system

Abstract

The system according to this embodiment aims to provide an optimized walking plan for each individual user and to provide appropriate feedback and advice based on their progress. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a progress collection unit, and a provision unit. The collection unit collects information such as the user's physical fitness level, daily activity level, and target healthy lifespan. The generation unit generates an individualized walking plan based on the information collected by the collection unit. The progress collection unit collects the progress of the user walking based on the walking plan generated by the generation unit. The provision unit provides feedback and advice based on the progress collected by the progress collection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a walking plan optimized for each individual user is not sufficiently provided, and appropriate feedback and advice are not given based on the progress status.

[0005] The system according to the embodiment aims to provide a walking plan optimized for each individual user and give appropriate feedback and advice based on the progress status.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a progress collection unit, and a provision unit. The collection unit collects information such as the user's physical fitness level, daily activity level, and target healthy lifespan. The generation unit generates an individualized walking plan based on the information collected by the collection unit. The progress collection unit collects the progress of the user walking based on the walking plan generated by the generation unit. The provision unit provides feedback and advice based on the progress collected by the progress collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimized walking plan for each individual user and offer appropriate feedback and advice based on their progress. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The walking support system according to an embodiment of the present invention is a mechanism that contributes to creating healthy, active seniors by enabling them to walk efficiently, in today's society where labor shortages are a major social issue. This walking support system utilizes generative AI to provide a way to further expand the possibilities of walking. For example, it personalizes walking plans. Users provide the generative AI with information such as their physical fitness level, daily activity level, and target healthy lifespan. The generative AI analyzes this information and provides a customized walking plan that is optimal for each individual user. For example, it recommends short walks for users with low physical fitness and long walks for users with high physical fitness. Next, it provides motivation and feedback. When a user reports their walking progress and achievements to the generative AI, the generative AI maintains motivation by praising the user and confirming their progress toward their goals. It also maximizes the effects of walking by providing appropriate feedback and advice. For example, when a user achieves a goal, it encourages them to set the next goal. Furthermore, it provides health information and know-how. When a user asks the generative AI questions, they can receive advice on health-related information and theories, effective walking techniques, effective stretches, and more. This allows users to deepen their knowledge and understanding of how walking can extend their healthy lifespan. This system provides personalized guidance and motivation for extending healthy lifespan through walking. Users can receive plans and advice tailored to their needs, supporting a healthy lifestyle. Furthermore, an increase in healthy seniors is expected to bring social benefits such as reduced medical and nursing care costs, securing a workforce, improved social vitality, contributions to local communities, and promotion of intergenerational exchange. Thus, walking support systems can contribute to creating healthy and active seniors.

[0029] The walking support system according to the embodiment comprises a collection unit, a generation unit, a progress collection unit, and a provision unit. The collection unit collects information such as the user's physical fitness level, daily activity level, and target healthy life expectancy. The collection unit can collect data to evaluate, for example, the user's cardiopulmonary function, muscle strength, and endurance. The collection unit can also use devices such as pedometers and calorie counters to measure the user's daily activity level. Furthermore, the collection unit can conduct questionnaires and interviews to collect information regarding the user's target healthy life expectancy. The generation unit generates an individualized walking plan based on the information collected by the collection unit. The generation unit can, for example, use a generation AI to generate a customized walking plan based on the user's physical fitness level and activity level. The generation unit inputs data regarding the user's physical fitness level and activity level to the generation AI, which outputs an optimal walking plan. The generation unit can also use the generation AI to generate a walking plan based on the user's target healthy life expectancy. The progress collection unit collects the progress of the user walking based on the walking plan generated by the generation unit. The progress collection unit can, for example, use GPS devices or smartphone apps to measure the user's walking achievement and progress. The progress collection unit can periodically collect data to evaluate the user's walking goal achievement rate. The provision unit provides feedback and advice based on the progress collected by the progress collection unit. The provision unit can, for example, provide feedback on areas for improvement in exercise and setting the next goal, depending on the user's walking progress. The provision unit can use generative AI to provide appropriate advice based on the user's walking progress. As a result, the walking support system according to the embodiment can contribute to creating healthy, active seniors by providing an individualized walking plan based on the user's physical fitness level, daily activity level, and target healthy lifespan, and by providing feedback and advice based on progress.

