algorithms.

[0088] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, refers to the user's past advice results. For example, the providing unit improves the accuracy of current advice based on the user's past advice results. The providing unit can also find specific patterns from the user's past advice results and reflect them in the advice. The providing unit can also adjust the advice algorithm by referring to the user's past advice results. In this way, the accuracy of advice is improved by referring to the user's past advice results. 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 input the user's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0089] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. The providing unit can also adjust the length of the advice based on the estimated user's emotion. For example, the providing unit can provide short and to-the-point advice when the user is in a hurry. The providing unit can also provide detailed advice when the user is relaxed. The providing unit can also provide visually stimulating advice when the user is excited. This allows the length of the advice to be adjusted according to the user's emotion, thereby providing more appropriate advice. 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI, and the generating AI may adjust the length of advice based on the emotion.

[0090] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the exercise data. The providing unit, for example, evaluates the time of submission of the exercise data. For example, the providing unit evaluates the time of submission of the exercise data based on the freshness and urgency of the data. The providing unit also determines the priority of advice based on the time of submission of the exercise data. For example, the providing unit provides advice based on the latest exercise data. The providing unit can also provide advice by delaying the submission of exercise data that was submitted earlier. The providing unit can also gradually adjust the priority of advice based on the time of submission. This enables efficient advice by determining the priority of advice based on the time of submission of the exercise data. Some or all of the above-mentioned 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 input the time of submission of the exercise data to a generating AI, which can then determine the priority of advice.

[0091] When providing advice, the providing unit can adjust the order of advice based on the relevance of the exercise data. The providing unit, for example, evaluates the relevance of the exercise data. For example, the providing unit evaluates the relevance of the exercise data based on data correlations or common characteristics. The providing unit also adjusts the order of advice based on the relevance of the exercise data. For example, the providing unit provides advice based on highly relevant exercise data. The providing unit can also provide advice after less relevant exercise data. The providing unit can also gradually adjust the order of advice based on the relevance of the exercise data. This enables efficient advice by adjusting the order of advice based on the relevance of the exercise data. Some or all of the above-mentioned 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 input the relevance of the exercise data to a generating AI, which can adjust the order of advice.

[0092] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise. For example, the providing unit evaluates the user's level of expertise based on the user's occupation, educational background, and experience. The providing unit also adjusts the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, the providing unit provides advice that uses a lot of technical terms. If the user does not have technical expertise, the providing unit can also provide concise and easy-to-understand advice. The providing unit can also adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easier to understand. 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 input the user's level of expertise to a generation AI, which can adjust the use of technical terms in the advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection 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 collection unit measures the user's daily exercise amount and calorie consumption using the computer 36 and camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI provides advice to the user based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection 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 collection unit measures the user's daily exercise amount and calorie consumption using the computer 36 and camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI provides advice to the user based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit measures the user's daily exercise amount and calorie consumption using the computer 36 and camera 42 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI provides advice to the user based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection 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 collection unit measures the user's daily exercise amount and calorie consumption using the computer 36 and camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI provides advice to the user based on the analysis results.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When analyzing the user's exercise data, the analysis unit can also analyze the user's dietary data. For example, the analysis unit collects data on the user's calorie and nutrient intake and analyzes it in combination with the exercise data. The analysis unit can also analyze the user's eating patterns and provide dietary advice to maximize the effects of exercise. Furthermore, the analysis unit can suggest meals suitable for post-exercise recovery based on the user's dietary data. This allows the user to manage their health from both an exercise and diet perspective.

[0095] The provider can implement a reward system to maintain the user's motivation based on the user's exercise data. For example, the provider can award badges or points when the user achieves the exercise goal they set. The provider can also provide special offers or discount coupons when the user accumulates a certain number of points. Furthermore, the provider can provide a function to compete with friends and family based on the user's exercise data to increase motivation. This allows the user to continue exercising while having fun.

[0096] The collection unit may also collect the user's sleep data when collecting the user's exercise data. For example, the collection unit may use a smartwatch or fitness tracker to measure the user's sleep time and sleep quality. The collection unit may also adjust the timing and intensity of exercise based on the user's sleep data. Furthermore, the collection unit may analyze the user's sleep data and provide advice on improving sleep quality. This allows the user to manage their health in terms of both exercise and sleep.

