system

Abstract

The system according to this embodiment aims to provide appropriate recommendations based on the user's daily activities and health data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects the user's daily activity and physical condition data. The analysis unit analyzes the data collected by the data collection unit and determines the user's fatigue level and physical condition. The suggestion unit provides comprehensive recommendations based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, providing appropriate recommendations based on a user's daily activities and physical condition data has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate recommendations based on a user's daily activities and physical condition data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data on the user's daily activities and physical condition. The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. The suggestion unit provides comprehensive recommendations based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate recommendations based on the user's daily activities and health data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The fatigue recovery suggestion system according to an embodiment of the present invention is a system that collects and analyzes a user's daily activity and physical condition data and makes specific suggestions for fatigue recovery at the appropriate time. The fatigue recovery suggestion system collects and analyzes the user's daily activity and physical condition data to determine the user's fatigue level and physical condition. Based on the analysis results, it provides comprehensive recommendations such as diet, exercise, meditation, reading, travel, and creative writing. For example, the fatigue recovery suggestion system collects the user's daily activity and physical condition data. In this case, data such as steps, heart rate, sleep time, diet, stress level, and activity level are collected using a smartphone or wearable device. For example, if the user is wearing a smartwatch, data such as steps, heart rate, and sleep time are automatically collected. Next, the fatigue recovery suggestion system analyzes the collected data. The AI ​​determines the user's fatigue level and physical condition based on the collected data. For example, if the user's heart rate is high and sleep time is short, it is determined that the fatigue level is high. This makes it possible to understand the user's current physical condition and fatigue level. Next, the fatigue recovery suggestion system provides comprehensive recommendations based on the analysis results. For example, if a user is highly fatigued, suggestions such as meditation, light exercise, or a balanced diet will be made to help them relax. Suggestions for activities like reading, creative writing, or travel will also be made based on the user's interests. This allows users to recover from fatigue in a way that suits them. For instance, if a user is tired from work, the AI ​​will suggest meditation or light exercise to help them refresh. It will also suggest activities related to the user's hobbies and interests to boost their motivation and promote fatigue recovery. In this way, the fatigue recovery suggestion system collects and analyzes the user's daily activity and physical condition data, enabling it to provide specific suggestions for fatigue recovery at the appropriate time.

[0029] The fatigue recovery suggestion system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects the user's daily activity and physical condition data. The data collection unit collects data such as steps, heart rate, sleep duration, diet, stress level, and activity level using, for example, a smartphone or wearable device. For example, if the user is wearing a smartwatch, the data collection unit automatically collects data such as steps, heart rate, and sleep duration. The data collection unit can also collect data such as diet and stress level manually entered by the user through a smartphone application. Furthermore, the data collection unit can transmit data acquired from wearable devices to the cloud for analysis by the analysis unit. The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. The analysis unit uses, for example, AI to determine the user's fatigue level and physical condition based on the collected data. For example, the analysis unit determines that the user's fatigue level is high if their heart rate is high and their sleep duration is short. The analysis unit can also determine that the user's fatigue level is high if their activity level is high. Furthermore, the analysis unit can comprehensively determine the user's fatigue level and physical condition by considering the user's diet and stress level. The suggestion unit provides comprehensive recommendations based on the analysis results obtained by the analysis unit. For example, if the user's fatigue level is high, the suggestion unit may suggest meditation, light exercise, or a nutritionally balanced meal to help them relax. The suggestion unit can also suggest activities such as reading, creative writing, or travel, depending on the user's interests. In addition, the suggestion unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time. As a result, the fatigue recovery suggestion system according to this embodiment can collect and analyze the user's daily activities and physical condition data and make specific suggestions for fatigue recovery at the appropriate time.