[0030] The data collection unit gathers information such as the user's fitness level, daily activity level, and target healthy lifespan. Specifically, the unit can collect data to evaluate the user's cardiopulmonary function, muscle strength, and endurance. This includes biometric measurement devices such as heart rate monitors and oxygen saturation sensors. These devices collect data in real time during exercise and daily life and transmit it to a cloud server. Furthermore, the unit can use devices such as pedometers and calorie counters to measure the user's daily activity level. These devices record the user's steps, distance traveled, calories burned, etc., allowing for a detailed understanding of daily activity levels. In addition, the unit can conduct questionnaires and interviews to collect information related to the user's target healthy lifespan. Questionnaires include questions to gain a detailed understanding of the user's health status, lifestyle, and exercise goals. Interviews allow professional trainers and medical personnel to directly interact with users and gather deeper information. As a result, the unit can collect multifaceted data and provide foundational information for personalized walking plans tailored to the user's health status and goals.

[0031] The generation unit generates personalized walking plans based on information collected by the collection unit. Specifically, the generation unit can use a generation AI to generate customized walking plans based on the user's fitness level and activity level. The generation AI is input with data such as the user's cardiopulmonary function, muscle strength, endurance, daily activity level, and healthy life expectancy goals. The generation AI analyzes this data and outputs the optimal walking plan for the user. For example, the generation AI calculates appropriate exercise intensity and duration based on the user's heart rate and oxygen saturation data. It can also adjust the frequency and distance of walking, taking into account the user's daily activity level. Furthermore, the generation AI can also generate walking plans based on the user's target healthy life expectancy. For example, if the user has set a goal to maintain their health in the long term, the generation AI will propose a step-by-step plan towards that goal. This includes plans to gradually increase walking intensity and distance, and specific action plans to achieve certain health indicators. The generation unit provides the generated walking plan to the user and supports the user in walking according to the plan. This allows the generation unit to provide an optimal walking plan tailored to the user's health condition and goals, supporting the user in maintaining and improving their health.

[0032] The progress collection unit collects the progress of users who are walking based on the walking plan generated by the generation unit. Specifically, the progress collection unit can use GPS devices and smartphone apps to measure the achievement and progress of users' walking. GPS devices track the user's location in real time and record the walking distance and route. Smartphone apps record the user's steps, calories burned, exercise time, etc., allowing for a detailed understanding of daily progress. The progress collection unit sends the data collected from these devices to a cloud server for centralized management. Furthermore, the progress collection unit can periodically collect data to evaluate the user's walking goal achievement rate. For example, it can evaluate the user's progress on a weekly or monthly basis and check the degree of achievement against the goal. This allows the progress collection unit to understand the user's walking progress in real time and adjust the plan or provide feedback as needed. The progress collection unit can also provide rewards and encouraging messages according to the level of achievement to maintain user motivation. This allows the progress collection unit to understand the user's walking progress in detail and support the user in achieving their goals.

[0033] The service provider provides feedback and advice based on the progress collected by the progress collection unit. Specifically, the service provider can provide feedback on areas for improvement in exercise and setting the next goal, depending on the user's walking progress. For example, if the user achieves their set goal, the service provider will suggest a new goal as the next step to maintain the user's motivation. If the user has not reached their goal, the service provider will point out areas for improvement and provide specific advice. The service provider can use generative AI to provide appropriate advice based on the user's walking progress. The generative AI analyzes the user's progress data and generates optimal feedback and advice. For example, the generative AI can analyze the user's exercise patterns and health indicators and suggest adjustments to exercise intensity or new exercise menus. Furthermore, the service provider can collect user feedback and use it to improve the overall system. For example, it can collect user comments and opinions on the feedback and advice provided and use them as data to improve the accuracy and effectiveness of the system. This allows the service provider to provide users with appropriate feedback and advice, supporting their health maintenance and improvement.

