system

Abstract

The system according to the embodiment aims to prevent deterioration of posture by sending a notification before posture deteriorates. [Solution] A system according to an embodiment includes a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors posture data in real time. The analysis unit analyzes the data collected by the monitoring unit and identifies patterns of posture deterioration. The notification unit sends a notification before posture deterioration occurs based on the patterns identified by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to prevent poor posture during long hours of desk work or telework.

[0005] The system according to the embodiment aims to prevent deterioration of posture by sending a notification before posture deteriorates. [Means for solving the problem]

[0006] A system according to an embodiment includes a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors posture data in real time. The analysis unit analyzes the data collected by the monitoring unit and identifies patterns of posture deterioration. The notification unit sends a notification before posture deterioration occurs based on the patterns identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent the deterioration of posture by sending a notification before the posture deteriorates. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A posture monitoring system according to an embodiment of the present invention monitors a user's posture in real time, and a generation AI analyzes past data to notify the user before their posture deteriorates. The posture monitoring system monitors the user's posture in real time and transmits the data to a cloud server. Next, the generation AI analyzes the past data to identify patterns of posture deterioration. Finally, the generation AI sends a notification to the user before their posture deteriorates. For example, the posture monitoring system collects posture data using an acceleration sensor or a gyro sensor. For example, notifications can be sent via a smartphone app or the vibration function of a wearable device. The generation AI analyzes past data using a machine learning algorithm to identify patterns of posture deterioration. This allows the posture monitoring system to take measures before posture deterioration occurs and prevent slouching. This allows the posture monitoring system to monitor the user's posture in real time, and the generation AI analyzes past data to notify the user before their posture deteriorates. For example, individual notifications can be sent based on the user's height, weight, and past posture data. The timing of notifications is also important; by notifying the user just before their posture deteriorates, the user can take immediate measures.

[0029] A posture monitoring system according to an embodiment includes a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors a user's posture in real time. The monitoring unit collects posture data using, for example, an acceleration sensor or a gyro sensor. The acceleration sensor can collect angle data of the user's posture using, for example, a triaxial acceleration sensor. The gyro sensor can collect position data of the user's posture using, for example, a triaxial gyro sensor. The analysis unit uses a generation AI to analyze the data collected by the monitoring unit and identify patterns of poor posture. The generation AI can analyze past data using, for example, deep learning or a neural network, and identify patterns of poor posture. The generation AI can provide personalized notifications taking into account individual data of the user. The notification unit sends a notification before the user's posture deteriorates based on the pattern identified by the analysis unit. The notification unit can send the notification using, for example, a smartphone app or a vibration function of a wearable device. As a result, the posture monitoring system according to an embodiment monitors a user's posture in real time and notifies the user before the user's posture deteriorates, thereby preventing hunched backs.

[0030] The monitoring unit can collect posture data using an acceleration sensor or a gyro sensor. The acceleration sensor can collect angle data of the user's posture using, for example, a three-axis acceleration sensor. The gyro sensor can collect position data of the user's posture using, for example, a three-axis gyro sensor. As a result, the accuracy of the posture data is improved by using the acceleration sensor or gyro sensor. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the acceleration sensor or gyro sensor into the generation AI and cause the generation AI to analyze the posture data.

[0031] The analysis unit can use the generation AI to analyze past data and identify patterns of poor posture. The generation AI can analyze past data and identify patterns of poor posture, for example, using deep learning or a neural network. The generation AI can, for example, analyze the frequency of poor posture and changes over time based on past posture data. The generation AI can, for example, provide personalized notifications by taking into account individual data of the user. This improves the accuracy of identifying patterns of poor posture by using the generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past data into the generation AI and have the generation AI identify patterns of poor posture.

[0032] The notification unit can send the notification using a smartphone app or a vibration function of a wearable device. The notification unit can send the notification using, for example, a smartphone app. The smartphone app, for example, has a notification function or an interface, and can effectively send the notification to the user. The notification unit can also send the notification using a vibration function of the wearable device. The wearable device, for example, is a smartwatch or a fitness tracker, and can send the notification to the user using a vibration function. This allows the notification to be effectively sent to the user by using the vibration function of the smartphone app or the wearable device. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can generate notification content using a generation AI and send the notification to the smartphone app or the wearable device.

[0033] The analysis unit can provide personalized notifications based on the user's individual data. The analysis unit can provide personalized notifications by taking into account individual data such as the user's height, weight, and past posture data. For example, the analysis unit can suggest the optimal timing for posture improvement based on the user's height and weight. The analysis unit can also send a notification before posture deterioration occurs based on past posture data. This enables more personalized notifications by taking into account the user's individual data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input individual data into a generation AI and have the generation AI execute personalized notification content.