[0030] The data collection unit collects data on the user's daily activities and physical condition. For example, it uses smartphones and wearable devices to collect data such as steps, heart rate, sleep duration, diet, stress levels, and activity levels. Specifically, for users wearing smartwatches, data such as steps, heart rate, and sleep duration are automatically collected. Smartwatches use built-in accelerometers and heart rate sensors to monitor the user's movements and heart rate variability in real time. This allows for detailed recording of the user's daily activity level, exercise intensity, and rest quality. The data collection unit can also collect data on diet and stress levels manually entered by the user through smartphone applications. For example, when a user enters their diet into the app, that information is sent to the cloud and integrated with other data. Furthermore, the data collection unit can send data acquired from wearable devices to the cloud for analysis by the analysis unit. On the cloud, data is centrally managed and stored in a format easily accessible to the analysis unit. This allows the data collection unit to collect a wide range of data from various devices and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. For example, the analysis unit uses AI to determine the user's fatigue level and physical condition based on the collected data. Specifically, the AI ​​uses machine learning algorithms to analyze data such as the user's heart rate, sleep duration, activity level, diet, and stress level. For example, if the user's heart rate is high and their sleep duration is short, the AI ​​will determine that they are fatigued. Similarly, if the user's activity level is high, the AI ​​can also determine that they are fatigued. Furthermore, the analysis unit can take into account the user's diet and stress level to make a comprehensive determination of fatigue level and physical condition. For example, if the user has consumed a high-calorie meal, the AI ​​will consider its impact when evaluating fatigue. Similarly, if the stress level is high, the AI ​​will consider its impact when evaluating physical condition. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's fatigue level and physical condition in real time. In addition, the analysis unit can utilize past data and statistical information to perform long-term health management and trend analysis. For example, based on past data, it can predict fluctuations in fatigue levels during specific seasons or time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The recommendation department provides comprehensive recommendations based on the analysis results obtained by the analysis department. For example, if a user is highly fatigued, the recommendation department may suggest relaxation methods such as meditation, light exercise, or a nutritionally balanced diet. Specifically, based on the user's heart rate and sleep data, it may suggest meditation apps or yoga videos with relaxing effects. It can also suggest nutritionally balanced recipes and ingredients based on the user's dietary data. Furthermore, the recommendation department can suggest activities such as reading, creative writing, and travel according to the user's interests. For example, if a user is interested in reading, it may suggest books or articles with relaxing effects. If a user is interested in creative writing, it may suggest ways to reduce stress through creative activities. In addition, the recommendation department can analyze the user's physical condition data in real time and make suggestions at the appropriate time. For example, if a user's heart rate is elevated and their stress level is high, it may suggest meditation or deep breathing exercises to relax. It can also suggest appropriate sleep environments and sleep habits based on the user's sleep data. In this way, the recommendation department can provide specific suggestions tailored to the user's physical condition and interests, supporting fatigue recovery. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the effectiveness of a suggestion based on the user's physical condition data after implementing it, and reflect this in future suggestions. This allows the suggestion department to provide users with optimal recommendations and effectively support their fatigue recovery.

[0033] The data collection unit can collect data such as steps, heart rate, sleep duration, diet, stress level, and activity level using smartphones and wearable devices. For example, the data collection unit can collect diet and stress level data manually entered by the user through a smartphone application. The data collection unit can also send data acquired from wearable devices to the cloud for analysis by the analysis unit. For example, if the user is wearing a smartwatch, the data collection unit can automatically collect data such as steps, heart rate, and sleep duration. The data collection unit can also collect diet and stress level data manually entered by the user through a smartphone application. Furthermore, the data collection unit can send data acquired from wearable devices to the cloud for analysis by the analysis unit. This allows for the efficient collection of the user's daily activity and physical condition data using smartphones and wearable devices. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smartphones and wearable devices into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can determine the user's fatigue level and physical condition based on the collected data. For example, the analysis unit can use AI to determine the user's fatigue level and physical condition based on the collected data. For example, the analysis unit may determine that the user's fatigue level is high if their heart rate is high and their sleep duration is short. The analysis unit may also determine that the user's fatigue level is high if their activity level is high. Furthermore, the analysis unit can take into account the user's diet and stress level to make a comprehensive determination of fatigue level and physical condition. This allows for an accurate determination of the user's fatigue level and physical condition based on the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the determination of the user's fatigue level and physical condition.

[0035] The suggestion unit can suggest relaxation activities such as meditation, light exercise, and nutritionally balanced meals when the user's fatigue level is high. For example, the suggestion unit can suggest relaxation activities such as meditation, light exercise, and nutritionally balanced meals when the user's fatigue level is high. Furthermore, the suggestion unit can suggest activities such as reading, creative writing, and travel, depending on the user's interests. In addition, the suggestion unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time. This allows for the provision of appropriate recommendations when the user's fatigue level is high. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, when the user's fatigue level is high, the suggestion unit can have a generating AI execute suggestions such as relaxation activities such as meditation, light exercise, and nutritionally balanced meals.

[0036] The suggestion unit can make suggestions such as reading, creative writing, and travel based on the user's interests. For example, the suggestion unit can make suggestions such as reading, creative writing, and travel based on the user's interests. Furthermore, the suggestion unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time. This allows for the provision of recommendations tailored to the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can have a generating AI execute suggestions such as reading, creative writing, and travel based on the user's interests.

[0037] The suggestion unit can analyze the user's health data in real time and make suggestions at the appropriate time. For example, the suggestion unit can analyze the user's health data in real time and make suggestions at the appropriate time. The suggestion unit can also make suggestions such as reading, creative writing, and travel according to the user's interests. This allows the suggestion unit to analyze the user's health data in real time and make suggestions at the appropriate time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can analyze the user's health data in real time and have a suggestion generation AI execute the suggestion at the appropriate time.