[0034] The data collection unit can analyze the user's past health data and select the optimal information collection method. For example, the data collection unit can suggest an appropriate walking plan based on the user's past exercise history. The data collection unit can collect information according to the user's health condition by referring to the user's past health checkup results. The data collection unit can analyze the user's past meal records and collect information considering nutritional balance. In this way, the optimal information collection method can be selected by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal information collection method.

[0035] The data collection unit can filter information based on the user's current lifestyle and health status during data collection. For example, if a user reports their current health status, the data collection unit can suggest an appropriate walking plan based on that information. The data collection unit can consider the user's current lifestyle and set the optimal timing for data collection. The data collection unit can suggest an appropriate exercise intensity based on the user's current activity level. This allows for the collection of more relevant information by filtering information based on the user's current lifestyle and health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's current lifestyle and health status into a generating AI and have the generating AI perform the information filtering.

[0036] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can suggest an appropriate walking plan based on weather information for the user's current location. The data collection unit can prioritize the collection of health information for the user's residential area. The data collection unit can collect information on exercise facilities near the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0037] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can suggest an appropriate walking plan based on health information shared by the user on social media. The data collection unit can analyze the content of the user's social media posts and collect information of interest. The data collection unit can collect information on health-related accounts that the user follows. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0038] The generation unit can adjust the level of detail of a walking plan based on the user's health goals. For example, if the user's goal is weight loss, the generation unit can generate a plan that details calorie consumption. If the user's goal is muscle strength improvement, the generation unit can generate a plan that includes strength training. If the user's goal is stress relief, the generation unit can generate a plan that includes relaxing routes. By adjusting the level of detail of the plan based on the user's health goals, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health goal data into a generation AI and have the generation AI perform the adjustment of the plan's level of detail.

[0039] The generation unit can apply different plan generation algorithms depending on the user's activity history when generating a walking plan. For example, the generation unit can generate an optimal plan based on the user's past walking history. The generation unit can analyze the user's past exercise history and set an appropriate exercise intensity. The generation unit can generate a plan that suits the user's health condition by referring to the user's past health data. By applying different plan generation algorithms depending on the user's activity history, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's activity history data into a generation AI and have the generation AI execute the application of the plan generation algorithm.

[0040] The generation unit can determine the priority of walking plans based on the user's submission timing when generating walking plans. For example, if a user submits a plan early in the morning, the generation unit can prioritize generating morning walking plans. If a user submits a plan at night, the generation unit can prioritize generating evening walking plans. If a user submits a plan on a weekend, the generation unit can prioritize generating weekend walking plans. This allows for the provision of more appropriate plans by prioritizing plans based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI determine the plan prioritization.

[0041] The generation unit can adjust the order of walking plans based on user relevance when generating them. For example, the generation unit can generate an optimal plan based on the order of walks the user has taken in the past. The generation unit can analyze the user's past exercise history and generate plans in an appropriate order. The generation unit can refer to the user's past health data and generate plans in an order appropriate to their health condition. By adjusting the order of plans based on user relevance, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0042] The progress collection unit can improve the accuracy of data collection by considering the interrelationships between walking activities. For example, if a user is executing multiple walking plans, the progress collection unit can collect the progress of each plan while relating them to each other. If a user is walking at different times of the day, the progress collection unit can collect the progress of each plan in a unified manner. If a user is walking in different locations, the progress collection unit can collect the progress of each location in a unified manner. This improves the accuracy of data collection by considering the interrelationships between walking activities. Some or all of the above-described processes in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input the user's walking data into a generating AI and have the generating AI perform an analysis of the interrelationships.

[0043] The progress collection unit can collect progress information while considering the user's attribute information. For example, the progress collection unit can collect progress information according to the user's age and gender. The progress collection unit can collect progress information according to the user's health status. The progress collection unit can collect progress information according to the user's exercise experience. By considering the user's attribute information, more appropriate progress information can be collected. Some or all of the above processing in the progress collection unit may be performed using AI, for example, or without using AI. For example, the progress collection unit can input the user's attribute information into a generating AI and have the generating AI perform the collection of progress information.

