system

Abstract

The system according to this embodiment aims to estimate the user's health status and healthy lifestyle based on shoe prints and the wear pattern of shoe soles, and to provide recommendations to the user. [Solution] The system according to the embodiment comprises a detection unit, an analysis unit, an estimation unit, and a recommendation unit. The detection unit detects shoe prints and the wear pattern of shoe soles. The analysis unit analyzes the data detected by the detection unit. The estimation unit estimates the health status and health lifestyle based on the analysis results obtained by the analysis unit. The recommendation unit makes recommendations to the user based on the results estimated by the estimation unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully possible to infer the health condition and health life from the shoe prints and the wear pattern of the shoe sole, and there is room for improvement.

[0005] The system according to the embodiment aims to infer the health condition and health life based on the shoe prints and the wear pattern of the shoe sole, and give advice to the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, an analysis unit, an estimation unit, and a recommendation unit. The detection unit detects shoe prints and the wear pattern on the soles of shoes. The analysis unit analyzes the data detected by the detection unit. The estimation unit estimates the user's health status and healthy lifestyle based on the analysis results obtained by the analysis unit. The recommendation unit makes recommendations to the user based on the results estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to this embodiment can estimate a user's health status and lifestyle based on shoe prints and the wear pattern of shoe soles, and make recommendations to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses a sensor-equipped entrance sheet to detect shoe prints and the wear pattern on shoe soles, and quantifies walking and standing posture. The system then analyzes this data to estimate the user's health status and healthy lifestyle, and makes appropriate recommendations to the user. For example, if the user has poor walking balance, the system can suggest appropriate shoe selection and improvements to their walking method. Furthermore, when this idea is used in a store, the store can provide appropriate services based on the analyzed data. For example, a shoe store can suggest the optimal shoes based on the customer's walking data. First, a sensor-equipped entrance sheet detects shoe prints and the wear pattern on shoe soles, and quantifies walking and standing posture. Next, the system analyzes this data to estimate the user's health status and healthy lifestyle. Based on the estimation results, the system makes appropriate recommendations to the user. For example, if the user has poor walking balance, the system can suggest appropriate shoe selection and improvements to their walking method. Furthermore, when this idea is used in a store, the store can provide appropriate services based on the analyzed data. For example, a shoe store can suggest the optimal shoes based on the customer's walking data. This allows the system to detect shoe prints and the wear pattern on shoe soles, estimate the user's health status and lifestyle, and provide appropriate recommendations.

[0029] The system according to this embodiment comprises a detection unit, an analysis unit, an estimation unit, and a recommendation unit. The detection unit detects shoe prints and the wear pattern of shoe soles. The detection unit can, for example, use a pressure sensor or an acceleration sensor to detect shoe prints and the wear pattern of shoe soles. The detection unit can, for example, use a pressure sensor to detect the pressure distribution of shoe prints. The detection unit can also detect the wear pattern of shoe soles using an acceleration sensor. The detection unit can, for example, use a pressure sensor to detect the pressure distribution of shoe prints and an acceleration sensor to detect the wear pattern of shoe soles. The analysis unit analyzes the data detected by the detection unit. The analysis unit can, for example, use AI to analyze the data. The analysis unit can, for example, use machine learning to analyze the data. The analysis unit can also analyze the data using deep learning. The analysis unit can, for example, use machine learning to analyze the data and use deep learning to analyze the data. The estimation unit estimates health status and healthy lifestyle based on the analysis results obtained by the analysis unit. The prediction unit can, for example, use AI to predict health status and health lifestyle. The prediction unit can, for example, use machine learning to predict health status and health lifestyle. The prediction unit can also predict health status and health lifestyle using deep learning. The prediction unit can, for example, use machine learning to predict health status and health lifestyle, and use deep learning to predict health status and health lifestyle. The recommendation unit makes recommendations to the user based on the results predicted by the prediction unit. The recommendation unit can, for example, suggest how to choose appropriate shoes or how to improve walking methods if the user has poor walking balance. The recommendation unit can, for example, suggest how to choose appropriate shoes or how to improve walking methods if the user has poor walking balance. The recommendation unit can, for example, suggest how to choose appropriate shoes and how to improve walking methods if the user has poor walking balance. As a result, the system according to the embodiment can detect shoe prints and the wear pattern on the soles of shoes, predict health status and health lifestyle, and make recommendations to the user as appropriate.

[0030] The detection unit detects shoe prints and the wear pattern on shoe soles. Specifically, it can use pressure sensors and acceleration sensors to detect shoe prints and the wear pattern on shoe soles. Pressure sensors are used to detect the pressure distribution of shoe prints in detail. For example, multiple pressure sensors placed on the sole of the shoe measure the pressure distribution on the sole of the foot in real time during walking and collect the data. This allows for an accurate understanding of foot movement and weight distribution during walking. Acceleration sensors are used to detect the wear pattern on shoe soles. Acceleration sensors capture the movement of the sole of the shoe in three dimensions and measure acceleration and vibration during walking. This allows for a detailed analysis of the sole wear pattern and walking habits. Furthermore, the detection unit centrally manages the data obtained from these sensors and can collect and analyze the data in real time. For example, by combining data from pressure sensors and acceleration sensors, it is possible to comprehensively understand foot movement, weight distribution, and sole wear patterns during walking. This allows the detection unit to precisely detect shoe prints and the wear pattern on shoe soles, which can be used to assess walking balance and overall health.

[0031] The analysis unit analyzes the data detected by the detection unit. Specifically, it can use AI to analyze the data. The AI ​​uses machine learning and deep learning techniques to analyze the collected data in detail. For example, machine learning algorithms can be used to extract walking patterns from pressure distribution and acceleration data during walking, and to detect abnormal walking patterns and imbalances. Furthermore, deep learning enables more advanced data analysis. Deep learning uses multi-layered neural networks to analyze data and automatically learn complex patterns and features. This allows for highly accurate detection of subtle movements and changes in weight distribution during walking. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term changes and trends in walking patterns. For example, based on historical data, it can track changes in walking patterns over a specific period and detect early signs of changes in health status or risks. This allows the analysis unit to analyze the data obtained from the detection unit in detail and use it to evaluate walking balance and health status.

[0032] The prediction unit predicts health status and healthy lifestyle based on the analysis results obtained by the analysis unit. Specifically, it can use AI to predict health status and healthy lifestyle. The AI ​​uses machine learning and deep learning techniques to predict information about health status and healthy lifestyle from the analysis results. For example, using machine learning algorithms, it can evaluate the state of leg muscle strength and balance from data on walking patterns and pressure distribution, and predict health status. Furthermore, using deep learning enables more sophisticated predictions. Deep learning analyzes data using multi-layered neural networks and can automatically learn complex patterns and features. This allows for high-precision detection of subtle movements and changes in weight distribution during walking, and predicts information about health status and healthy lifestyle. In addition, the prediction unit can utilize past data and statistical information to detect long-term changes in health status and signs of risk early. For example, based on past data, it can track changes in walking patterns over a specific period and detect changes in health status and signs of risk early. As a result, the prediction unit can accurately predict information about health status and healthy lifestyle based on data obtained from the analysis unit, and provide information to take appropriate measures.

[0033] The recommendation unit makes recommendations to users based on the results inferred by the prediction unit. Specifically, if a user has poor walking balance, the recommendation unit can suggest appropriate shoe selection and improvements to their walking method. For example, if a user has poor walking balance, the recommendation unit can suggest appropriate shoe selection. For example, it can suggest shoe selection that suits the shape of the foot and walking pattern, thereby improving walking balance. The recommendation unit can also suggest improvements to walking method if a user has poor walking balance. For example, it can provide specific advice on improving posture and foot movement while walking, thereby improving walking balance. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, it can review and improve its recommendations based on feedback from users who have received recommendations, providing more effective recommendations. In addition, the recommendation unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the recommendation unit to provide users with recommendations quickly and reliably, supporting improvements in their health and healthy lifestyle.