[0038] The data collection unit can analyze the user's past activity data and select the optimal data collection method. For example, the data collection unit can customize the data collection method based on the user's past activities. The data collection unit can also select the most efficient data collection method from the user's past activity data. Furthermore, the data collection unit can analyze the user's past activity data and suggest the optimal data collection method for a specific time period. This allows for the selection of the optimal data collection method by analyzing the user's past activity data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past activity data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and environment. For example, if the user is at work, the unit can limit data collection and collect only essential data. If the user is on vacation, the unit can collect detailed data and offer suggestions for relaxation. Furthermore, if the user is exercising, the unit can prioritize collecting exercise-related data. This enables data collection tailored to the user's lifestyle and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and environment data into a generating AI and have the generating AI perform the data collection filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at home, the data collection unit can prioritize the collection of data related to indoor activities. If the user is outside, the data collection unit can also prioritize the collection of data related to steps taken and distance traveled. Furthermore, if the user is traveling, the data collection unit can also prioritize the collection of data related to the environment of the travel destination. This allows for the priority collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is highly active on social media, the data collection unit may prioritize collecting data related to stress levels and heart rate. Conversely, if a user is less active on social media, the data collection unit may prioritize collecting data related to sleep and activity levels. Furthermore, the data collection unit can analyze the content of a user's social media posts and collect relevant data. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's past data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data to detect abnormalities in the current heart rate. It can also refer to the user's past sleep data to analyze the current sleep pattern. Furthermore, the analysis unit can refer to the user's past activity data to evaluate the current activity level. By referring to the user's past data, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0043] The analysis unit can apply different analysis methods depending on the user's lifestyle pattern during analysis. For example, if the user is a night owl, the analysis unit can apply an analysis method that emphasizes nighttime data. Similarly, if the user is an early riser, the analysis unit can apply an analysis method that emphasizes morning data. Furthermore, if the user has an irregular lifestyle, the analysis unit can apply an analysis method that considers overall data. This improves analysis accuracy by applying an analysis method tailored to the user's lifestyle pattern. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI execute the application of the analysis method.

[0044] The analysis unit can determine the priority of analysis based on the user's activity history during the analysis process. For example, the analysis unit may prioritize analyzing activities that the user has frequently performed in the past. The analysis unit can also prioritize the analysis of important data from the user's activity history. Furthermore, the analysis unit can analyze the user's activity history and apply the most suitable analysis method for a specific time period. This allows for the prioritization of important data by determining the analysis priority based on the user's activity history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user activity history data into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data to detect abnormalities in the current heart rate. It can also refer to the user's past sleep data to analyze the current sleep pattern. Furthermore, the analysis unit can refer to the user's past activity data to evaluate the current activity level. By referring to the user's relevant data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The suggestion unit can adjust the level of detail of its suggestions based on the user's fatigue level. For example, if the user's fatigue level is high, the suggestion unit will provide simple and highly visual suggestions. Conversely, if the user's fatigue level is low, the suggestion unit can also provide suggestions that include detailed information. Furthermore, the suggestion unit can adjust the level of detail of its suggestions according to the user's fatigue level. This allows for the provision of appropriate suggestions by adjusting the level of detail according to the user's fatigue level. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user fatigue level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the suggestions.

[0047] The suggestion unit can apply different suggestion algorithms depending on the user's interests and preferences when making suggestions. For example, if the user is interested in reading, the suggestion unit will make suggestions related to reading. It can also make suggestions related to exercise if the user is interested in exercise. Furthermore, if the user is interested in travel, it can make suggestions related to travel. This allows for suggestions tailored to the user's interests and preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user interest data into a generating AI and have the generating AI apply the suggestion algorithm.

[0048] The suggestion unit can determine the priority of suggestions based on the user's activity history when making suggestions. For example, the suggestion unit may prioritize suggesting activities that the user has frequently performed in the past. The suggestion unit can also prioritize important suggestions based on the user's activity history. Furthermore, the suggestion unit can analyze the user's activity history and make suggestions that are optimal for specific time periods. This allows for prioritizing important suggestions by determining the priority of suggestions based on the user's activity history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user activity history data into a generating AI and have the generating AI perform the task of determining the priority of suggestions.

[0049] The suggestion unit can improve the accuracy of its suggestions by referring to relevant user data during the suggestion process. For example, the suggestion unit can refer to the user's past heart rate data to detect abnormalities in the current heart rate. It can also refer to the user's past sleep data to analyze the current sleep pattern. Furthermore, the suggestion unit can refer to the user's past activity data to evaluate the current activity level. This improves the accuracy of the suggestions by referring to relevant user data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relevant user data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can optimize the frequency of data collection based on the user's past data. For example, it can adjust the frequency of data collection based on activities the user has frequently performed in the past. It can also select the most efficient timing for data collection from the user's past data. Furthermore, it can analyze the user's past data and suggest the optimal data collection frequency for a specific time period. This enables efficient data collection by optimizing the frequency of data collection based on the user's past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the data collection frequency.