[0044] The progress collection unit can collect progress data while considering the geographical distribution of walking. For example, if a user is walking in different areas, the progress collection unit can collect progress information for each area. If a user is walking in a specific area, the progress collection unit can collect detailed progress information for that area. If a user is walking in multiple areas, the progress collection unit can collect integrated progress information for each area. This allows for the collection of more appropriate progress information by considering the geographical distribution of walking. Some or all of the above processing in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the collection of progress information.

[0045] The progress collection unit can improve the accuracy of its data collection by referring to relevant literature on walking during the data collection process. For example, the progress collection unit can update its progress collection criteria by referring to the latest walking research. The progress collection unit can improve its progress collection methods based on literature on walking. The progress collection unit can improve the accuracy of its progress collection by referring to literature on the effects of walking. Thus, by referring to relevant literature on walking, the accuracy of the data collection can be improved. Some or all of the above-described processes in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input walking-related literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the data collection.

[0046] The service provider can provide optimal advice by referring to past data when providing feedback and advice. For example, the service provider can provide appropriate feedback based on the user's past walking history. The service provider can refer to the user's past health data and provide advice according to their health condition. The service provider can analyze the user's past feedback history and provide optimal advice. This allows for the provision of more appropriate advice by referring to past data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0047] The service provider can apply different advice methods to each user category when providing feedback and advice. For example, the service provider can provide advice tailored to the user's age, gender, or exercise experience. By applying different advice methods to each user category, more appropriate advice can be provided. 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 user category data into a generating AI and have the generating AI apply the advice method.

[0048] The service provider can analyze how advice changes based on the user's submission timing when providing feedback and advice. For example, if a user submits early in the morning, the service provider can provide advice about morning walks. If a user submits at night, the service provider can provide advice about evening walks. If a user submits on the weekend, the service provider can provide advice about weekend walks. By analyzing how advice changes based on the user's submission timing, the service provider can provide more appropriate advice. 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 user submission timing data into a generating AI and have the generating AI perform an analysis of how advice changes.

[0049] The service provider can analyze advice by referring to the user's relevant market data when providing feedback and advice. For example, the service provider can provide appropriate advice based on health market data in the user's area. The service provider can provide optimal advice by referring to market data corresponding to the user's age group. The service provider can provide appropriate advice by referring to market data corresponding to the user's gender. This allows for the provision of more appropriate advice by referring to the user's relevant market data. 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 relevant market data into a generating AI and have the generating AI perform the analysis of the advice.

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

[0051] The walking support system can also collect user meal data and incorporate it into the walking plan. The data collection unit provides an interface for users to input their daily meal contents, and the generation unit can generate a walking plan that takes calorie consumption into account based on that data. The progress collection unit integrates the user's meal data and walking data, and the provision unit can provide the results as feedback. This allows users to maintain their health while balancing diet and exercise. Furthermore, the provision unit can also provide dietary advice and recipes. This enables users to lead a healthier lifestyle.

[0052] The walking support system can also collect user sleep data and incorporate it into the walking plan. The collection unit collects sleep data from the user's smartwatch or fitness tracker, and the generation unit can generate an optimal walking plan based on that data. The progress collection unit integrates the user's sleep data and walking data, and the provision unit can provide the results as feedback. This allows the user to continue walking while improving the quality of their sleep. Furthermore, the provision unit can also provide advice on sleep and relaxation methods. This helps the user get better sleep.

[0053] The walking support system can further optimize walking plans by utilizing the user's health checkup data. The data collection unit collects the user's past health checkup results, and the generation unit generates a walking plan tailored to the user's health condition based on that data. The progress collection unit integrates the user's health checkup data and walking data, and the provision unit provides the results as feedback. This allows the user to execute a walking plan best suited to their health condition. Furthermore, the provision unit can also provide health management advice based on the health checkup results, enabling the user to maintain their health more effectively.

[0054] The walking support system can further customize walking plans to take into account the user's work environment. The data collection unit collects information about the user's work environment, and the generation unit generates a workplace walking plan based on that data. The progress collection unit collects the user's walking data at work, and the provision unit provides the results as feedback. This allows users to continue walking at work and maintain their health. Furthermore, the provision unit can also provide advice on workplace health management, enabling users to manage their health in a way that is appropriate for their work environment.