[0034] The notification unit can transmit a notification just before the posture is about to deteriorate. For example, the notification unit can transmit a notification just before the posture is about to deteriorate, thereby enabling the user to take measures immediately. For example, the notification unit can transmit a notification a few seconds before the posture is about to deteriorate. The notification unit can also transmit a notification a few minutes before the posture is about to deteriorate. In this way, by transmitting a notification just before the posture is about to deteriorate, the user can take measures immediately. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can determine the timing of the notification using a generation AI and transmit the notification just before the posture is about to deteriorate.

[0035] During monitoring, the monitoring unit can adjust the sensitivity of the sensor according to the user's activity level. For example, when the user is sitting, the monitoring unit can increase the sensitivity of the sensor to detect subtle changes in posture. Furthermore, when the user is standing, the monitoring unit can set the sensitivity of the sensor to a medium level to appropriately detect changes in posture. Furthermore, when the user is walking, the monitoring unit can reduce the sensitivity of the sensor to minimize the influence of movement. This allows for more accurate posture data to be collected by adjusting the sensitivity of the sensor according to the user's activity level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's activity level data to the generation AI and cause the generation AI to adjust the sensitivity of the sensor.

[0036] During monitoring, the monitoring unit can refer to the user's past posture data and strengthen monitoring of specific postures. For example, if the user has previously assumed a posture that is prone to hunching, the monitoring unit can strengthen monitoring of that posture. Furthermore, if the user has previously assumed a posture that caused lower back pain, the monitoring unit can strengthen monitoring of that posture. Furthermore, if the user has previously assumed a posture that caused stiff shoulders, the monitoring unit can strengthen monitoring of that posture. In this way, by referring to past posture data, monitoring of specific postures can be strengthened. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past posture data into the generation AI and cause the generation AI to strengthen monitoring of specific postures.

[0037] During monitoring, the monitoring unit can change the monitoring method based on the user's device usage status. For example, when the user is using a personal computer, the monitoring unit can focus on monitoring the posture of the upper body. Furthermore, when the user is using a smartphone, the monitoring unit can focus on monitoring the posture of the neck and shoulders. Furthermore, when the user is using a tablet, the monitoring unit can monitor the posture of the entire body in a balanced manner. This allows more appropriate posture data to be collected by changing the monitoring method based on the device usage status. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's device usage status data into the generation AI and cause the generation AI to change the monitoring method.

[0038] During monitoring, the monitoring unit can perform monitoring according to the environment, taking into account the user's geographical location information. For example, when the user is in an office, the monitoring unit can monitor a posture suitable for desk work. Furthermore, when the user is at home, the monitoring unit can monitor a relaxed posture. Furthermore, when the user is in a cafe, the monitoring unit can monitor a posture in which the user sits for a long time. This enables appropriate monitoring according to the environment by taking the geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform monitoring according to the environment.

[0039] During monitoring, the monitoring unit can analyze the user's social media activity and collect related posture data. For example, if the user posts on social media for a long time, the monitoring unit can monitor the user's sitting posture. Furthermore, if the user is taking photos on social media, the monitoring unit can monitor the user's standing posture. Furthermore, if the user is watching videos on social media, the monitoring unit can monitor the user's relaxed posture. In this way, related posture data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related posture data.

[0040] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit can, for example, adjust the frequency of monitoring based on feedback provided by the user in the past. The monitoring unit can also change the target area for monitoring based on feedback provided by the user in the past. Furthermore, the monitoring unit can customize the monitoring method based on feedback provided by the user in the past. This allows the monitoring method to be customized by reflecting past feedback. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the monitoring method.

[0041] During analysis, the analysis unit can analyze the variation pattern of the posture data in detail and identify a more accurate pattern. The analysis unit can, for example, analyze minute variations in the posture data and identify signs of posture deterioration. The analysis unit can also analyze long-term variation patterns of the posture data and identify a tendency for posture deterioration. Furthermore, the analysis unit can analyze short-term variation patterns of the posture data and identify the moment when posture deterioration occurs. In this way, by analyzing the variation pattern of the posture data in detail, a more accurate pattern can be identified. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the posture data to a generation AI and cause the generation AI to perform a detailed analysis of the variation pattern.

[0042] During analysis, the analysis unit can personalize the analysis results by taking into account the user's lifestyle and activity schedule. The analysis unit can, for example, suggest optimal timing for posture improvement based on the user's lifestyle. The analysis unit can also suggest actions for posture improvement based on the user's activity schedule. Furthermore, the analysis unit can integrate the user's lifestyle and activity schedule to suggest an optimal posture improvement plan. This allows for more personalized analysis results to be obtained by taking into account the lifestyle and activity schedule. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle and activity schedule data into the generation AI and have the generation AI personalize the analysis results.