[0052] The analysis unit can optimize its analysis algorithm based on the user's past data. For example, it can detect abnormalities in the current heart rate based on the user's past heart rate data. It can also analyze the current sleep pattern based on the user's past sleep data. Furthermore, it can evaluate the current activity level based on the user's past activity data. By optimizing the analysis algorithm based on the user's past data, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0053] The suggestion unit can optimize the content of suggestions based on the user's past data. For example, it can customize suggestions based on activities the user has preferred in the past. It can also select the most effective suggestions from the user's past data. Furthermore, it can analyze the user's past data and provide suggestions that are optimal for a specific time period. In this way, the effectiveness of suggestions can be maximized by optimizing the content of suggestions based on the user's past data. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the suggestion content.

[0054] The data collection unit can optimize its data collection methods based on the user's geographical location information. For example, if the user is at home, it can prioritize collecting data related to indoor activities. If the user is out, it can prioritize collecting data related to steps taken and distance traveled. Furthermore, if the user is traveling, it can prioritize collecting data related to the environment of their travel destination. By optimizing the data collection method based on the user's geographical location information, it is possible to efficiently collect highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the optimization of the data collection method.

[0055] The suggestion unit can optimize the timing of suggestions based on the user's activity history. For example, it can adjust the timing of suggestions based on the time periods in which the user has frequently engaged in activities in the past. It can also select the most effective timing for suggestions from the user's activity history. Furthermore, it can analyze the user's activity history and make suggestions that are optimal for specific time periods. In this way, the effectiveness of suggestions can be maximized by optimizing the timing of suggestions based on the user's activity history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user activity history data into a generating AI and have the generating AI perform the optimization of suggestion timing.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the user's daily activity and health data. The data collection unit uses smartphones and wearable devices to collect data such as steps, heart rate, sleep duration, diet, stress level, and activity level. For example, if the user is wearing a smartwatch, data such as steps, heart rate, and sleep duration will be collected automatically. The data collection unit can also collect data on diet and stress levels that the user manually enters through a smartphone application. Furthermore, the data collection unit can send the data acquired from wearable devices to the cloud for analysis by the analysis unit. Step 2: The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. The analysis unit uses AI to determine the user's fatigue level and physical condition based on the collected data. For example, if the user's heart rate is high and their sleep duration is short, the analysis unit will determine that they are fatigued. It can also determine that the user is fatigued if their activity level is high. Furthermore, the analysis unit can take into account the user's diet and stress level to make a comprehensive determination of their fatigue level and physical condition. Step 3: The recommendation unit provides comprehensive recommendations based on the analysis results obtained by the analysis unit. If the user's fatigue level is high, the recommendation unit will suggest things like meditation to relax, light exercise, or a nutritionally balanced diet. It can also suggest things like reading, creative writing, or travel, depending on the user's interests. Furthermore, the recommendation unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time.

[0058] (Example of form 2) The fatigue recovery suggestion system according to an embodiment of the present invention is a system that collects and analyzes a user's daily activity and physical condition data and makes specific suggestions for fatigue recovery at the appropriate time. The fatigue recovery suggestion system collects and analyzes the user's daily activity and physical condition data to determine the user's fatigue level and physical condition. Based on the analysis results, it provides comprehensive recommendations such as diet, exercise, meditation, reading, travel, and creative writing. For example, the fatigue recovery suggestion system collects the user's daily activity and physical condition data. In this case, data such as steps, heart rate, sleep time, diet, stress level, and activity level are collected using a smartphone or wearable device. For example, if the user is wearing a smartwatch, data such as steps, heart rate, and sleep time are automatically collected. Next, the fatigue recovery suggestion system analyzes the collected data. The AI ​​determines the user's fatigue level and physical condition based on the collected data. For example, if the user's heart rate is high and sleep time is short, it is determined that the fatigue level is high. This makes it possible to understand the user's current physical condition and fatigue level. Next, the fatigue recovery suggestion system provides comprehensive recommendations based on the analysis results. For example, if a user is highly fatigued, suggestions such as meditation, light exercise, or a balanced diet will be made to help them relax. Suggestions for activities like reading, creative writing, or travel will also be made based on the user's interests. This allows users to recover from fatigue in a way that suits them. For instance, if a user is tired from work, the AI ​​will suggest meditation or light exercise to help them refresh. It will also suggest activities related to the user's hobbies and interests to boost their motivation and promote fatigue recovery. In this way, the fatigue recovery suggestion system collects and analyzes the user's daily activity and physical condition data, enabling it to provide specific suggestions for fatigue recovery at the appropriate time.