[0055] The walking support system can further customize walking plans to match the user's travel plans. The data collection unit gathers information about the user's travel plans, and the generation unit generates walking plans for the travel destination based on this data. The progress collection unit collects walking data during the user's trip, and the provision unit provides the results as feedback. This allows users to maintain their health even while traveling. Furthermore, the provision unit can suggest walking routes that include tourist spots and landmarks at the travel destination. This allows users to continue walking while enjoying their trip.

[0056] The walking support system can further customize walking plans by taking into account the user's seasonal health data. The data collection unit collects the user's seasonal health data, and the generation unit generates seasonal walking plans based on that data. The progress collection unit collects the user's seasonal walking data, and the provision unit provides the results as feedback. This allows the user to manage their health according to the season. Furthermore, the provision unit can also provide advice on seasonal health management, enabling the user to manage their health according to the season.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit gathers information such as the user's fitness level, daily activity level, and target healthy life expectancy. For example, the data collection unit collects data to evaluate the user's cardiopulmonary function, muscle strength, and endurance, and measures daily activity levels using devices such as pedometers and calorie counters. It also collects information about the user's target healthy life expectancy through questionnaires and interviews. Step 2: The generation unit generates personalized walking plans based on the information collected by the collection unit. For example, it uses a generation AI to generate customized walking plans based on the user's fitness level and activity level, and also generates walking plans based on the user's target healthy lifespan. Step 3: The progress collection unit collects the progress of users walking based on the walking plan generated by the generation unit. For example, it uses GPS devices or smartphone apps to measure the user's walking achievement and progress, and collects data periodically. Step 4: The service provider provides feedback and advice based on the progress collected by the progress collection unit. For example, it provides feedback on areas for improvement in exercise and setting the next goal based on the user's walking progress, and uses generative AI to provide appropriate advice.

[0059] (Example of form 2) The walking support system according to an embodiment of the present invention is a mechanism that contributes to creating healthy, active seniors by enabling them to walk efficiently, in today's society where labor shortages are a major social issue. This walking support system utilizes generative AI to provide a way to further expand the possibilities of walking. For example, it personalizes walking plans. Users provide the generative AI with information such as their physical fitness level, daily activity level, and target healthy lifespan. The generative AI analyzes this information and provides a customized walking plan that is optimal for each individual user. For example, it recommends short walks for users with low physical fitness and long walks for users with high physical fitness. Next, it provides motivation and feedback. When a user reports their walking progress and achievements to the generative AI, the generative AI maintains motivation by praising the user and confirming their progress toward their goals. It also maximizes the effects of walking by providing appropriate feedback and advice. For example, when a user achieves a goal, it encourages them to set the next goal. Furthermore, it provides health information and know-how. When a user asks the generative AI questions, they can receive advice on health-related information and theories, effective walking techniques, effective stretches, and more. This allows users to deepen their knowledge and understanding of how walking can extend their healthy lifespan. This system provides personalized guidance and motivation for extending healthy lifespan through walking. Users can receive plans and advice tailored to their needs, supporting a healthy lifestyle. Furthermore, an increase in healthy seniors is expected to bring social benefits such as reduced medical and nursing care costs, securing a workforce, improved social vitality, contributions to local communities, and promotion of intergenerational exchange. Thus, walking support systems can contribute to creating healthy and active seniors.