[0034] The detection unit can detect shoe prints and the wear pattern on shoe soles using pressure sensors and acceleration sensors. For example, the detection unit can detect the pressure distribution of shoe prints using a pressure sensor. For example, the detection unit can detect the wear pattern on shoe soles using an acceleration sensor. For example, the detection unit can detect the pressure distribution of shoe prints using a pressure sensor and detect the wear pattern on shoe soles using an acceleration sensor. This allows for accurate detection of shoe prints and the wear pattern on shoe soles by using pressure sensors and acceleration sensors. Pressure sensors include, but are not limited to, piezo sensors and capacitive sensors. Acceleration sensors include, but are not limited to, MEMS sensors and piezoresistive sensors. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data acquired from pressure sensors and acceleration sensors into a generating AI, and the generating AI can analyze the data.

[0035] The analysis unit can analyze data using AI. The analysis unit can analyze data using, for example, machine learning. The analysis unit can analyze data using, for example, deep learning. The analysis unit can analyze data using, for example, machine learning and deep learning. This improves the accuracy of data analysis by using AI. AI includes, but is not limited to, machine learning and deep learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data acquired from the detection unit into a generation AI, and the generation AI can perform data analysis.

[0036] The recommendation unit can suggest appropriate shoe selection and improvements to walking methods if walking balance is poor. For example, the recommendation unit can suggest appropriate shoe selection if walking balance is poor. For example, the recommendation unit can suggest improvements to walking methods if walking balance is poor. For example, the recommendation unit can suggest appropriate shoe selection and improvements to walking methods if walking balance is poor. This allows for improvements in the user's health by suggesting appropriate shoe selection and improvements to walking methods when walking balance is poor. Walking balance includes, but is not limited to, the position of the center of gravity and left-right balance. Some or all of the processing described above in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input data obtained from the prediction unit into a generation AI, and the generation AI can generate recommendation content.

[0037] The service provision department can provide appropriate services based on the data analyzed by the store. For example, the service provision department can suggest the best shoes based on the customer's walking data in a shoe store. For example, the service provision department can suggest the best shoes based on the customer's walking data in a shoe store. For example, the service provision department can suggest the best shoes based on the customer's walking data in a shoe store. This allows the store to improve customer satisfaction by providing appropriate services based on the data analyzed by the store. Appropriate services include, but are not limited to, advice on choosing shoes and walking instruction. Some or all of the above processing in the service provision department may be performed using AI, for example, or without AI. For example, the service provision department can input data obtained from the analysis department into a generation AI, and the generation AI can generate service content.

[0038] The service provider can suggest the most suitable shoes based on the customer's walking data at a shoe store. The service provider can, for example, suggest the most suitable shoes based on the customer's walking data at a shoe store. The service provider can, for example, suggest the most suitable shoes based on the customer's walking data at a shoe store. This allows the service provider to provide the customer with the most suitable shoes by suggesting the most suitable shoes based on the customer's walking data at a shoe store. Optimal shoes include, but are not limited to, shoes that fit the shape of the foot or shoes suited to the intended use. Some or all of the above processing in the service provider can be performed using, for example, AI, or without AI. For example, the service provider can input customer walking data into a generating AI, and the generating AI can suggest the most suitable shoes.

[0039] The detection unit can improve detection accuracy by considering the user's physical information, such as weight and height, during detection. For example, the detection unit can adjust the sensitivity of the pressure sensor by considering the user's weight. For example, the detection unit can correct stride length data by considering the user's height. For example, the detection unit can correct the wear data of the soles of shoes based on the user's physical information. This improves detection accuracy by considering the user's physical information, such as weight and height. Physical information includes, but is not limited to, weight, height, and BMI. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's physical information into a generating AI, and the generating AI can correct the data.

[0040] The detection unit can measure the user's walking speed and stride length in real time upon detection and reflect this in the data. For example, the detection unit can measure the user's walking speed in real time and reflect this in the data. For example, the detection unit can measure the user's stride length in real time and reflect this in the data. For example, the detection unit can combine the user's walking speed and stride length to analyze the walking pattern in detail. This allows for the acquisition of more detailed data by measuring the user's walking speed and stride length in real time. Examples of walking speed include, but are not limited to, the placement of sensors and measurement frequency. Examples of stride length include, but are not limited to, the placement of sensors and measurement frequency. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data on the user's walking speed and stride length into a generating AI, which can then analyze the data.

[0041] The detection unit can adjust its detection method when detecting footwear, taking into account the type and material of the user's shoes. For example, in the case of leather shoes, the detection unit can increase the sensitivity of the pressure sensor to acquire detailed data. For example, in the case of sneakers, the detection unit can adjust the sensitivity of the acceleration sensor to analyze the walking pattern in detail. For example, in the case of sandals, the detection unit can prioritize detecting footprint data. This allows the detection method to be appropriately adjusted by taking into account the type and material of the user's shoes. The types of shoes include, but are not limited to, running shoes and business shoes. The materials include, but are not limited to, leather and synthetic materials. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input data on the type and material of the user's shoes into a generating AI, and the generating AI can adjust the detection method.

[0042] The detection unit can improve detection accuracy by considering the user's walking environment (e.g., indoors or outdoors, type of ground) during detection. For example, indoors, the detection unit can increase the sensitivity of the pressure sensor to acquire more detailed data. For example outdoors, the detection unit can adjust the sensitivity of the acceleration sensor to analyze the walking pattern in detail. For example, if the ground is slippery, the detection unit can prioritize detecting walking speed data. This improves detection accuracy by considering the user's walking environment. The walking environment includes, but is not limited to, indoors or outdoors and type of ground. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data on the user's walking environment into a generating AI, which can then improve detection accuracy.

[0043] The analysis unit can detect anomalies by comparing current data with past data during analysis and identify changes in health status. For example, the analysis unit can detect abnormalities in walking patterns by comparing them with past data. For example, the analysis unit can detect abnormalities in the wear pattern of shoe soles by comparing them with past data. For example, the analysis unit can detect abnormalities in standing posture by comparing them with past data. In this way, changes in health status can be identified by detecting anomalies by comparing them with past data. Anomalies include, but are not limited to, threshold settings and definitions of anomalies. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past data into a generating AI, and the generating AI can detect anomalies.

[0044] The analysis unit can interpret data while considering the user's lifestyle and exercise history. For example, the analysis unit can interpret walking pattern data while considering the user's exercise history. For example, the analysis unit can interpret shoe sole wear data while considering the user's lifestyle. For example, the analysis unit can interpret standing posture data by combining the user's exercise history and lifestyle. This makes the interpretation of data more accurate by considering the user's lifestyle and exercise history. Lifestyle includes, but is not limited to, dietary patterns and exercise habits. Exercise history includes, but is not limited to, the frequency and type of exercise. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data on the user's lifestyle and exercise history into a generating AI, and the generating AI can interpret the data.

[0045] The analysis unit can interpret data while considering the user's age and gender during analysis. For example, the analysis unit can interpret walking pattern data while considering the user's age. For example, the analysis unit can interpret shoe sole wear data while considering the user's gender. For example, the analysis unit can interpret standing posture data by combining the user's age and gender. This makes the interpretation of data more accurate by considering the user's age and gender. Age includes, but is not limited to, age-specific criteria and age-appropriate adjustments. Gender includes, but is not limited to, gender-specific criteria and gender-appropriate adjustments. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's age and gender data into a generating AI, and the generating AI can interpret the data.

[0046] The analysis unit can evaluate the user's health status by considering their eating and sleeping patterns during analysis. For example, the analysis unit can interpret walking pattern data by considering the user's eating patterns. For example, the analysis unit can interpret shoe sole wear data by considering the user's sleeping patterns. For example, the analysis unit can interpret standing posture data by combining the user's eating and sleeping patterns. This makes the evaluation of health status more accurate by considering the user's eating and sleeping patterns. Eating includes, but is not limited to, the content and frequency of meals. Sleep patterns include, but are not limited to, the duration and quality of sleep. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's eating and sleeping pattern data into a generating AI, and the generating AI can evaluate the health status.