[0043] During analysis, the analysis unit can integrate data from different sensors to perform a more comprehensive analysis. For example, the analysis unit can integrate data from an acceleration sensor and a gyro sensor to perform a detailed analysis of posture fluctuations. The analysis unit can also integrate data from a heart rate sensor and a posture sensor to analyze the relationship between stress and posture. Furthermore, the analysis unit can integrate data from a temperature sensor and a posture sensor to analyze the relationship between the environment and posture. This enables a more comprehensive analysis by integrating data from different sensors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from different sensors into a generation AI and have the generation AI perform a comprehensive analysis.

[0044] During analysis, the analysis unit can perform analysis appropriate to the environment, taking into account the user's geographical location information. For example, if the user is in an office, the analysis unit can analyze posture data suitable for desk work. Furthermore, if the user is at home, the analysis unit can analyze relaxed posture data. Furthermore, if the user is in a cafe, the analysis unit can analyze posture data in which the user is sitting for a long time. This allows appropriate analysis appropriate to the environment by taking into account the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform an analysis appropriate to the environment.

[0045] During the analysis, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. For example, if the user posts on social media for a long period of time, the analysis unit can analyze posture data related to that activity. Furthermore, if the user takes photos on social media, the analysis unit can analyze posture data related to that activity. Furthermore, if the user watches videos on social media, the analysis unit can analyze posture data related to that activity. In this way, by analyzing social media activity, related data can be reflected in the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and have the generation AI analyze the related data.

[0046] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit can, for example, adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also change the target data for analysis based on feedback provided by the user in the past. Furthermore, the analysis unit can customize the analysis method based on feedback provided by the user in the past. This allows the analysis algorithm to be customized by reflecting past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI customize the analysis algorithm.

[0047] The notification unit can change the notification method depending on the user's current activity status when notifying the user. For example, when the user is at work, the notification unit can send a quiet vibration notification. Furthermore, when the user is taking a break, the notification unit can send a voice notification. Furthermore, when the user is exercising, the notification unit can send a visual notification. This allows for more effective notification by changing the notification method depending on the current activity status. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's current activity status data into the generation AI and have the generation AI change the notification method.

[0048] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit can prioritize the selection of a notification method that the user has used favorably in the past. The notification unit can also select a notification method that the user has found effective in the past. Furthermore, the notification unit can select the optimal notification method based on feedback provided by the user in the past. In this way, the optimal notification method can be selected by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0049] The notification unit can customize the notification method based on the user's device usage status when notifying the user. For example, if the user is using a smartphone, the notification unit can send an app notification. Furthermore, if the user is using a wearable device, the notification unit can send a vibration notification. Furthermore, if the user is using a personal computer, the notification unit can send a desktop notification. This enables more effective notification by customizing the notification method based on the device usage status. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device usage status data into the generation AI and have the generation AI customize the notification method.

[0050] When providing a notification, the notification unit can provide a notification appropriate to the environment, taking into account the user's geographical location information. For example, if the user is in an office, the notification unit can send a notification to improve posture suitable for desk work. Furthermore, if the user is at home, the notification unit can send a notification to improve posture to relax. Furthermore, if the user is in a cafe, the notification unit can send a notification to improve posture for sitting for long periods of time. This makes it possible to provide an appropriate notification appropriate to the environment by taking into account the geographical location information. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's geographical location information data into a generation AI and cause the generation AI to execute a notification appropriate to the environment.

[0051] The notification unit can analyze the user's social media activity at the time of notification and reflect related information in the notification. For example, if the user posts on social media for a long time, the notification unit can send a posture improvement notification related to that activity. Furthermore, if the user takes photos on social media, the notification unit can send a posture improvement notification related to that activity. Furthermore, if the user watches videos on social media, the notification unit can send a posture improvement notification related to that activity. In this way, by analyzing social media activity, related information can be reflected in the notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's social media activity data into a generation AI and cause the generation AI to notify the user of related information.

[0052] The notification unit can customize the notification method by reflecting the user's past feedback when notifying. The notification unit can adjust the timing of the notification based on, for example, feedback provided by the user in the past. The notification unit can also change the content of the notification based on feedback provided by the user in the past. The notification unit can also customize the notification method based on feedback provided by the user in the past. This allows the notification method to be customized by reflecting past feedback. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the notification method.

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

[0054] The monitoring unit can simultaneously monitor the user's breathing pattern while collecting the user's posture data. For example, a breathing sensor can be used to measure the depth and rhythm of the user's breathing and integrate the data with the posture data. Furthermore, if a change in the breathing pattern is associated with poor posture, the analysis unit can take this into account in its analysis. Furthermore, the notification unit can suggest breathing techniques to the user to help them relax based on the change in the breathing pattern. This allows the posture monitoring system to provide more comprehensive health management by taking the user's breathing pattern into account.