[0060] The walking support system according to the embodiment comprises a collection unit, a generation unit, a progress collection unit, and a provision unit. The collection unit collects information such as the user's physical fitness level, daily activity level, and target healthy life expectancy. The collection unit can collect data to evaluate, for example, the user's cardiopulmonary function, muscle strength, and endurance. The collection unit can also use devices such as pedometers and calorie counters to measure the user's daily activity level. Furthermore, the collection unit can conduct questionnaires and interviews to collect information regarding the user's target healthy life expectancy. The generation unit generates an individualized walking plan based on the information collected by the collection unit. The generation unit can, for example, use a generation AI to generate a customized walking plan based on the user's physical fitness level and activity level. The generation unit inputs data regarding the user's physical fitness level and activity level to the generation AI, which outputs an optimal walking plan. The generation unit can also use the generation AI to generate a walking plan based on the user's target healthy life expectancy. The progress collection unit collects the progress of the user walking based on the walking plan generated by the generation unit. The progress collection unit can, for example, use GPS devices or smartphone apps to measure the user's walking achievement and progress. The progress collection unit can periodically collect data to evaluate the user's walking goal achievement rate. The provision unit provides feedback and advice based on the progress collected by the progress collection unit. The provision unit can, for example, provide feedback on areas for improvement in exercise and setting the next goal, depending on the user's walking progress. The provision unit can use generative AI to provide appropriate advice based on the user's walking progress. As a result, the walking support system according to the embodiment can contribute to creating healthy, active seniors by providing an individualized walking plan based on the user's physical fitness level, daily activity level, and target healthy lifespan, and by providing feedback and advice based on progress.

[0061] The data collection unit gathers information such as the user's fitness level, daily activity level, and target healthy lifespan. Specifically, the unit can collect data to evaluate the user's cardiopulmonary function, muscle strength, and endurance. This includes biometric measurement devices such as heart rate monitors and oxygen saturation sensors. These devices collect data in real time during exercise and daily life and transmit it to a cloud server. Furthermore, the unit can use devices such as pedometers and calorie counters to measure the user's daily activity level. These devices record the user's steps, distance traveled, calories burned, etc., allowing for a detailed understanding of daily activity levels. In addition, the unit can conduct questionnaires and interviews to collect information related to the user's target healthy lifespan. Questionnaires include questions to gain a detailed understanding of the user's health status, lifestyle, and exercise goals. Interviews allow professional trainers and medical personnel to directly interact with users and gather deeper information. As a result, the unit can collect multifaceted data and provide foundational information for personalized walking plans tailored to the user's health status and goals.

[0062] The generation unit generates personalized walking plans based on information collected by the collection unit. Specifically, the generation unit can use a generation AI to generate customized walking plans based on the user's fitness level and activity level. The generation AI is input with data such as the user's cardiopulmonary function, muscle strength, endurance, daily activity level, and healthy life expectancy goals. The generation AI analyzes this data and outputs the optimal walking plan for the user. For example, the generation AI calculates appropriate exercise intensity and duration based on the user's heart rate and oxygen saturation data. It can also adjust the frequency and distance of walking, taking into account the user's daily activity level. Furthermore, the generation AI can also generate walking plans based on the user's target healthy life expectancy. For example, if the user has set a goal to maintain their health in the long term, the generation AI will propose a step-by-step plan towards that goal. This includes plans to gradually increase walking intensity and distance, and specific action plans to achieve certain health indicators. The generation unit provides the generated walking plan to the user and supports the user in walking according to the plan. This allows the generation unit to provide an optimal walking plan tailored to the user's health condition and goals, supporting the user in maintaining and improving their health.

[0063] The progress collection unit collects the progress of users who are walking based on the walking plan generated by the generation unit. Specifically, the progress collection unit can use GPS devices and smartphone apps to measure the achievement and progress of users' walking. GPS devices track the user's location in real time and record the walking distance and route. Smartphone apps record the user's steps, calories burned, exercise time, etc., allowing for a detailed understanding of daily progress. The progress collection unit sends the data collected from these devices to a cloud server for centralized management. Furthermore, the progress collection unit can periodically collect data to evaluate the user's walking goal achievement rate. For example, it can evaluate the user's progress on a weekly or monthly basis and check the degree of achievement against the goal. This allows the progress collection unit to understand the user's walking progress in real time and adjust the plan or provide feedback as needed. The progress collection unit can also provide rewards and encouraging messages according to the level of achievement to maintain user motivation. This allows the progress collection unit to understand the user's walking progress in detail and support the user in achieving their goals.