[0059] The fatigue recovery suggestion system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects the user's daily activity and physical condition data. The data collection unit collects data such as steps, heart rate, sleep duration, diet, stress level, and activity level using, for example, a smartphone or wearable device. For example, if the user is wearing a smartwatch, the data collection unit automatically collects data such as steps, heart rate, and sleep duration. The data collection unit can also collect data such as diet and stress level manually entered by the user through a smartphone application. Furthermore, the data collection unit can transmit data acquired from wearable devices to the cloud for analysis by the analysis unit. The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. The analysis unit uses, for example, AI to determine the user's fatigue level and physical condition based on the collected data. For example, the analysis unit determines that the user's fatigue level is high if their heart rate is high and their sleep duration is short. The analysis unit can also determine that the user's fatigue level is high if their activity level is high. Furthermore, the analysis unit can comprehensively determine the user's fatigue level and physical condition by considering the user's diet and stress level. The suggestion unit provides comprehensive recommendations based on the analysis results obtained by the analysis unit. For example, if the user's fatigue level is high, the suggestion unit may suggest meditation, light exercise, or a nutritionally balanced meal to help them relax. The suggestion unit can also suggest activities such as reading, creative writing, or travel, depending on the user's interests. In addition, the suggestion unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time. As a result, the fatigue recovery suggestion system according to this embodiment can collect and analyze the user's daily activities and physical condition data and make specific suggestions for fatigue recovery at the appropriate time.

[0060] The data collection unit collects data on the user's daily activities and physical condition. For example, it uses smartphones and wearable devices to collect data such as steps, heart rate, sleep duration, diet, stress levels, and activity levels. Specifically, for users wearing smartwatches, data such as steps, heart rate, and sleep duration are automatically collected. Smartwatches use built-in accelerometers and heart rate sensors to monitor the user's movements and heart rate variability in real time. This allows for detailed recording of the user's daily activity level, exercise intensity, and rest quality. The data collection unit can also collect data on diet and stress levels manually entered by the user through smartphone applications. For example, when a user enters their diet into the app, that information is sent to the cloud and integrated with other data. Furthermore, the data collection unit can send data acquired from wearable devices to the cloud for analysis by the analysis unit. On the cloud, data is centrally managed and stored in a format easily accessible to the analysis unit. This allows the data collection unit to collect a wide range of data from various devices and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. For example, the analysis unit uses AI to determine the user's fatigue level and physical condition based on the collected data. Specifically, the AI ​​uses machine learning algorithms to analyze data such as the user's heart rate, sleep duration, activity level, diet, and stress level. For example, if the user's heart rate is high and their sleep duration is short, the AI ​​will determine that they are fatigued. Similarly, if the user's activity level is high, the AI ​​can also determine that they are fatigued. Furthermore, the analysis unit can take into account the user's diet and stress level to make a comprehensive determination of fatigue level and physical condition. For example, if the user has consumed a high-calorie meal, the AI ​​will consider its impact when evaluating fatigue. Similarly, if the stress level is high, the AI ​​will consider its impact when evaluating physical condition. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's fatigue level and physical condition in real time. In addition, the analysis unit can utilize past data and statistical information to perform long-term health management and trend analysis. For example, based on past data, it can predict fluctuations in fatigue levels during specific seasons or time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0062] The recommendation department provides comprehensive recommendations based on the analysis results obtained by the analysis department. For example, if a user is highly fatigued, the recommendation department may suggest relaxation methods such as meditation, light exercise, or a nutritionally balanced diet. Specifically, based on the user's heart rate and sleep data, it may suggest meditation apps or yoga videos with relaxing effects. It can also suggest nutritionally balanced recipes and ingredients based on the user's dietary data. Furthermore, the recommendation department can suggest activities such as reading, creative writing, and travel according to the user's interests. For example, if a user is interested in reading, it may suggest books or articles with relaxing effects. If a user is interested in creative writing, it may suggest ways to reduce stress through creative activities. In addition, the recommendation department can analyze the user's physical condition data in real time and make suggestions at the appropriate time. For example, if a user's heart rate is elevated and their stress level is high, it may suggest meditation or deep breathing exercises to relax. It can also suggest appropriate sleep environments and sleep habits based on the user's sleep data. In this way, the recommendation department can provide specific suggestions tailored to the user's physical condition and interests, supporting fatigue recovery. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the effectiveness of a suggestion based on the user's physical condition data after implementing it, and reflect this in future suggestions. This allows the suggestion department to provide users with optimal recommendations and effectively support their fatigue recovery.

[0063] The data collection unit can collect data such as steps, heart rate, sleep duration, diet, stress level, and activity level using smartphones and wearable devices. For example, the data collection unit can collect diet and stress level data manually entered by the user through a smartphone application. The data collection unit can also send data acquired from wearable devices to the cloud for analysis by the analysis unit. For example, if the user is wearing a smartwatch, the data collection unit can automatically collect data such as steps, heart rate, and sleep duration. The data collection unit can also collect diet and stress level data manually entered by the user through a smartphone application. Furthermore, the data collection unit can send data acquired from wearable devices to the cloud for analysis by the analysis unit. This allows for the efficient collection of the user's daily activity and physical condition data using smartphones and wearable devices. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smartphones and wearable devices into a generating AI and have the generating AI perform data analysis.