[0055] The analysis unit can take into account the user's dietary and hydration data when analyzing the user's posture data. For example, the analysis unit can analyze posture data immediately after the user has eaten or drank a meal and identify the impact of these factors on posture. The analysis unit can also suggest optimal timing for improving posture based on the timing of meals and hydration. Furthermore, the notification unit can provide advice for maintaining proper posture after the user has eaten or drank a meal. This allows the posture monitoring system to take into account the user's dietary and hydration data, enabling more personalized health management.

[0056] The monitoring unit can simultaneously monitor the user's muscle tension when collecting the user's posture data. For example, the monitoring unit can measure the user's muscle tension using an electromyography sensor and integrate the data with the posture data. Furthermore, if changes in muscle tension are related to poor posture, the analysis unit can take this into account when performing analysis. Furthermore, the notification unit can provide the user with advice on stretching and relaxation based on changes in muscle tension. This allows the posture monitoring system to take the user's muscle tension into account, enabling more comprehensive health management.

[0057] The monitoring unit can simultaneously monitor the user's heart rate while collecting the user's posture data. For example, the heart rate can be measured using a heart rate sensor and integrated with the posture data. If changes in heart rate are related to poor posture, the analysis unit can take this into account when performing analysis. Furthermore, the notification unit can provide the user with advice on how to relax based on changes in heart rate. This allows the posture monitoring system to take the user's heart rate into account for more comprehensive health management.

[0058] The monitoring unit can simultaneously monitor the user's skin temperature when collecting the user's posture data. For example, a temperature sensor can be used to measure the user's skin temperature and integrate it with the posture data. If changes in skin temperature are related to poor posture, the analysis unit can take this into account when performing analysis. Furthermore, the notification unit can provide the user with advice on maintaining proper posture based on changes in skin temperature. This allows the posture monitoring system to provide more comprehensive health management by taking the user's skin temperature into account.

[0059] The monitoring unit can simultaneously monitor the user's activity level while collecting the user's posture data. For example, an activity tracker can be used to measure the user's steps and exercise volume, and the data can be integrated with the posture data. Furthermore, if a change in activity level is related to poor posture, the analysis unit can take this into account when analyzing the data. Furthermore, the notification unit can provide the user with advice on maintaining proper posture based on the change in activity level. This allows the posture monitoring system to take the user's activity level into account, enabling more comprehensive health management.

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

[0061] Step 1: The monitoring unit monitors the user's posture in real time. The monitoring unit collects posture data using an acceleration sensor and a gyro sensor. The acceleration sensor collects angle data of the user's posture using a 3-axis acceleration sensor, and the gyro sensor collects position data of the user's posture using a 3-axis gyro sensor. Step 2: The analysis unit uses the generation AI to analyze the data collected by the monitoring unit and identify patterns of poor posture. The generation AI then analyzes past data using deep learning and neural networks, and provides personalized notifications taking into account the user's individual data. Step 3: The notification unit sends a notification before the posture deteriorates based on the pattern identified by the analysis unit. The notification unit can send the notification using a smartphone app or the vibration function of a wearable device.

[0062] (Example 2) A posture monitoring system according to an embodiment of the present invention monitors a user's posture in real time, and a generation AI analyzes past data to notify the user before their posture deteriorates. The posture monitoring system monitors the user's posture in real time and transmits the data to a cloud server. Next, the generation AI analyzes the past data to identify patterns of posture deterioration. Finally, the generation AI sends a notification to the user before their posture deteriorates. For example, the posture monitoring system collects posture data using an acceleration sensor or a gyro sensor. For example, notifications can be sent via a smartphone app or the vibration function of a wearable device. The generation AI analyzes past data using a machine learning algorithm to identify patterns of posture deterioration. This allows the posture monitoring system to take measures before posture deterioration occurs and prevent slouching. This allows the posture monitoring system to monitor the user's posture in real time, and the generation AI analyzes past data to notify the user before their posture deteriorates. For example, individual notifications can be sent based on the user's height, weight, and past posture data. The timing of notifications is also important; by notifying the user just before their posture deteriorates, the user can take immediate measures.

[0063] A posture monitoring system according to an embodiment includes a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors a user's posture in real time. The monitoring unit collects posture data using, for example, an acceleration sensor or a gyro sensor. The acceleration sensor can collect angle data of the user's posture using, for example, a triaxial acceleration sensor. The gyro sensor can collect position data of the user's posture using, for example, a triaxial gyro sensor. The analysis unit uses a generation AI to analyze the data collected by the monitoring unit and identify patterns of poor posture. The generation AI can analyze past data using, for example, deep learning or a neural network, and identify patterns of poor posture. The generation AI can provide personalized notifications taking into account individual data of the user. The notification unit sends a notification before the user's posture deteriorates based on the pattern identified by the analysis unit. The notification unit can send the notification using, for example, a smartphone app or a vibration function of a wearable device. As a result, the posture monitoring system according to an embodiment monitors a user's posture in real time and notifies the user before the user's posture deteriorates, thereby preventing hunched backs.