[0064] The service provider provides feedback and advice based on the progress collected by the progress collection unit. Specifically, the service provider can provide feedback on areas for improvement in exercise and setting the next goal, depending on the user's walking progress. For example, if the user achieves their set goal, the service provider will suggest a new goal as the next step to maintain the user's motivation. If the user has not reached their goal, the service provider will point out areas for improvement and provide specific advice. The service provider can use generative AI to provide appropriate advice based on the user's walking progress. The generative AI analyzes the user's progress data and generates optimal feedback and advice. For example, the generative AI can analyze the user's exercise patterns and health indicators and suggest adjustments to exercise intensity or new exercise menus. Furthermore, the service provider can collect user feedback and use it to improve the overall system. For example, it can collect user comments and opinions on the feedback and advice provided and use them as data to improve the accuracy and effectiveness of the system. This allows the service provider to provide users with appropriate feedback and advice, supporting their health maintenance and improvement.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect information during relaxed times. If the user is relaxed, the data collection unit can actively collect information. If the user is busy, the data collection unit can collect the necessary information in a short amount of time. By adjusting the timing of information collection according to the user's emotions, information can be collected 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0066] The data collection unit can analyze the user's past health data and select the optimal information collection method. For example, the data collection unit can suggest an appropriate walking plan based on the user's past exercise history. The data collection unit can collect information according to the user's health condition by referring to the user's past health checkup results. The data collection unit can analyze the user's past meal records and collect information considering nutritional balance. In this way, the optimal information collection method can be selected by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal information collection method.

[0067] The data collection unit can filter information based on the user's current lifestyle and health status during data collection. For example, if a user reports their current health status, the data collection unit can suggest an appropriate walking plan based on that information. The data collection unit can consider the user's current lifestyle and set the optimal timing for data collection. The data collection unit can suggest an appropriate exercise intensity based on the user's current activity level. This allows for the collection of more relevant information by filtering information based on the user's current lifestyle and health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's current lifestyle and health status into a generating AI and have the generating AI perform the information filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information that has a relaxing effect. If the user is relaxed, the data collection unit can prioritize collecting detailed information about health. If the user is busy, the data collection unit can prioritize collecting information that can be obtained in a short time. By prioritizing information based on the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can suggest an appropriate walking plan based on weather information for the user's current location. The data collection unit can prioritize the collection of health information for the user's residential area. The data collection unit can collect information on exercise facilities near the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0070] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can suggest an appropriate walking plan based on health information shared by the user on social media. The data collection unit can analyze the content of the user's social media posts and collect information of interest. The data collection unit can collect information on health-related accounts that the user follows. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0071] The generation unit can estimate the user's emotions and adjust the way the walking plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a walking plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a walking plan that emphasizes the shortest route. If the user is excited, the generation unit can generate a walking plan with visually stimulating effects. This allows for the provision of a more appropriate plan by adjusting the way the walking plan is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] The generation unit can adjust the level of detail of a walking plan based on the user's health goals. For example, if the user's goal is weight loss, the generation unit can generate a plan that details calorie consumption. If the user's goal is muscle strength improvement, the generation unit can generate a plan that includes strength training. If the user's goal is stress relief, the generation unit can generate a plan that includes relaxing routes. By adjusting the level of detail of the plan based on the user's health goals, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health goal data into a generation AI and have the generation AI perform the adjustment of the plan's level of detail.

[0073] The generation unit can apply different plan generation algorithms depending on the user's activity history when generating a walking plan. For example, the generation unit can generate an optimal plan based on the user's past walking history. The generation unit can analyze the user's past exercise history and set an appropriate exercise intensity. The generation unit can generate a plan that suits the user's health condition by referring to the user's past health data. By applying different plan generation algorithms depending on the user's activity history, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's activity history data into a generation AI and have the generation AI execute the application of the plan generation algorithm.

[0074] The generation unit can estimate the user's emotions and adjust the length of the walking plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a longer walking plan. If the user is in a hurry, the generation unit can generate a short but effective walking plan. If the user is excited, the generation unit can generate a walking plan of moderate length. By adjusting the length of the walking plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The generation unit can determine the priority of walking plans based on the user's submission timing when generating walking plans. For example, if a user submits a plan early in the morning, the generation unit can prioritize generating morning walking plans. If a user submits a plan at night, the generation unit can prioritize generating evening walking plans. If a user submits a plan on a weekend, the generation unit can prioritize generating weekend walking plans. This allows for the provision of more appropriate plans by prioritizing plans based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI determine the plan prioritization.