[0064] The analysis unit can determine the user's fatigue level and physical condition based on the collected data. For example, the analysis unit can use AI to determine the user's fatigue level and physical condition based on the collected data. For example, the analysis unit may determine that the user's fatigue level is high if their heart rate is high and their sleep duration is short. The analysis unit may also determine that the user's fatigue level is high if their activity level is high. Furthermore, the analysis unit can take into account the user's diet and stress level to make a comprehensive determination of fatigue level and physical condition. This allows for an accurate determination of the user's fatigue level and physical condition based on the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the determination of the user's fatigue level and physical condition.

[0065] The suggestion unit can suggest relaxation activities such as meditation, light exercise, and nutritionally balanced meals when the user's fatigue level is high. For example, the suggestion unit can suggest relaxation activities such as meditation, light exercise, and nutritionally balanced meals when the user's fatigue level is high. Furthermore, the suggestion unit can suggest activities such as reading, creative writing, and travel, depending on the user's interests. In addition, the suggestion unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time. This allows for the provision of appropriate recommendations when the user's fatigue level is high. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, when the user's fatigue level is high, the suggestion unit can have a generating AI execute suggestions such as relaxation activities such as meditation, light exercise, and nutritionally balanced meals.

[0066] The suggestion unit can make suggestions such as reading, creative writing, and travel based on the user's interests. For example, the suggestion unit can make suggestions such as reading, creative writing, and travel based on the user's interests. Furthermore, the suggestion unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time. This allows for the provision of recommendations tailored to the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can have a generating AI execute suggestions such as reading, creative writing, and travel based on the user's interests.

[0067] The suggestion unit can analyze the user's health data in real time and make suggestions at the appropriate time. For example, the suggestion unit can analyze the user's health data in real time and make suggestions at the appropriate time. The suggestion unit can also make suggestions such as reading, creative writing, and travel according to the user's interests. This allows the suggestion unit to analyze the user's health data in real time and make suggestions at the appropriate time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can analyze the user's health data in real time and have a suggestion generation AI execute the suggestion at the appropriate time.

[0068] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can minimize the frequency of data collection to quickly collect the necessary data. This reduces the user's burden by adjusting the frequency of data collection according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0069] The data collection unit can analyze the user's past activity data and select the optimal data collection method. For example, the data collection unit can customize the data collection method based on the user's past activities. The data collection unit can also select the most efficient data collection method from the user's past activity data. Furthermore, the data collection unit can analyze the user's past activity data and suggest the optimal data collection method for a specific time period. This allows for the selection of the optimal data collection method by analyzing the user's past activity data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past activity data into a generating AI and have the generating AI select the optimal data collection method.

[0070] The data collection unit can filter data based on the user's current lifestyle and environment. For example, if the user is at work, the unit can limit data collection and collect only essential data. If the user is on vacation, the unit can collect detailed data and offer suggestions for relaxation. Furthermore, if the user is exercising, the unit can prioritize collecting exercise-related data. This enables data collection tailored to the user's lifestyle and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and environment data into a generating AI and have the generating AI perform the data collection filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress levels. It can also prioritize collecting heart rate and sleep data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data related to steps and activity levels. This allows for the priority collection of important data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data collection.

[0072] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at home, the data collection unit can prioritize the collection of data related to indoor activities. If the user is outside, the data collection unit can also prioritize the collection of data related to steps taken and distance traveled. Furthermore, if the user is traveling, the data collection unit can also prioritize the collection of data related to the environment of the travel destination. This allows for the priority collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0073] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is highly active on social media, the data collection unit may prioritize collecting data related to stress levels and heart rate. Conversely, if a user is less active on social media, the data collection unit may prioritize collecting data related to sleep and activity levels. Furthermore, the data collection unit can analyze the content of a user's social media posts and collect relevant data. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply an analysis algorithm that emphasizes stress levels. If the user is relaxed, the analysis unit can also apply an analysis algorithm that emphasizes heart rate and sleep data. Furthermore, if the user is in a hurry, the analysis unit can apply an analysis algorithm that emphasizes activity level and steps taken. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0075] The analysis unit can improve the accuracy of its analysis by referring to the user's past data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data to detect abnormalities in the current heart rate. It can also refer to the user's past sleep data to analyze the current sleep pattern. Furthermore, the analysis unit can refer to the user's past activity data to evaluate the current activity level. By referring to the user's past data, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0076] The analysis unit can apply different analysis methods depending on the user's lifestyle pattern during analysis. For example, if the user is a night owl, the analysis unit can apply an analysis method that emphasizes nighttime data. Similarly, if the user is an early riser, the analysis unit can apply an analysis method that emphasizes morning data. Furthermore, if the user has an irregular lifestyle, the analysis unit can apply an analysis method that considers overall data. This improves analysis accuracy by applying an analysis method tailored to the user's lifestyle pattern. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI execute the application of the analysis method.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0078] The analysis unit can determine the priority of analysis based on the user's activity history during the analysis process. For example, the analysis unit may prioritize analyzing activities that the user has frequently performed in the past. The analysis unit can also prioritize the analysis of important data from the user's activity history. Furthermore, the analysis unit can analyze the user's activity history and apply the most suitable analysis method for a specific time period. This allows for the prioritization of important data by determining the analysis priority based on the user's activity history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user activity history data into a generating AI and have the generating AI determine the analysis priority.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data to detect abnormalities in the current heart rate. It can also refer to the user's past sleep data to analyze the current sleep pattern. Furthermore, the analysis unit can refer to the user's past activity data to evaluate the current activity level. By referring to the user's relevant data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the readability is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0081] The suggestion unit can adjust the level of detail of its suggestions based on the user's fatigue level. For example, if the user's fatigue level is high, the suggestion unit will provide simple and highly visual suggestions. Conversely, if the user's fatigue level is low, the suggestion unit can also provide suggestions that include detailed information. Furthermore, the suggestion unit can adjust the level of detail of its suggestions according to the user's fatigue level. This allows for the provision of appropriate suggestions by adjusting the level of detail according to the user's fatigue level. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user fatigue level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the suggestions.