[0064] The monitoring unit can collect posture data using an acceleration sensor or a gyro sensor. The acceleration sensor can collect angle data of the user's posture using, for example, a three-axis acceleration sensor. The gyro sensor can collect position data of the user's posture using, for example, a three-axis gyro sensor. As a result, the accuracy of the posture data is improved by using the acceleration sensor or gyro sensor. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the acceleration sensor or gyro sensor into the generation AI and cause the generation AI to analyze the posture data.

[0065] The analysis unit can use the generation AI to analyze past data and identify patterns of poor posture. The generation AI can analyze past data and identify patterns of poor posture, for example, using deep learning or a neural network. The generation AI can, for example, analyze the frequency of poor posture and changes over time based on past posture data. The generation AI can, for example, provide personalized notifications by taking into account individual data of the user. This improves the accuracy of identifying patterns of poor posture by using the generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past data into the generation AI and have the generation AI identify patterns of poor posture.

[0066] The notification unit can send the notification using a smartphone app or a vibration function of a wearable device. The notification unit can send the notification using, for example, a smartphone app. The smartphone app, for example, has a notification function or an interface, and can effectively send the notification to the user. The notification unit can also send the notification using a vibration function of the wearable device. The wearable device, for example, is a smartwatch or a fitness tracker, and can send the notification to the user using a vibration function. This allows the notification to be effectively sent to the user by using the vibration function of the smartphone app or the wearable device. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can generate notification content using a generation AI and send the notification to the smartphone app or the wearable device.

[0067] The analysis unit can provide personalized notifications based on the user's individual data. The analysis unit can provide personalized notifications by taking into account individual data such as the user's height, weight, and past posture data. For example, the analysis unit can suggest the optimal timing for posture improvement based on the user's height and weight. The analysis unit can also send a notification before posture deterioration occurs based on past posture data. This enables more personalized notifications by taking into account the user's individual data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input individual data into a generation AI and have the generation AI execute personalized notification content.

[0068] The notification unit can transmit a notification just before the posture is about to deteriorate. For example, the notification unit can transmit a notification just before the posture is about to deteriorate, thereby enabling the user to take measures immediately. For example, the notification unit can transmit a notification a few seconds before the posture is about to deteriorate. The notification unit can also transmit a notification a few minutes before the posture is about to deteriorate. In this way, by transmitting a notification just before the posture is about to deteriorate, the user can take measures immediately. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can determine the timing of the notification using a generation AI and transmit the notification just before the posture is about to deteriorate.

[0069] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, when the user is feeling stressed, the monitoring unit can increase the monitoring frequency to quickly detect changes in posture. Furthermore, when the user is relaxed, the monitoring unit can reduce the monitoring frequency to reduce the burden of data collection. Furthermore, when the user is concentrating, the monitoring unit can moderately adjust the monitoring frequency so as not to interfere with the user's work. This enables more appropriate data collection by adjusting the monitoring frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring frequency.

[0070] During monitoring, the monitoring unit can adjust the sensitivity of the sensor according to the user's activity level. For example, when the user is sitting, the monitoring unit can increase the sensitivity of the sensor to detect subtle changes in posture. Furthermore, when the user is standing, the monitoring unit can set the sensitivity of the sensor to a medium level to appropriately detect changes in posture. Furthermore, when the user is walking, the monitoring unit can reduce the sensitivity of the sensor to minimize the influence of movement. This allows for more accurate posture data to be collected by adjusting the sensitivity of the sensor according to the user's activity level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's activity level data to the generation AI and cause the generation AI to adjust the sensitivity of the sensor.

[0071] During monitoring, the monitoring unit can refer to the user's past posture data and strengthen monitoring of specific postures. For example, if the user has previously assumed a posture that is prone to hunching, the monitoring unit can strengthen monitoring of that posture. Furthermore, if the user has previously assumed a posture that caused lower back pain, the monitoring unit can strengthen monitoring of that posture. Furthermore, if the user has previously assumed a posture that caused stiff shoulders, the monitoring unit can strengthen monitoring of that posture. In this way, by referring to past posture data, monitoring of specific postures can be strengthened. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past posture data into the generation AI and cause the generation AI to strengthen monitoring of specific postures.

[0072] During monitoring, the monitoring unit can change the monitoring method based on the user's device usage status. For example, when the user is using a personal computer, the monitoring unit can focus on monitoring the posture of the upper body. Furthermore, when the user is using a smartphone, the monitoring unit can focus on monitoring the posture of the neck and shoulders. Furthermore, when the user is using a tablet, the monitoring unit can monitor the posture of the entire body in a balanced manner. This allows more appropriate posture data to be collected by changing the monitoring method based on the device usage status. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's device usage status data into the generation AI and cause the generation AI to change the monitoring method.