[0047] The prediction unit can improve its prediction accuracy by referring to the user's past health check results during prediction. For example, the prediction unit can predict the current health status based on past health check results. For example, the prediction unit can evaluate changes in health status by comparing past health check results with current data. For example, the prediction unit can predict future health risks by referring to past health check results. This improves prediction accuracy by referring to the user's past health check results. Health check results include, but are not limited to, the types and methods of reference of past check results. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or without AI. For example, the prediction unit can input the user's past health check results into a generating AI, and the generating AI can improve the prediction accuracy.

[0048] The prediction unit can assess health risks by considering the user's family history and genetic information during prediction. For example, the prediction unit can assess genetic health risks by considering the user's family history. For example, the prediction unit can predict specific health risks based on the user's genetic information. For example, the prediction unit can assess overall health risks by combining the user's family history and genetic information. This makes the assessment of health risks more accurate by considering the user's family history and genetic information. Family history includes, but is not limited to, the health status and genetic factors of family members. Genetic information includes, but is not limited to, the results of genetic tests and genetic risks. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input the user's family history and genetic information into a generating AI, and the generating AI can perform the health risk assessment.

[0049] The estimation unit can assess the user's health status by considering their occupation and daily activity level during estimation. For example, the estimation unit can assess health risks by considering the user's occupation. For example, the estimation unit can estimate the health status based on the user's daily activity level. For example, the estimation unit can assess the overall health status by combining the user's occupation and activity level. This makes the assessment of health status more accurate by considering the user's occupation and daily activity level. Occupation includes, but is not limited to, occupation-specific standards and occupation-specific adjustments. Daily activity level includes, but is not limited to, activity volume and type of activity. Some or all of the above processing in the estimation unit may be performed using, for example, AI, or not using AI. For example, the estimation unit can input data on the user's occupation and daily activity level into a generating AI, and the generating AI can perform the health status assessment.

[0050] The estimation unit can estimate a user's health life by considering the user's stress level and psychological state during estimation. For example, the estimation unit can estimate a user's health status by considering the user's stress level. For example, the estimation unit can evaluate a user's health life based on the user's psychological state. For example, the estimation unit can estimate an overall health life by combining the user's stress level and psychological state. This makes the estimation of health life more accurate by considering the user's stress level and psychological state. Stress level includes, but is not limited to, examples of stress assessment methods and the effects of stress. Psychological state includes, but is not limited to, examples of psychological test results and psychological evaluations. Some or all of the above processing in the estimation unit may be performed using, for example, AI, or not using AI. For example, the estimation unit can input data on the user's stress level and psychological state into a generating AI, and the generating AI can perform the estimation of health life.

[0051] The recommendation unit can make optimal recommendations by referring to the user's past behavioral history when making recommendations. For example, the recommendation unit can make recommendations for maintaining health based on past behavioral history. For example, the recommendation unit can compare past behavioral history with current data and propose areas for improvement. For example, the recommendation unit can make easy-to-implement recommendations by referring to past behavioral history. This makes it possible to make optimal recommendations by referring to the user's past behavioral history. Behavioral history includes, but is not limited to, past behavioral data and behavioral patterns. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's past behavioral history into a generating AI, and the generating AI can make optimal recommendations.

[0052] The recommendation department can propose specific improvement measures when making recommendations, taking into account the user's living environment and lifestyle. For example, the recommendation department can propose appropriate exercise methods, taking into account the user's living environment. For example, the recommendation department can propose dietary improvements based on the user's lifestyle. For example, the recommendation department can propose comprehensive health improvement measures by combining the user's living environment and lifestyle. This allows for the proposal of specific improvement measures by taking into account the user's living environment and lifestyle. The living environment includes, but is not limited to, the living environment and lifestyle habits. The lifestyle includes, but is not limited to, daily activity patterns and hobbies. Some or all of the above processing in the recommendation department may be performed using, for example, AI, or not using AI. For example, the recommendation department can input data on the user's living environment and lifestyle into a generating AI, and the generating AI can propose specific improvement measures.

[0053] The recommendation unit can customize the content of its recommendations by taking into account the user's health goals and preferences. For example, the recommendation unit can propose a specific exercise plan considering the user's health goals. For example, the recommendation unit can propose dietary improvements based on the user's preferences. For example, the recommendation unit can propose comprehensive health improvement measures by combining the user's health goals and preferences. In this way, the content of the recommendations can be customized by taking into account the user's health goals and preferences. Health goals include, but are not limited to, methods for setting goals and methods for evaluating goal achievement. Preferences include, but are not limited to, the types of preferences and their priority. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input data on the user's health goals and preferences into a generating AI, and the generating AI can customize the content of the recommendations.

[0054] The recommendation department can adjust its recommendations when making them, taking into account the user's community and support network. For example, the recommendation department can propose group exercises, taking into account the user's community. For example, the recommendation department can make recommendations that can be implemented with support, based on the user's support network. For example, the recommendation department can propose comprehensive health improvement measures, combining the user's community and support network. This makes it possible to make recommendations that are easier to implement by taking into account the user's community and support network. The community includes, but is not limited to, groups to which the user belongs and support networks. The support network includes, but is not limited to, types of supporters and the content of support. Some or all of the above processing in the recommendation department may be performed using, for example, AI, or not using AI. For example, the recommendation department can input data on the user's community and support network into a generating AI, and the generating AI can adjust the recommendation content.

[0055] The service provider can provide the most suitable service by referring to the user's past service usage history when providing a service. For example, the service provider can propose the most suitable service based on the user's past service usage history. For example, the service provider can provide an appropriate service by comparing the user's past service usage history with current data. For example, the service provider can provide a service that suits the user by referring to the user's past service usage history. This allows the service provider to provide the most suitable service by referring to the user's past service usage history. Service usage history includes, but is not limited to, past usage data and usage patterns. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past service usage history into a generating AI, and the generating AI can provide the most suitable service.

[0056] The service provider can collect user feedback in real time during service provision and improve the service content. For example, the service provider can collect user feedback in real time and immediately improve the service content. For example, the service provider can improve the quality of the service based on user feedback. For example, the service provider can analyze user feedback and propose long-term service improvement measures. This allows for immediate improvement of the service content by collecting user feedback in real time. Feedback includes, but is not limited to, real-time collection and feedback evaluation methods. Some or all of the above processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user feedback into a generating AI, and the generating AI can improve the service content.

[0057] The service provider can provide the most suitable service by considering the user's geographical location information when providing the service. For example, the service provider can suggest nearby services based on the user's geographical location information. For example, the service provider can provide an appropriate service by comparing the user's geographical location information with current data. For example, the service provider can provide a service that can be used even while on the move by referring to the user's geographical location information. In this way, the service provider can provide the most suitable service by considering the user's geographical location information. Geographical location information includes, but is not limited to, the method of acquiring location information and the method of using location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can provide the most suitable service.

[0058] The service provider can analyze the user's social media activity and suggest relevant services when providing services. For example, the service provider can analyze the user's social media activity and suggest services based on their interests. For example, the service provider can compare the user's social media activity with current data to provide appropriate services. For example, the service provider can refer to the user's social media activity to provide services that are in line with current trends. In this way, relevant services can be suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of posted content and analysis of activity patterns. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI, and the generating AI can suggest relevant services.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The detection unit detects the sounds a user makes while walking and analyzes the sound patterns to infer their walking balance and health status. For example, if the walking sounds are irregular, it may indicate poor walking balance, allowing the system to suggest appropriate shoe selection and improvements to walking technique. If the walking sounds are louder than usual, it may indicate worn-out shoe soles, suggesting the purchase of new shoes. Furthermore, if the walking sounds are light and airy, it can infer good health and provide advice for maintaining that health. In this way, detecting walking sounds allows for more detailed inferences about a person's health status.