[0082] The suggestion unit can apply different suggestion algorithms depending on the user's interests and preferences when making suggestions. For example, if the user is interested in reading, the suggestion unit will make suggestions related to reading. It can also make suggestions related to exercise if the user is interested in exercise. Furthermore, if the user is interested in travel, it can make suggestions related to travel. This allows for suggestions tailored to the user's interests and preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user interest data into a generating AI and have the generating AI apply the suggestion algorithm.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide quick and concise suggestions. By adjusting the length of suggestions according to the user's emotions, readability is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0084] The suggestion unit can determine the priority of suggestions based on the user's activity history when making suggestions. For example, the suggestion unit may prioritize suggesting activities that the user has frequently performed in the past. The suggestion unit can also prioritize important suggestions based on the user's activity history. Furthermore, the suggestion unit can analyze the user's activity history and make suggestions that are optimal for specific time periods. This allows for prioritizing important suggestions by determining the priority of suggestions based on the user's activity history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user activity history data into a generating AI and have the generating AI perform the task of determining the priority of suggestions.

[0085] The suggestion unit can improve the accuracy of its suggestions by referring to relevant user data during the suggestion process. For example, the suggestion unit can refer to the user's past heart rate data to detect abnormalities in the current heart rate. It can also refer to the user's past sleep data to analyze the current sleep pattern. Furthermore, the suggestion unit can refer to the user's past activity data to evaluate the current activity level. This improves the accuracy of the suggestions by referring to relevant user data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relevant user data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can make suggestions during times when the user can relax. If the user is relaxed, the suggestion unit can also make suggestions for trying new activities. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that can be completed in a short amount of time. In this way, the effectiveness of suggestions can be maximized by adjusting the timing of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0088] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of data related to stress levels. If the user is relaxed, it can prioritize the analysis of heart rate and sleep data. Furthermore, if the user is in a hurry, it can prioritize the analysis of activity level and steps taken. This allows for the rapid analysis of important data by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of the analysis results.

[0089] The suggestion unit can estimate the user's emotions and customize the content of suggestions based on those emotions. For example, if the user is stressed, it can suggest meditation or light exercise to help them relax. If the user is relaxed, it can also suggest new hobbies or activities. Furthermore, if the user is in a hurry, it can offer suggestions that can be completed in a short amount of time. This maximizes the effectiveness of suggestions by customizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI customize the content of the suggestions.

[0090] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection method can be simplified to reduce the user's burden. If the user is relaxed, detailed data can be collected. Furthermore, if the user is in a hurry, a method for quickly collecting data can be selected. In this way, the user's burden can be reduced by adjusting the data collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the data collection method.

[0091] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is stressed, it can provide simple and easy-to-understand feedback. If the user is relaxed, it can also provide feedback that includes detailed information. Furthermore, if the user is in a hurry, it can provide concise feedback. This improves readability by adjusting the feedback method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the feedback method of the analysis results.

[0092] The data collection unit can optimize the frequency of data collection based on the user's past data. For example, it can adjust the frequency of data collection based on activities the user has frequently performed in the past. It can also select the most efficient timing for data collection from the user's past data. Furthermore, it can analyze the user's past data and suggest the optimal data collection frequency for a specific time period. This enables efficient data collection by optimizing the frequency of data collection based on the user's past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the data collection frequency.