[0073] The monitoring unit can estimate the user's emotions and prioritize the monitoring data based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring posture data related to stress reduction. Furthermore, if the user is relaxed, the monitoring unit can prioritize monitoring posture data for maintaining a relaxed state. Furthermore, if the user is concentrating, the monitoring unit can prioritize monitoring posture data for maintaining concentration. Thus, by prioritizing the monitoring data based on the user's emotions, more important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the monitoring data.

[0074] During monitoring, the monitoring unit can perform monitoring according to the environment, taking into account the user's geographical location information. For example, when the user is in an office, the monitoring unit can monitor a posture suitable for desk work. Furthermore, when the user is at home, the monitoring unit can monitor a relaxed posture. Furthermore, when the user is in a cafe, the monitoring unit can monitor a posture in which the user sits for a long time. This enables appropriate monitoring according to the environment by taking the geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform monitoring according to the environment.

[0075] During monitoring, the monitoring unit can analyze the user's social media activity and collect related posture data. For example, if the user posts on social media for a long time, the monitoring unit can monitor the user's sitting posture. Furthermore, if the user is taking photos on social media, the monitoring unit can monitor the user's standing posture. Furthermore, if the user is watching videos on social media, the monitoring unit can monitor the user's relaxed posture. In this way, related posture data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related posture data.

[0076] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit can, for example, adjust the frequency of monitoring based on feedback provided by the user in the past. The monitoring unit can also change the target area for monitoring based on feedback provided by the user in the past. Furthermore, the monitoring unit can customize the monitoring method based on feedback provided by the user in the past. This allows the monitoring method to be customized by reflecting past feedback. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the monitoring method.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize data related to stress reduction during analysis. Furthermore, if the user is relaxed, the analysis unit can prioritize data for maintaining a relaxed state during analysis. Furthermore, if the user is concentrating, the analysis unit can prioritize data for maintaining concentration during analysis. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0078] During analysis, the analysis unit can analyze the variation pattern of the posture data in detail and identify a more accurate pattern. The analysis unit can, for example, analyze minute variations in the posture data and identify signs of posture deterioration. The analysis unit can also analyze long-term variation patterns of the posture data and identify a tendency for posture deterioration. Furthermore, the analysis unit can analyze short-term variation patterns of the posture data and identify the moment when posture deterioration occurs. In this way, by analyzing the variation pattern of the posture data in detail, a more accurate pattern can be identified. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the posture data to a generation AI and cause the generation AI to perform a detailed analysis of the variation pattern.

[0079] During analysis, the analysis unit can personalize the analysis results by taking into account the user's lifestyle and activity schedule. The analysis unit can, for example, suggest optimal timing for posture improvement based on the user's lifestyle. The analysis unit can also suggest actions for posture improvement based on the user's activity schedule. Furthermore, the analysis unit can integrate the user's lifestyle and activity schedule to suggest an optimal posture improvement plan. This allows for more personalized analysis results to be obtained by taking into account the lifestyle and activity schedule. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle and activity schedule data into the generation AI and have the generation AI personalize the analysis results.

[0080] During analysis, the analysis unit can integrate data from different sensors to perform a more comprehensive analysis. For example, the analysis unit can integrate data from an acceleration sensor and a gyro sensor to perform a detailed analysis of posture fluctuations. The analysis unit can also integrate data from a heart rate sensor and a posture sensor to analyze the relationship between stress and posture. Furthermore, the analysis unit can integrate data from a temperature sensor and a posture sensor to analyze the relationship between the environment and posture. This enables a more comprehensive analysis by integrating data from different sensors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from different sensors into a generation AI and have the generation AI perform a comprehensive analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is concentrating, the analysis unit can provide a display method that focuses on the main points. This enables more appropriate information to be provided by adjusting the display method of the analysis results based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0082] During analysis, the analysis unit can perform analysis appropriate to the environment, taking into account the user's geographical location information. For example, if the user is in an office, the analysis unit can analyze posture data suitable for desk work. Furthermore, if the user is at home, the analysis unit can analyze relaxed posture data. Furthermore, if the user is in a cafe, the analysis unit can analyze posture data in which the user is sitting for a long time. This allows appropriate analysis appropriate to the environment by taking into account the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform an analysis appropriate to the environment.

[0083] During the analysis, the analysis unit can analyze the user's social media activity and reflect related data in the analysis. For example, if the user posts on social media for a long period of time, the analysis unit can analyze posture data related to that activity. Furthermore, if the user takes photos on social media, the analysis unit can analyze posture data related to that activity. Furthermore, if the user watches videos on social media, the analysis unit can analyze posture data related to that activity. In this way, by analyzing social media activity, related data can be reflected in the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and have the generation AI analyze the related data.

[0084] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit can, for example, adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also change the target data for analysis based on feedback provided by the user in the past. Furthermore, the analysis unit can customize the analysis method based on feedback provided by the user in the past. This allows the analysis algorithm to be customized by reflecting past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and have the generation AI customize the analysis algorithm.