[0097] The providing unit can estimate the user's emotions and adjust the content of the exercise advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can suggest an exercise that has a relaxing effect. Also, if the user is relaxed, the providing unit can suggest a challenging exercise. Furthermore, if the user is tired, the providing unit can suggest light exercise or stretching. This makes it possible to provide appropriate exercise advice according to the user's emotions.

[0098] The analysis unit can take the user's stress level into consideration when analyzing the user's exercise data. For example, the analysis unit can estimate the user's stress level based on the user's heart rate and breathing rate and adjust the intensity and frequency of exercise. If the user's stress level is high, the analysis unit can also suggest exercise that has a relaxing effect. Furthermore, the analysis unit can provide advice to reduce the user's stress level. This allows the user to continue exercising while managing stress.

[0099] The providing unit can monitor the user's health condition based on the user's exercise data and issue an alert if an abnormality is detected. For example, if the user's heart rate or blood pressure indicates an abnormal value, the providing unit can issue an alert recommending that the user see a doctor. The providing unit can also predict the risk of injury during exercise based on the user's exercise data and suggest preventive measures. Furthermore, the providing unit can regularly check the user's health condition to check for any abnormalities. This allows the user to detect health risks early and take appropriate measures.

[0100] The collection unit can estimate the user's emotions and customize the method of collecting exercise data based on the estimated user's emotions. For example, the collection unit can reduce the frequency of exercise data collection when the user is feeling stressed. Alternatively, the collection unit can increase the frequency of exercise data collection when the user is relaxed. Furthermore, the collection unit can adjust the type of data to be collected depending on the user's emotions. This enables appropriate data collection according to the user's emotions.

[0101] When analyzing the user's exercise data, the analysis unit can also analyze the user's lifestyle data. For example, the analysis unit can adjust the timing and intensity of exercise taking into account the user's work schedule and home situation. The analysis unit can also provide advice to maximize the effectiveness of exercise based on the user's lifestyle data. Furthermore, the analysis unit can analyze the user's lifestyle data and make suggestions for balancing exercise and life. This allows the user to create an exercise plan that suits their lifestyle.

[0102] The providing unit can estimate the user's emotions and adjust the timing of exercise advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice during a relaxed time period. Also, if the user is relaxed, the providing unit can provide advice before exercise. Furthermore, if the user is tired, the providing unit can provide advice after resting. This makes it possible to provide exercise advice at appropriate timing according to the user's emotions.

[0103] When analyzing the user's exercise data, the analysis unit can build a predictive model based on the user's past exercise data and predict future exercise performance. For example, the analysis unit can analyze the user's past exercise data to identify trends in exercise performance. The analysis unit can also use the predictive model to evaluate the user's likelihood of achieving goals set by the user. Furthermore, the analysis unit can provide advice for optimizing the user's exercise plan based on the predictive model. This allows the user to predict future exercise performance and create an effective exercise plan.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit measures the user's daily exercise volume and calories burned. For example, the collection unit can use a smartwatch or fitness tracker to measure the user's steps and heart rate and calculate calories burned. The collection unit can also collect the user's exercise data and send it to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit can analyze the user's exercise patterns and health condition to detect signs of insufficient or excessive exercise. The analysis unit can also use the generation AI to evaluate the user's health condition based on the user's exercise data. Step 3: The providing unit uses the generating AI to provide advice to the user based on the data analyzed by the analyzing unit. For example, the providing unit can provide advice to the user such as "You should walk a little more today" or "You should exercise less today." The providing unit can also use the generating AI to suggest future exercise plans to the user. Step 4: The collection unit collects data from multiple users. This allows the system to centrally manage the exercise data of multiple users, improving the accuracy of analysis and advice.

[0106] 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.

[0107] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 collection unit that measures the user's daily exercise amount and calories burned; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides advice to a user based on the data analyzed by the analyzing unit; a unit for collecting a plurality of user data by the collection unit; A system characterized by:

2. The collecting unit Using a smartwatch or fitness tracker to measure the user's daily exercise and calorie consumption 2. The system of claim 1.

3. The analysis unit Analyzing users' exercise patterns and health conditions 2. The system of claim 1.

4. The providing unit Providing specific exercise advice to users 2. The system of claim 1.

5. The collecting unit Collect data from multiple users and analyze common exercise patterns and health conditions 2. The system of claim 1.

6. The providing unit Propose future exercise plans 2. The system of claim 1.

7. The collecting unit The user's emotions are estimated, and the timing of collecting exercise data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past exercise history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit When collecting exercise data, it filters it based on the user's current health and lifestyle.

2. The system of claim 1.

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