[0093] The analysis unit can optimize its analysis algorithm based on the user's past data. For example, it can detect abnormalities in the current heart rate based on the user's past heart rate data. It can also analyze the current sleep pattern based on the user's past sleep data. Furthermore, it can evaluate the current activity level based on the user's past activity data. By optimizing the analysis algorithm based on the user's past data, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0094] The suggestion unit can optimize the content of suggestions based on the user's past data. For example, it can customize suggestions based on activities the user has preferred in the past. It can also select the most effective suggestions from the user's past data. Furthermore, it can analyze the user's past data and provide suggestions that are optimal for a specific time period. In this way, the effectiveness of suggestions can be maximized by optimizing the content of suggestions based on the user's past data. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the suggestion content.

[0095] The data collection unit can optimize its data collection methods based on the user's geographical location information. For example, if the user is at home, it can prioritize collecting data related to indoor activities. If the user is out, it can prioritize collecting data related to steps taken and distance traveled. Furthermore, if the user is traveling, it can prioritize collecting data related to the environment of their travel destination. By optimizing the data collection method based on the user's geographical location information, it is possible to efficiently collect highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the optimization of the data collection method.

[0096] The suggestion unit can optimize the timing of suggestions based on the user's activity history. For example, it can adjust the timing of suggestions based on the time periods in which the user has frequently engaged in activities in the past. It can also select the most effective timing for suggestions from the user's activity history. Furthermore, it can analyze the user's activity history and make suggestions that are optimal for specific time periods. In this way, the effectiveness of suggestions can be maximized by optimizing the timing of suggestions based on the user's activity history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user activity history data into a generating AI and have the generating AI perform the optimization of suggestion timing.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects the user's daily activity and health data. The data collection unit uses smartphones and wearable devices to collect data such as steps, heart rate, sleep duration, diet, stress level, and activity level. For example, if the user is wearing a smartwatch, data such as steps, heart rate, and sleep duration will be collected automatically. The data collection unit can also collect data on diet and stress levels that the user manually enters through a smartphone application. Furthermore, the data collection unit can send the data acquired from wearable devices to the cloud for analysis by the analysis unit. Step 2: The analysis unit analyzes the data collected by the data collection unit to determine the user's fatigue level and physical condition. The analysis unit uses AI to determine the user's fatigue level and physical condition based on the collected data. For example, if the user's heart rate is high and their sleep duration is short, the analysis unit will determine that they are fatigued. It can also determine that the user is fatigued if their activity level is high. Furthermore, the analysis unit can take into account the user's diet and stress level to make a comprehensive determination of their fatigue level and physical condition. Step 3: The recommendation unit provides comprehensive recommendations based on the analysis results obtained by the analysis unit. If the user's fatigue level is high, the recommendation unit will suggest things like meditation to relax, light exercise, or a nutritionally balanced diet. It can also suggest things like reading, creative writing, or travel, depending on the user's interests. Furthermore, the recommendation unit can analyze the user's physical condition data in real time and make suggestions at the appropriate time.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's daily activity and physical condition data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to determine the user's fatigue level and physical condition. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates complex recommendations based on the analysis results, which are provided to the user through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's daily activity and physical condition data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the user's fatigue level and physical condition. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates complex recommendations based on the analysis results and provides them to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's daily activity and physical condition data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the user's fatigue level and physical condition. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates complex recommendations based on the analysis results and provides them to the user through the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's daily activity and physical condition data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the user's fatigue level and physical condition. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates complex recommendations based on the analysis results and provides them to the user through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The data collection unit collects users' daily activity and health data, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the user's fatigue level and physical condition, The system includes a proposal unit that provides a comprehensive recommendation based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Using smartphones and wearable devices, data such as steps taken, heart rate, sleep duration, diet, stress levels, and activity levels are collected. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, the system determines the user's fatigue level and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, If the user's fatigue level is high, the system will suggest relaxation methods such as meditation, light exercise, and a nutritionally balanced diet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the user's interests, we offer suggestions such as reading, creative writing, and travel. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The system analyzes the user's health data in real time and provides suggestions at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past activity data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the user's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system references relevant user data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the user's fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, the priority of the proposal is determined based on the user's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, we refer to relevant user data to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects users' daily activity and health data, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the user's fatigue level and physical condition, The system includes a proposal unit that provides a comprehensive recommendation based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Using smartphones and wearable devices, data such as steps taken, heart rate, sleep duration, diet, stress levels, and activity levels are collected. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the system determines the user's fatigue level and physical condition. The system according to feature 1.

4. The aforementioned proposal section is, If the user's fatigue level is high, the system will suggest relaxation methods such as meditation, light exercise, and a nutritionally balanced diet. The system according to feature 1.

5. The aforementioned proposal section is, Based on the user's interests, we offer suggestions such as reading, creative writing, and travel. The system according to feature 1.

6. The aforementioned proposal section is, The system analyzes the user's health data in real time and provides suggestions at the appropriate time. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past activity data and select the optimal data collection method. The system according to feature 1.

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