[0061] The analysis department can acquire vibration data from users while they walk and evaluate their health status by analyzing the vibration patterns. For example, if the vibrations during walking are strong, it may indicate strain on the feet or knees, allowing for suggestions on appropriate shoe selection and improvements to walking technique. Irregular vibrations may indicate poor balance during walking, allowing for suggestions on exercises to improve balance. Furthermore, if the vibrations are light, it can be inferred that the user is in good health, and advice for maintaining good health can be provided. In this way, analyzing vibration data enables a more detailed evaluation of health status.

[0062] The detection unit can detect changes in the user's body temperature while walking and analyze the data to assess their health status. For example, if body temperature rises rapidly while walking, it may indicate poor health, and a rest can be suggested. If body temperature drops, it may indicate poor circulation, and appropriate cold weather protection measures can be suggested. Furthermore, if body temperature remains stable, it can be inferred that the user is in good health, and advice for maintaining good health can be provided. In this way, detecting changes in body temperature allows for a more detailed assessment of health status.

[0063] The analysis department can acquire heart rate data from users while they are walking and analyze that data to assess their health status. For example, if the heart rate during walking is high, it may indicate a lack of exercise or stress, and appropriate exercise and relaxation methods can be suggested. Conversely, if the heart rate is low, it may indicate poor health, and a doctor's consultation can be suggested. Furthermore, if the heart rate is stable, it can be inferred that the user is in good health, and advice for maintaining good health can be provided. In this way, analyzing heart rate data allows for a more detailed assessment of health status.

[0064] The prediction unit can improve its prediction accuracy by referring to the user's past health checkup results. For example, it can predict the current health status based on past health checkup results. It can evaluate changes in health status by comparing past health checkup results with current data. It can predict future health risks by referring to past health checkup results. In this way, the prediction accuracy is improved by referring to the user's past health checkup results.

[0065] The recommendation department can make optimal recommendations by referring to the user's past behavioral history. For example, it can make recommendations for maintaining health based on past behavioral history. It can compare past behavioral history with current data and suggest areas for improvement. It can make easy-to-implement recommendations by referring to past behavioral history. In this way, it becomes possible to make optimal recommendations by referring to the user's past behavioral history.

[0066] The service provider can offer optimal services by considering the user's geographical location. For example, it can suggest nearby services based on the user's geographical location. It can offer appropriate services by comparing the user's geographical location with current data. It can offer services that can be used even while on the move by referring to the user's geographical location. In this way, by considering the user's geographical location, the service provider can offer optimal services.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The detection unit detects shoe prints and the wear pattern on the soles of shoes. For example, a pressure sensor can be used to detect the pressure distribution of shoe prints, and an acceleration sensor can be used to detect the wear pattern on the soles of shoes. Step 2: The analysis unit analyzes the data detected by the detection unit. For example, the data can be analyzed using AI, machine learning, or deep learning. Step 3: The prediction unit predicts health status and health lifestyle based on the analysis results obtained by the analysis unit. For example, health status and health lifestyle can be predicted using AI, machine learning, or deep learning. Step 4: The recommendation unit makes recommendations to the user based on the results predicted by the prediction unit. For example, if the user has poor walking balance, it can suggest how to choose appropriate shoes or improve their walking technique.

[0069] (Example of form 2) The system according to an embodiment of the present invention is a system that uses a sensor-equipped entrance sheet to detect shoe prints and the wear pattern on shoe soles, and quantifies walking and standing posture. The system then analyzes this data to estimate the user's health status and healthy lifestyle, and makes appropriate recommendations to the user. For example, if the user has poor walking balance, the system can suggest appropriate shoe selection and improvements to their walking method. Furthermore, when this idea is used in a store, the store can provide appropriate services based on the analyzed data. For example, a shoe store can suggest the optimal shoes based on the customer's walking data. First, a sensor-equipped entrance sheet detects shoe prints and the wear pattern on shoe soles, and quantifies walking and standing posture. Next, the system analyzes this data to estimate the user's health status and healthy lifestyle. Based on the estimation results, the system makes appropriate recommendations to the user. For example, if the user has poor walking balance, the system can suggest appropriate shoe selection and improvements to their walking method. Furthermore, when this idea is used in a store, the store can provide appropriate services based on the analyzed data. For example, a shoe store can suggest the optimal shoes based on the customer's walking data. This allows the system to detect shoe prints and the wear pattern on shoe soles, estimate the user's health status and lifestyle, and provide appropriate recommendations.

[0070] The system according to this embodiment comprises a detection unit, an analysis unit, an estimation unit, and a recommendation unit. The detection unit detects shoe prints and the wear pattern of shoe soles. The detection unit can, for example, use a pressure sensor or an acceleration sensor to detect shoe prints and the wear pattern of shoe soles. The detection unit can, for example, use a pressure sensor to detect the pressure distribution of shoe prints. The detection unit can also detect the wear pattern of shoe soles using an acceleration sensor. The detection unit can, for example, use a pressure sensor to detect the pressure distribution of shoe prints and an acceleration sensor to detect the wear pattern of shoe soles. The analysis unit analyzes the data detected by the detection unit. The analysis unit can, for example, use AI to analyze the data. The analysis unit can, for example, use machine learning to analyze the data. The analysis unit can also analyze the data using deep learning. The analysis unit can, for example, use machine learning to analyze the data and use deep learning to analyze the data. The estimation unit estimates health status and healthy lifestyle based on the analysis results obtained by the analysis unit. The prediction unit can, for example, use AI to predict health status and health lifestyle. The prediction unit can, for example, use machine learning to predict health status and health lifestyle. The prediction unit can also predict health status and health lifestyle using deep learning. The prediction unit can, for example, use machine learning to predict health status and health lifestyle, and use deep learning to predict health status and health lifestyle. The recommendation unit makes recommendations to the user based on the results predicted by the prediction unit. The recommendation unit can, for example, suggest how to choose appropriate shoes or how to improve walking methods if the user has poor walking balance. The recommendation unit can, for example, suggest how to choose appropriate shoes or how to improve walking methods if the user has poor walking balance. The recommendation unit can, for example, suggest how to choose appropriate shoes and how to improve walking methods if the user has poor walking balance. As a result, the system according to the embodiment can detect shoe prints and the wear pattern on the soles of shoes, predict health status and health lifestyle, and make recommendations to the user as appropriate.

[0071] The detection unit detects shoe prints and the wear pattern on shoe soles. Specifically, it can use pressure sensors and acceleration sensors to detect shoe prints and the wear pattern on shoe soles. Pressure sensors are used to detect the pressure distribution of shoe prints in detail. For example, multiple pressure sensors placed on the sole of the shoe measure the pressure distribution on the sole of the foot in real time during walking and collect the data. This allows for an accurate understanding of foot movement and weight distribution during walking. Acceleration sensors are used to detect the wear pattern on shoe soles. Acceleration sensors capture the movement of the sole of the shoe in three dimensions and measure acceleration and vibration during walking. This allows for a detailed analysis of the sole wear pattern and walking habits. Furthermore, the detection unit centrally manages the data obtained from these sensors and can collect and analyze the data in real time. For example, by combining data from pressure sensors and acceleration sensors, it is possible to comprehensively understand foot movement, weight distribution, and sole wear patterns during walking. This allows the detection unit to precisely detect shoe prints and the wear pattern on shoe soles, which can be used to assess walking balance and overall health.

[0072] The analysis unit analyzes the data detected by the detection unit. Specifically, it can use AI to analyze the data. The AI ​​uses machine learning and deep learning techniques to analyze the collected data in detail. For example, machine learning algorithms can be used to extract walking patterns from pressure distribution and acceleration data during walking, and to detect abnormal walking patterns and imbalances. Furthermore, deep learning enables more advanced data analysis. Deep learning uses multi-layered neural networks to analyze data and automatically learn complex patterns and features. This allows for highly accurate detection of subtle movements and changes in weight distribution during walking. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term changes and trends in walking patterns. For example, based on historical data, it can track changes in walking patterns over a specific period and detect early signs of changes in health status or risks. This allows the analysis unit to analyze the data obtained from the detection unit in detail and use it to evaluate walking balance and health status.