[0085] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, if the user is feeling stressed, the notification unit can send a notification early, before the user's posture deteriorates. Furthermore, if the user is relaxed, the notification unit can send a notification just before the user's posture deteriorates. Furthermore, if the user is concentrating, the notification unit can send a notification just before the user's posture deteriorates. This allows the notification to be sent at a more appropriate time by adjusting the timing of the notification based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of the notification.

[0086] The notification unit can change the notification method depending on the user's current activity status when notifying the user. For example, when the user is at work, the notification unit can send a quiet vibration notification. Furthermore, when the user is taking a break, the notification unit can send a voice notification. Furthermore, when the user is exercising, the notification unit can send a visual notification. This allows for more effective notification by changing the notification method depending on the current activity status. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's current activity status data into the generation AI and have the generation AI change the notification method.

[0087] At the time of notification, the notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit can prioritize the selection of a notification method that the user has used favorably in the past. The notification unit can also select a notification method that the user has found effective in the past. Furthermore, the notification unit can select the optimal notification method based on feedback provided by the user in the past. In this way, the optimal notification method can be selected by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0088] The notification unit can customize the notification method based on the user's device usage status when notifying the user. For example, if the user is using a smartphone, the notification unit can send an app notification. Furthermore, if the user is using a wearable device, the notification unit can send a vibration notification. Furthermore, if the user is using a personal computer, the notification unit can send a desktop notification. This enables more effective notification by customizing the notification method based on the device usage status. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device usage status data into the generation AI and have the generation AI customize the notification method.

[0089] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can send a notification including advice to relax. Furthermore, if the user is relaxed, the notification unit can send a notification including simple advice to maintain posture. Furthermore, if the user is concentrating, the notification unit can send a notification including advice to maintain concentration. This enables more appropriate notifications by adjusting the notification content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification content.

[0090] When providing a notification, the notification unit can provide a notification appropriate to the environment, taking into account the user's geographical location information. For example, if the user is in an office, the notification unit can send a notification to improve posture suitable for desk work. Furthermore, if the user is at home, the notification unit can send a notification to improve posture to relax. Furthermore, if the user is in a cafe, the notification unit can send a notification to improve posture for sitting for long periods of time. This makes it possible to provide an appropriate notification appropriate to the environment by taking into account the geographical location information. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's geographical location information data into a generation AI and cause the generation AI to execute a notification appropriate to the environment.

[0091] The notification unit can analyze the user's social media activity at the time of notification and reflect related information in the notification. For example, if the user posts on social media for a long time, the notification unit can send a posture improvement notification related to that activity. Furthermore, if the user takes photos on social media, the notification unit can send a posture improvement notification related to that activity. Furthermore, if the user watches videos on social media, the notification unit can send a posture improvement notification related to that activity. In this way, by analyzing social media activity, related information can be reflected in the notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's social media activity data into a generation AI and cause the generation AI to notify the user of related information.

[0092] The notification unit can customize the notification method by reflecting the user's past feedback when notifying. The notification unit can adjust the timing of the notification based on, for example, feedback provided by the user in the past. The notification unit can also change the content of the notification based on feedback provided by the user in the past. The notification unit can also customize the notification method based on feedback provided by the user in the past. This allows the notification method to be customized by reflecting past feedback. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the notification method. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit collects posture data using an acceleration sensor or gyro sensor of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes past data using a generation AI to identify patterns of poor posture. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and sends a notification using a smartphone app or the vibration function of a wearable device. === Hard Collateral 1-2 === Each of the multiple elements including the monitoring unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit collects posture data using an acceleration sensor or gyro sensor of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes past data using a generation AI to identify patterns of poor posture. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends a notification using a smartphone app or the vibration function of the wearable device. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, analysis unit, and notification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the monitoring unit collects posture data using an acceleration sensor or gyro sensor of the headset-type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes past data using a generation AI to identify patterns of poor posture. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and sends a notification using a smartphone app or the vibration function of the wearable device. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit collects posture data using an acceleration sensor or gyro sensor of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes past data using a generation AI to identify patterns of poor posture. The notification unit is realized, for example, by the control unit 46A of the robot 414, and sends a notification using a smartphone app or the vibration function of a wearable device.

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

[0094] The monitoring unit can simultaneously monitor the user's breathing pattern while collecting the user's posture data. For example, a breathing sensor can be used to measure the depth and rhythm of the user's breathing and integrate the data with the posture data. Furthermore, if a change in the breathing pattern is associated with poor posture, the analysis unit can take this into account in its analysis. Furthermore, the notification unit can suggest breathing techniques to the user to help them relax based on the change in the breathing pattern. This allows the posture monitoring system to provide more comprehensive health management by taking the user's breathing pattern into account.