[0073] The prediction unit predicts health status and healthy lifestyle based on the analysis results obtained by the analysis unit. Specifically, it can use AI to predict health status and healthy lifestyle. The AI ​​uses machine learning and deep learning techniques to predict information about health status and healthy lifestyle from the analysis results. For example, using machine learning algorithms, it can evaluate the state of leg muscle strength and balance from data on walking patterns and pressure distribution, and predict health status. Furthermore, using deep learning enables more sophisticated predictions. Deep learning analyzes data using multi-layered neural networks and can automatically learn complex patterns and features. This allows for high-precision detection of subtle movements and changes in weight distribution during walking, and predicts information about health status and healthy lifestyle. In addition, the prediction unit can utilize past data and statistical information to detect long-term changes in health status and signs of risk early. For example, based on past data, it can track changes in walking patterns over a specific period and detect changes in health status and signs of risk early. As a result, the prediction unit can accurately predict information about health status and healthy lifestyle based on data obtained from the analysis unit, and provide information to take appropriate measures.

[0074] The recommendation unit makes recommendations to users based on the results inferred by the prediction unit. Specifically, if a user has poor walking balance, the recommendation unit can suggest appropriate shoe selection and improvements to their walking method. For example, if a user has poor walking balance, the recommendation unit can suggest appropriate shoe selection. For example, it can suggest shoe selection that suits the shape of the foot and walking pattern, thereby improving walking balance. The recommendation unit can also suggest improvements to walking method if a user has poor walking balance. For example, it can provide specific advice on improving posture and foot movement while walking, thereby improving walking balance. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, it can review and improve its recommendations based on feedback from users who have received recommendations, providing more effective recommendations. In addition, the recommendation unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the recommendation unit to provide users with recommendations quickly and reliably, supporting improvements in their health and healthy lifestyle.

[0075] The detection unit can detect shoe prints and the wear pattern on shoe soles using pressure sensors and acceleration sensors. For example, the detection unit can detect the pressure distribution of shoe prints using a pressure sensor. For example, the detection unit can detect the wear pattern on shoe soles using an acceleration sensor. For example, the detection unit can detect the pressure distribution of shoe prints using a pressure sensor and detect the wear pattern on shoe soles using an acceleration sensor. This allows for accurate detection of shoe prints and the wear pattern on shoe soles by using pressure sensors and acceleration sensors. Pressure sensors include, but are not limited to, piezo sensors and capacitive sensors. Acceleration sensors include, but are not limited to, MEMS sensors and piezoresistive sensors. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data acquired from pressure sensors and acceleration sensors into a generating AI, and the generating AI can analyze the data.

[0076] The analysis unit can analyze data using AI. The analysis unit can analyze data using, for example, machine learning. The analysis unit can analyze data using, for example, deep learning. The analysis unit can analyze data using, for example, machine learning and deep learning. This improves the accuracy of data analysis by using AI. AI includes, but is not limited to, machine learning and deep learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data acquired from the detection unit into a generation AI, and the generation AI can perform data analysis.

[0077] The recommendation unit can suggest appropriate shoe selection and improvements to walking methods if walking balance is poor. For example, the recommendation unit can suggest appropriate shoe selection if walking balance is poor. For example, the recommendation unit can suggest improvements to walking methods if walking balance is poor. For example, the recommendation unit can suggest appropriate shoe selection and improvements to walking methods if walking balance is poor. This allows for improvements in the user's health by suggesting appropriate shoe selection and improvements to walking methods when walking balance is poor. Walking balance includes, but is not limited to, the position of the center of gravity and left-right balance. Some or all of the processing described above in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input data obtained from the prediction unit into a generation AI, and the generation AI can generate recommendation content.

[0078] The service provision department can provide appropriate services based on the data analyzed by the store. For example, the service provision department can suggest the best shoes based on the customer's walking data in a shoe store. For example, the service provision department can suggest the best shoes based on the customer's walking data in a shoe store. For example, the service provision department can suggest the best shoes based on the customer's walking data in a shoe store. This allows the store to improve customer satisfaction by providing appropriate services based on the data analyzed by the store. Appropriate services include, but are not limited to, advice on choosing shoes and walking instruction. Some or all of the above processing in the service provision department may be performed using AI, for example, or without AI. For example, the service provision department can input data obtained from the analysis department into a generation AI, and the generation AI can generate service content.

[0079] The service provider can suggest the most suitable shoes based on the customer's walking data at a shoe store. The service provider can, for example, suggest the most suitable shoes based on the customer's walking data at a shoe store. The service provider can, for example, suggest the most suitable shoes based on the customer's walking data at a shoe store. This allows the service provider to provide the customer with the most suitable shoes by suggesting the most suitable shoes based on the customer's walking data at a shoe store. Optimal shoes include, but are not limited to, shoes that fit the shape of the foot or shoes suited to the intended use. Some or all of the above processing in the service provider can be performed using, for example, AI, or without AI. For example, the service provider can input customer walking data into a generating AI, and the generating AI can suggest the most suitable shoes.

[0080] The detection unit can estimate the user's emotions and adjust the detection accuracy of shoe prints and shoe sole wear based on the estimated emotions. For example, if the user is stressed, the detection unit can increase the detection accuracy to acquire more detailed data. For example, if the user is relaxed, the detection unit can set the detection accuracy to normal and acquire standard data. For example, if the user is in a hurry, the detection unit can lower the detection accuracy to acquire data quickly. This allows for the acquisition of more accurate data by adjusting the detection accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's emotion data into a generative AI, which can then perform emotion estimation.

[0081] The detection unit can improve detection accuracy by considering the user's physical information, such as weight and height, during detection. For example, the detection unit can adjust the sensitivity of the pressure sensor by considering the user's weight. For example, the detection unit can correct stride length data by considering the user's height. For example, the detection unit can correct the wear data of the soles of shoes based on the user's physical information. This improves detection accuracy by considering the user's physical information, such as weight and height. Physical information includes, but is not limited to, weight, height, and BMI. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's physical information into a generating AI, and the generating AI can correct the data.

[0082] The detection unit can measure the user's walking speed and stride length in real time upon detection and reflect this in the data. For example, the detection unit can measure the user's walking speed in real time and reflect this in the data. For example, the detection unit can measure the user's stride length in real time and reflect this in the data. For example, the detection unit can combine the user's walking speed and stride length to analyze the walking pattern in detail. This allows for the acquisition of more detailed data by measuring the user's walking speed and stride length in real time. Examples of walking speed include, but are not limited to, the placement of sensors and measurement frequency. Examples of stride length include, but are not limited to, the placement of sensors and measurement frequency. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data on the user's walking speed and stride length into a generating AI, which can then analyze the data.

[0083] The detection unit can estimate the user's emotions and determine the priority of data to detect based on the estimated emotions. For example, if the user is stressed, the detection unit can prioritize detecting data on the wear pattern of the soles of shoes. For example, if the user is relaxed, the detection unit can prioritize detecting data on shoe prints. For example, if the user is in a hurry, the detection unit can prioritize detecting data on walking speed. This allows for the priority acquisition of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, or not using AI. For example, the detection unit can input the user's emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0084] The detection unit can adjust its detection method when detecting footwear, taking into account the type and material of the user's shoes. For example, in the case of leather shoes, the detection unit can increase the sensitivity of the pressure sensor to acquire detailed data. For example, in the case of sneakers, the detection unit can adjust the sensitivity of the acceleration sensor to analyze the walking pattern in detail. For example, in the case of sandals, the detection unit can prioritize detecting footprint data. This allows the detection method to be appropriately adjusted by taking into account the type and material of the user's shoes. The types of shoes include, but are not limited to, running shoes and business shoes. The materials include, but are not limited to, leather and synthetic materials. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input data on the type and material of the user's shoes into a generating AI, and the generating AI can adjust the detection method.

[0085] The detection unit can improve detection accuracy by considering the user's walking environment (e.g., indoors or outdoors, type of ground) during detection. For example, indoors, the detection unit can increase the sensitivity of the pressure sensor to acquire more detailed data. For example outdoors, the detection unit can adjust the sensitivity of the acceleration sensor to analyze the walking pattern in detail. For example, if the ground is slippery, the detection unit can prioritize detecting walking speed data. This improves detection accuracy by considering the user's walking environment. The walking environment includes, but is not limited to, indoors or outdoors and type of ground. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data on the user's walking environment into a generating AI, which can then improve detection accuracy.

[0086] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a detailed data analysis to identify the cause of the stress. For example, if the user is relaxed, the analysis unit can perform a standard data analysis to assess their health status. For example, if the user is in a hurry, the analysis unit can perform a rapid data analysis to provide concise results. This allows for more appropriate analysis results by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's emotion data into a generative AI, and the generative AI can adjust the data analysis method.

[0087] The analysis unit can detect anomalies by comparing current data with past data during analysis and identify changes in health status. For example, the analysis unit can detect abnormalities in walking patterns by comparing them with past data. For example, the analysis unit can detect abnormalities in the wear pattern of shoe soles by comparing them with past data. For example, the analysis unit can detect abnormalities in standing posture by comparing them with past data. In this way, changes in health status can be identified by detecting anomalies by comparing them with past data. Anomalies include, but are not limited to, threshold settings and definitions of anomalies. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past data into a generating AI, and the generating AI can detect anomalies.

[0088] The analysis unit can interpret data while considering the user's lifestyle and exercise history. For example, the analysis unit can interpret walking pattern data while considering the user's exercise history. For example, the analysis unit can interpret shoe sole wear data while considering the user's lifestyle. For example, the analysis unit can interpret standing posture data by combining the user's exercise history and lifestyle. This makes the interpretation of data more accurate by considering the user's lifestyle and exercise history. Lifestyle includes, but is not limited to, dietary patterns and exercise habits. Exercise history includes, but is not limited to, the frequency and type of exercise. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data on the user's lifestyle and exercise history into a generating AI, and the generating AI can interpret the data.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI, and the generative AI can adjust the display method.

[0090] The analysis unit can interpret data while considering the user's age and gender during analysis. For example, the analysis unit can interpret walking pattern data while considering the user's age. For example, the analysis unit can interpret shoe sole wear data while considering the user's gender. For example, the analysis unit can interpret standing posture data by combining the user's age and gender. This makes the interpretation of data more accurate by considering the user's age and gender. Age includes, but is not limited to, age-specific criteria and age-appropriate adjustments. Gender includes, but is not limited to, gender-specific criteria and gender-appropriate adjustments. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's age and gender data into a generating AI, and the generating AI can interpret the data.

[0091] The analysis unit can evaluate the user's health status by considering their eating and sleeping patterns during analysis. For example, the analysis unit can interpret walking pattern data by considering the user's eating patterns. For example, the analysis unit can interpret shoe sole wear data by considering the user's sleeping patterns. For example, the analysis unit can interpret standing posture data by combining the user's eating and sleeping patterns. This makes the evaluation of health status more accurate by considering the user's eating and sleeping patterns. Eating includes, but is not limited to, the content and frequency of meals. Sleep patterns include, but are not limited to, the duration and quality of sleep. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's eating and sleeping pattern data into a generating AI, and the generating AI can evaluate the health status.

[0092] The inference unit can estimate the user's emotions and adjust the method of inferring health status and health life based on the estimated emotions. For example, if the user is stressed, the inference unit can estimate health status by taking the effects of stress into account. For example, if the user is relaxed, the inference unit can assess health status using a normal inference method. For example, if the user is in a hurry, the inference unit can assess health status using a rapid inference method. This allows for more accurate predictions of health status and health life by adjusting the inference method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the user's emotion data into a generative AI, which can then adjust the inference method.

[0093] The prediction unit can improve its prediction accuracy by referring to the user's past health check results during prediction. For example, the prediction unit can predict the current health status based on past health check results. For example, the prediction unit can evaluate changes in health status by comparing past health check results with current data. For example, the prediction unit can predict future health risks by referring to past health check results. This improves prediction accuracy by referring to the user's past health check results. Health check results include, but are not limited to, the types and methods of reference of past check results. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or without AI. For example, the prediction unit can input the user's past health check results into a generating AI, and the generating AI can improve the prediction accuracy.

[0094] The prediction unit can assess health risks by considering the user's family history and genetic information during prediction. For example, the prediction unit can assess genetic health risks by considering the user's family history. For example, the prediction unit can predict specific health risks based on the user's genetic information. For example, the prediction unit can assess overall health risks by combining the user's family history and genetic information. This makes the assessment of health risks more accurate by considering the user's family history and genetic information. Family history includes, but is not limited to, the health status and genetic factors of family members. Genetic information includes, but is not limited to, the results of genetic tests and genetic risks. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input the user's family history and genetic information into a generating AI, and the generating AI can perform the health risk assessment.

[0095] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit can provide a display method that gets straight to the point. By adjusting the display method based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method.

[0096] The estimation unit can assess the user's health status by considering their occupation and daily activity level during estimation. For example, the estimation unit can assess health risks by considering the user's occupation. For example, the estimation unit can estimate the health status based on the user's daily activity level. For example, the estimation unit can assess the overall health status by combining the user's occupation and activity level. This makes the assessment of health status more accurate by considering the user's occupation and daily activity level. Occupation includes, but is not limited to, occupation-specific standards and occupation-specific adjustments. Daily activity level includes, but is not limited to, activity volume and type of activity. Some or all of the above processing in the estimation unit may be performed using, for example, AI, or not using AI. For example, the estimation unit can input data on the user's occupation and daily activity level into a generating AI, and the generating AI can perform the health status assessment.

[0097] The estimation unit can estimate a user's health life by considering the user's stress level and psychological state during estimation. For example, the estimation unit can estimate a user's health status by considering the user's stress level. For example, the estimation unit can evaluate a user's health life based on the user's psychological state. For example, the estimation unit can estimate an overall health life by combining the user's stress level and psychological state. This makes the estimation of health life more accurate by considering the user's stress level and psychological state. Stress level includes, but is not limited to, examples of stress assessment methods and the effects of stress. Psychological state includes, but is not limited to, examples of psychological test results and psychological evaluations. Some or all of the above processing in the estimation unit may be performed using, for example, AI, or not using AI. For example, the estimation unit can input data on the user's stress level and psychological state into a generating AI, and the generating AI can perform the estimation of health life.

[0098] The recommendation unit can estimate the user's emotions and adjust its recommendations based on those emotions. For example, if the user is feeling stressed, the recommendation unit can suggest ways to relax. For example, if the user is relaxed, the recommendation unit can provide advice for maintaining good health. For example, if the user is in a hurry, the recommendation unit can provide concise and easy-to-implement recommendations. By adjusting the recommendations based on the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input the user's emotion data into a generative AI, and the generative AI can adjust the recommendations.

[0099] The recommendation unit can make optimal recommendations by referring to the user's past behavioral history when making recommendations. For example, the recommendation unit can make recommendations for maintaining health based on past behavioral history. For example, the recommendation unit can compare past behavioral history with current data and propose areas for improvement. For example, the recommendation unit can make easy-to-implement recommendations by referring to past behavioral history. This makes it possible to make optimal recommendations by referring to the user's past behavioral history. Behavioral history includes, but is not limited to, past behavioral data and behavioral patterns. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's past behavioral history into a generating AI, and the generating AI can make optimal recommendations.

[0100] The recommendation department can propose specific improvement measures when making recommendations, taking into account the user's living environment and lifestyle. For example, the recommendation department can propose appropriate exercise methods, taking into account the user's living environment. For example, the recommendation department can propose dietary improvements based on the user's lifestyle. For example, the recommendation department can propose comprehensive health improvement measures by combining the user's living environment and lifestyle. This allows for the proposal of specific improvement measures by taking into account the user's living environment and lifestyle. The living environment includes, but is not limited to, the living environment and lifestyle habits. The lifestyle includes, but is not limited to, daily activity patterns and hobbies. Some or all of the above processing in the recommendation department may be performed using, for example, AI, or not using AI. For example, the recommendation department can input data on the user's living environment and lifestyle into a generating AI, and the generating AI can propose specific improvement measures.