[0095] The analysis unit can take into account the user's dietary and hydration data when analyzing the user's posture data. For example, the analysis unit can analyze posture data immediately after the user has eaten or drank a meal and identify the impact of these factors on posture. The analysis unit can also suggest optimal timing for improving posture based on the timing of meals and hydration. Furthermore, the notification unit can provide advice for maintaining proper posture after the user has eaten or drank a meal. This allows the posture monitoring system to take into account the user's dietary and hydration data, enabling more personalized health management.

[0096] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, a notification containing advice to relax can be sent. If the user is relaxed, a notification containing simple advice to maintain posture can be sent. If the user is concentrating, a notification containing advice to maintain concentration can be sent. This allows for more appropriate notification by adjusting the notification content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification content.

[0097] The monitoring unit can simultaneously monitor the user's muscle tension when collecting the user's posture data. For example, the monitoring unit can measure the user's muscle tension using an electromyography sensor and integrate the data with the posture data. Furthermore, if changes in muscle tension are related to poor posture, the analysis unit can take this into account when performing analysis. Furthermore, the notification unit can provide the user with advice on stretching and relaxation based on changes in muscle tension. This allows the posture monitoring system to take the user's muscle tension into account, enabling more comprehensive health management.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can prioritize data related to stress reduction. Furthermore, if the user is relaxed, the analysis can prioritize data for maintaining a relaxed state. Furthermore, if the user is concentrating, the analysis can prioritize data for maintaining concentration. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0099] The monitoring unit can simultaneously monitor the user's heart rate while collecting the user's posture data. For example, the heart rate can be measured using a heart rate sensor and integrated with the posture data. If changes in heart rate are related to poor posture, the analysis unit can take this into account when performing analysis. Furthermore, the notification unit can provide the user with advice on how to relax based on changes in heart rate. This allows the posture monitoring system to take the user's heart rate into account for more comprehensive health management.

[0100] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, a notification can be sent early, before the user's posture deteriorates. Furthermore, if the user is relaxed, a notification can be sent just before the user's posture deteriorates. Furthermore, if the user is concentrating, a notification can be sent just before the user's posture deteriorates. By adjusting the timing of notifications based on the user's emotions, notifications can be sent at more appropriate times. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of notifications.

[0101] The monitoring unit can simultaneously monitor the user's skin temperature when collecting the user's posture data. For example, a temperature sensor can be used to measure the user's skin temperature and integrate it with the posture data. If changes in skin temperature are related to poor posture, the analysis unit can take this into account when performing analysis. Furthermore, the notification unit can provide the user with advice on maintaining proper posture based on changes in skin temperature. This allows the posture monitoring system to provide more comprehensive health management by taking the user's skin temperature into account.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. Furthermore, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is concentrating, a display method that focuses on the main points can be provided. By adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0103] The monitoring unit can simultaneously monitor the user's activity level while collecting the user's posture data. For example, an activity tracker can be used to measure the user's steps and exercise volume, and the data can be integrated with the posture data. Furthermore, if a change in activity level is related to poor posture, the analysis unit can take this into account when analyzing the data. Furthermore, the notification unit can provide the user with advice on maintaining proper posture based on the change in activity level. This allows the posture monitoring system to take the user's activity level into account, enabling more comprehensive health management.

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

[0105] Step 1: The monitoring unit monitors the user's posture in real time. The monitoring unit collects posture data using an acceleration sensor and a gyro sensor. The acceleration sensor collects angle data of the user's posture using a 3-axis acceleration sensor, and the gyro sensor collects position data of the user's posture using a 3-axis gyro sensor. Step 2: The analysis unit uses the generation AI to analyze the data collected by the monitoring unit and identify patterns of poor posture. The generation AI then analyzes past data using deep learning and neural networks, and provides personalized notifications taking into account the user's individual data. Step 3: The notification unit sends a notification before the posture deteriorates based on the pattern identified by the analysis unit. The notification unit can send the notification using a smartphone app or the vibration function of a wearable device.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a monitoring unit that monitors posture data in real time; an analysis unit that analyzes the data collected by the monitoring unit and identifies patterns of poor posture; a notification unit that sends a notification before the posture is disrupted based on the pattern identified by the analysis unit; Equipped with A system characterized by:

2. The monitoring unit Use an accelerometer or gyro sensor to collect attitude data 2. The system of claim 1.

3. The analysis unit Generative AI is used to analyze past data and identify patterns of poor posture.

2. The system of claim 1.

4. The notification unit Send notifications via a smartphone app or the vibration function of a wearable device 2. The system of claim 1.

5. The analysis unit Send personalized notifications based on individual user data 2. The system of claim 1.

6. The notification unit Send a notification just before your posture collapses 2. The system of claim 1.

7. The monitoring unit Estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions.

2. The system of claim 1.

8. The monitoring unit During monitoring, adjust the sensitivity of the sensor according to the user's activity level 2. The system of claim 1.

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