[0101] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated emotions. For example, if the user is feeling stressed, the recommendation unit can prioritize stress reduction recommendations. For example, if the user is relaxed, the recommendation unit can prioritize health maintenance recommendations. For example, if the user is in a hurry, the recommendation unit can prioritize recommendations that can be acted upon quickly. This allows important recommendations to be prioritized by determining the priority of recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's emotion data into a generative AI, and the generative AI can determine the priority of recommendations.

[0102] The recommendation unit can customize the content of its recommendations by taking into account the user's health goals and preferences. For example, the recommendation unit can propose a specific exercise plan considering the user's health goals. For example, the recommendation unit can propose dietary improvements based on the user's preferences. For example, the recommendation unit can propose comprehensive health improvement measures by combining the user's health goals and preferences. In this way, the content of the recommendations can be customized by taking into account the user's health goals and preferences. Health goals include, but are not limited to, methods for setting goals and methods for evaluating goal achievement. Preferences include, but are not limited to, the types of preferences and their priority. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input data on the user's health goals and preferences into a generating AI, and the generating AI can customize the content of the recommendations.

[0103] The recommendation department can adjust its recommendations when making them, taking into account the user's community and support network. For example, the recommendation department can propose group exercises, taking into account the user's community. For example, the recommendation department can make recommendations that can be implemented with support, based on the user's support network. For example, the recommendation department can propose comprehensive health improvement measures, combining the user's community and support network. This makes it possible to make recommendations that are easier to implement by taking into account the user's community and support network. The community includes, but is not limited to, groups to which the user belongs and support networks. The support network includes, but is not limited to, types of supporters and the content of support. Some or all of the above processing in the recommendation department may be performed using, for example, AI, or not using AI. For example, the recommendation department can input data on the user's community and support network into a generating AI, and the generating AI can adjust the recommendation content.

[0104] The service provider can estimate the user's emotions and adjust the services provided based on those emotions. For example, if the user is stressed, the service provider can provide a relaxing service. If the user is relaxed, the service provider can provide a health maintenance service. If the user is in a hurry, the service provider can provide a quickly available service. By adjusting the service content based on the user's emotions, more appropriate services can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotion data into a generative AI, and the generative AI can adjust the service content.

[0105] The service provider can provide the most suitable service by referring to the user's past service usage history when providing a service. For example, the service provider can propose the most suitable service based on the user's past service usage history. For example, the service provider can provide an appropriate service by comparing the user's past service usage history with current data. For example, the service provider can provide a service that suits the user by referring to the user's past service usage history. This allows the service provider to provide the most suitable service by referring to the user's past service usage history. Service usage history includes, but is not limited to, past usage data and usage patterns. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past service usage history into a generating AI, and the generating AI can provide the most suitable service.

[0106] The service provider can collect user feedback in real time during service provision and improve the service content. For example, the service provider can collect user feedback in real time and immediately improve the service content. For example, the service provider can improve the quality of the service based on user feedback. For example, the service provider can analyze user feedback and propose long-term service improvement measures. This allows for immediate improvement of the service content by collecting user feedback in real time. Feedback includes, but is not limited to, real-time collection and feedback evaluation methods. Some or all of the above processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user feedback into a generating AI, and the generating AI can improve the service content.

[0107] The service provider can estimate the user's emotions and adjust the timing of service delivery based on the estimated emotions. For example, if the user is stressed, the service provider can provide service at a time when the user can relax. For example, if the user is relaxed, the service provider can provide service at a time when the user can maintain their health. For example, if the user is in a hurry, the service provider can provide service at a time when it can be used quickly. In this way, by adjusting the timing of service delivery based on the user's emotions, services can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotion data into a generative AI, and the generative AI can adjust the timing of service delivery.

[0108] The service provider can provide the most suitable service by considering the user's geographical location information when providing the service. For example, the service provider can suggest nearby services based on the user's geographical location information. For example, the service provider can provide an appropriate service by comparing the user's geographical location information with current data. For example, the service provider can provide a service that can be used even while on the move by referring to the user's geographical location information. In this way, the service provider can provide the most suitable service by considering the user's geographical location information. Geographical location information includes, but is not limited to, the method of acquiring location information and the method of using location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can provide the most suitable service.

[0109] The service provider can analyze the user's social media activity and suggest relevant services when providing services. For example, the service provider can analyze the user's social media activity and suggest services based on their interests. For example, the service provider can compare the user's social media activity with current data to provide appropriate services. For example, the service provider can refer to the user's social media activity to provide services that are in line with current trends. In this way, relevant services can be suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of posted content and analysis of activity patterns. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's social media activity into a generating AI, and the generating AI can suggest relevant services.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The detection unit detects the sounds a user makes while walking and analyzes the sound patterns to infer their walking balance and health status. For example, if the walking sounds are irregular, it may indicate poor walking balance, allowing the system to suggest appropriate shoe selection and improvements to walking technique. If the walking sounds are louder than usual, it may indicate worn-out shoe soles, suggesting the purchase of new shoes. Furthermore, if the walking sounds are light and airy, it can infer good health and provide advice for maintaining that health. In this way, detecting walking sounds allows for more detailed inferences about a person's health status.

[0112] The analysis department can acquire vibration data from users while they walk and evaluate their health status by analyzing the vibration patterns. For example, if the vibrations during walking are strong, it may indicate strain on the feet or knees, allowing for suggestions on appropriate shoe selection and improvements to walking technique. Irregular vibrations may indicate poor balance during walking, allowing for suggestions on exercises to improve balance. Furthermore, if the vibrations are light, it can be inferred that the user is in good health, and advice for maintaining good health can be provided. In this way, analyzing vibration data enables a more detailed evaluation of health status.

[0113] The estimation unit can estimate the user's emotions and adjust the estimation method for health status and health life based on those estimated emotions. For example, if the user is stressed, the system can estimate their health status taking the effects of stress into account. If the user is relaxed, the system can assess their health status using the normal estimation method. If the user is in a hurry, the system can assess their health status using the rapid estimation method. By adjusting the estimation method based on the user's emotions, more accurate estimations of health status and health life become possible.

[0114] The recommendation function can estimate the user's emotions and adjust its recommendations based on those estimates. For example, if the user is stressed, it can suggest relaxation methods. If the user is relaxed, it can provide advice for maintaining good health. If the user is in a hurry, it can provide concise and easy-to-implement recommendations. By adjusting the recommendations based on the user's emotions, more appropriate recommendations can be made.

[0115] The service provider can estimate the user's emotions and adjust the services offered based on those estimates. For example, if the user is stressed, they can provide relaxing services. If the user is relaxed, they can provide services to maintain their health. If the user is in a hurry, they can provide services that can be used quickly. By adjusting the services based on the user's emotions, more appropriate services can be provided.

[0116] The detection unit can detect changes in the user's body temperature while walking and analyze the data to assess their health status. For example, if body temperature rises rapidly while walking, it may indicate poor health, and a rest can be suggested. If body temperature drops, it may indicate poor circulation, and appropriate cold weather protection measures can be suggested. Furthermore, if body temperature remains stable, it can be inferred that the user is in good health, and advice for maintaining good health can be provided. In this way, detecting changes in body temperature allows for a more detailed assessment of health status.

[0117] The analysis department can acquire heart rate data from users while they are walking and analyze that data to assess their health status. For example, if the heart rate during walking is high, it may indicate a lack of exercise or stress, and appropriate exercise and relaxation methods can be suggested. Conversely, if the heart rate is low, it may indicate poor health, and a doctor's consultation can be suggested. Furthermore, if the heart rate is stable, it can be inferred that the user is in good health, and advice for maintaining good health can be provided. In this way, analyzing heart rate data allows for a more detailed assessment of health status.

[0118] The prediction unit can improve its prediction accuracy by referring to the user's past health checkup results. For example, it can predict the current health status based on past health checkup results. It can evaluate changes in health status by comparing past health checkup results with current data. It can predict future health risks by referring to past health checkup results. In this way, the prediction accuracy is improved by referring to the user's past health checkup results.

[0119] The recommendation department can make optimal recommendations by referring to the user's past behavioral history. For example, it can make recommendations for maintaining health based on past behavioral history. It can compare past behavioral history with current data and suggest areas for improvement. It can make easy-to-implement recommendations by referring to past behavioral history. In this way, it becomes possible to make optimal recommendations by referring to the user's past behavioral history.

[0120] The service provider can offer optimal services by considering the user's geographical location. For example, it can suggest nearby services based on the user's geographical location. It can offer appropriate services by comparing the user's geographical location with current data. It can offer services that can be used even while on the move by referring to the user's geographical location. In this way, by considering the user's geographical location, the service provider can offer optimal services.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The detection unit detects shoe prints and the wear pattern on the soles of shoes. For example, a pressure sensor can be used to detect the pressure distribution of shoe prints, and an acceleration sensor can be used to detect the wear pattern on the soles of shoes. Step 2: The analysis unit analyzes the data detected by the detection unit. For example, the data can be analyzed using AI, machine learning, or deep learning. Step 3: The prediction unit predicts health status and health lifestyle based on the analysis results obtained by the analysis unit. For example, health status and health lifestyle can be predicted using AI, machine learning, or deep learning. Step 4: The recommendation unit makes recommendations to the user based on the results predicted by the prediction unit. For example, if the user has poor walking balance, it can suggest how to choose appropriate shoes or improve their walking technique.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the detection unit, analysis unit, prediction unit, recommendation unit, and service provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the pressure sensor and acceleration sensor of the smart device 14 to detect shoe prints and the wear pattern of shoe soles. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts health status and healthy lifestyle. The recommendation unit is implemented in the specific processing unit 46A of the smart device 14 and suggests to the user how to choose appropriate shoes and improve their walking method. The service provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and the store suggests the optimal shoes based on the customer's walking data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the detection unit, analysis unit, prediction unit, recommendation unit, and service provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the pressure sensor and acceleration sensor of the smart glasses 214 to detect shoe prints and the wear pattern of shoe soles. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the data using AI. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts health status and health lifestyle. The recommendation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and suggests to the user how to choose appropriate shoes and improve their walking method. The service provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and the store suggests the optimal shoes based on the customer's walking data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the detection unit, analysis unit, prediction unit, recommendation unit, and service provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the pressure sensor and acceleration sensor of the headset terminal 314 to detect shoe prints and the wear pattern of shoe soles. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts the user's health status and healthy lifestyle. The recommendation unit is implemented in the specific processing unit 46A of the headset terminal 314 and suggests to the user how to choose appropriate shoes and improve their walking method. The service provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and allows the store to suggest the optimal shoes based on the customer's walking data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the detection unit, analysis unit, prediction unit, recommendation unit, and service provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the detection unit uses the robot 414's pressure sensor and acceleration sensor to detect shoe prints and the wear pattern on the soles of shoes. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the data using AI. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts the user's health status and healthy lifestyle. The recommendation unit is implemented, for example, by the control unit 46A of the robot 414, and suggests to the user how to choose appropriate shoes and improve their walking method. The service provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the store suggests the optimal shoes based on the customer's walking data. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A detection unit that detects shoe prints and the wear pattern on the soles of shoes, An analysis unit analyzes the data detected by the detection unit, Based on the analysis results obtained by the aforementioned analysis unit, an estimation unit estimates the health status and health lifestyle, The system includes a suggestion unit that makes suggestions to the user based on the results inferred by the aforementioned inference unit. A system characterized by the following features. (Note 2) The detection unit, Pressure sensors and acceleration sensors are used to detect shoe prints and the wear pattern on the soles of shoes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Using AI to analyze data The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation section, If your walking balance is poor, we will suggest how to choose appropriate shoes and improve your walking technique. The system described in Appendix 1, characterized by the features described herein. (Note 5) The store has a service department that provides appropriate services based on data analyzed by the store. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned service provision unit, In shoe stores, we suggest the most suitable shoes based on the customer's walking data. The system described in Appendix 2, characterized by the features described herein. (Note 7) The detection unit, The system estimates the user's emotions and adjusts the detection accuracy of shoe prints and sole wear based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit, During detection, the detection accuracy is improved by taking into account the user's physical information such as weight and height. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit, Upon detection, the system measures the user's walking speed and stride length in real time and incorporates this data into the system. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit, It estimates the user's emotions and determines the priority of data to detect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit, When detecting footwear, the detection method is adjusted to take into account the type and material of the user's shoes. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit, When detection occurs, the detection accuracy is improved by taking into account the user's walking environment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the user's emotions and adjust the data analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, anomalies are detected by comparing them with past data, and changes in health status are identified. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, the data is interpreted while taking into account the user's lifestyle and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing the data, the age and gender of the users should be taken into consideration when interpreting the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, the user's diet and sleep patterns are taken into consideration to assess their health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned inference unit, The system estimates the user's emotions and adjusts the method of predicting health status and health life based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned inference unit, When making predictions, the accuracy of the predictions is improved by referring to the user's past health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inference unit, When making predictions, health risks are assessed by considering the user's family history and genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inference unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned inference unit, When making an estimate, the user's occupation and daily activity level are taken into consideration when assessing their health status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned inference unit, When making predictions, the user's stress level and psychological state are taken into consideration when estimating their health life. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation section, The system estimates the user's emotions and adjusts the recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation section, When making recommendations, we refer to the user's past behavioral history to provide the most appropriate recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation section, When making recommendations, we propose specific improvement measures that take into account the user's living environment and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation section, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation section, When making recommendations, customize the content of the recommendations to take into account the user's health goals and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recommendation section, When making recommendations, we adjust the content of the recommendations to take into account the user community and support network. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned service provision unit, The system estimates the user's emotions and adjusts the services provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned service provision unit, When providing a service, we refer to the user's past service usage history to provide the most suitable service. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned service provision unit, We collect user feedback in real time while providing the service and use it to improve the service content. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned service provision unit, The system estimates the user's emotions and adjusts the timing of service delivery based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned service provision unit, When providing services, we take into account the user's geographical location to provide the most suitable service. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned service provision unit, When providing services, we analyze the user's social media activity and suggest relevant services. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit that detects shoe prints and the wear pattern on the soles of shoes, An analysis unit analyzes the data detected by the detection unit, Based on the analysis results obtained by the aforementioned analysis unit, an estimation unit estimates the health status and health lifestyle, The system includes a suggestion unit that makes suggestions to the user based on the results inferred by the aforementioned inference unit. A system characterized by the following features.

2. The detection unit, Pressure sensors and acceleration sensors are used to detect shoe prints and the wear pattern on the soles of shoes. The system according to feature 1.

3. The aforementioned analysis unit is Using AI to analyze data The system according to feature 1.

4. The aforementioned recommendation section, If your walking balance is poor, we will suggest how to choose appropriate shoes and improve your walking technique. The system according to feature 1.

5. The store has a service department that provides appropriate services based on data analyzed by the store. The system according to feature 1.

6. The detection unit, The system estimates the user's emotions and adjusts the detection accuracy of shoe prints and sole wear based on those estimated emotions. The system according to feature 1.

7. The detection unit, During detection, the detection accuracy is improved by taking into account the user's physical information such as weight and height. The system according to feature 1.

8. The detection unit, Upon detection, the system measures the user's walking speed and stride length in real time and incorporates this data into the system. The system according to feature 1.

9. The detection unit, It estimates the user's emotions and determines the priority of data to detect based on those estimated emotions. The system according to feature 1.

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