[0076] The generation unit can adjust the order of walking plans based on user relevance when generating them. For example, the generation unit can generate an optimal plan based on the order of walks the user has taken in the past. The generation unit can analyze the user's past exercise history and generate plans in an appropriate order. The generation unit can refer to the user's past health data and generate plans in an order appropriate to their health condition. By adjusting the order of plans based on user relevance, a more appropriate plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0077] The progress collection unit can estimate the user's emotions and adjust the progress collection criteria based on the estimated user emotions. For example, if the user is relaxed, the progress collection unit can collect detailed progress information. If the user is in a hurry, the progress collection unit can collect concise progress information. If the user is excited, the progress collection unit can collect visually stimulating progress information. By adjusting the progress collection criteria based on the user's emotions, more appropriate progress information can be collected. 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 progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The progress collection unit can improve the accuracy of data collection by considering the interrelationships between walking activities. For example, if a user is executing multiple walking plans, the progress collection unit can collect the progress of each plan while relating them to each other. If a user is walking at different times of the day, the progress collection unit can collect the progress of each plan in a unified manner. If a user is walking in different locations, the progress collection unit can collect the progress of each location in a unified manner. This improves the accuracy of data collection by considering the interrelationships between walking activities. Some or all of the above-described processes in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input the user's walking data into a generating AI and have the generating AI perform an analysis of the interrelationships.

[0079] The progress collection unit can collect progress information while considering the user's attribute information. For example, the progress collection unit can collect progress information according to the user's age and gender. The progress collection unit can collect progress information according to the user's health status. The progress collection unit can collect progress information according to the user's exercise experience. By considering the user's attribute information, more appropriate progress information can be collected. Some or all of the above processing in the progress collection unit may be performed using AI, for example, or without using AI. For example, the progress collection unit can input the user's attribute information into a generating AI and have the generating AI perform the collection of progress information.

[0080] The progress collection unit can estimate the user's emotions and adjust the order in which the progress collection results are displayed based on the estimated user emotions. For example, if the user is relaxed, the progress collection unit can prioritize displaying detailed progress information. If the user is in a hurry, the progress collection unit can prioritize displaying concise progress information. If the user is excited, the progress collection unit can prioritize displaying visually stimulating progress information. This allows for the provision of more appropriate information by adjusting the order in which the progress collection results are displayed 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-described processing in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The progress collection unit can collect progress data while considering the geographical distribution of walking. For example, if a user is walking in different areas, the progress collection unit can collect progress information for each area. If a user is walking in a specific area, the progress collection unit can collect detailed progress information for that area. If a user is walking in multiple areas, the progress collection unit can collect integrated progress information for each area. This allows for the collection of more appropriate progress information by considering the geographical distribution of walking. Some or all of the above processing in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the collection of progress information.

[0082] The progress collection unit can improve the accuracy of its data collection by referring to relevant literature on walking during the data collection process. For example, the progress collection unit can update its progress collection criteria by referring to the latest walking research. The progress collection unit can improve its progress collection methods based on literature on walking. The progress collection unit can improve the accuracy of its progress collection by referring to literature on the effects of walking. Thus, by referring to relevant literature on walking, the accuracy of the data collection can be improved. Some or all of the above-described processes in the progress collection unit may be performed using AI, for example, or without AI. For example, the progress collection unit can input walking-related literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the data collection.

[0083] The service provider can estimate the user's emotions and adjust how feedback and advice are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed feedback. If the user is in a hurry, the service provider can provide concise feedback. If the user is excited, the service provider can provide visually stimulating feedback. This allows for the provision of more appropriate information by adjusting how feedback and advice are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The service provider can provide optimal advice by referring to past data when providing feedback and advice. For example, the service provider can provide appropriate feedback based on the user's past walking history. The service provider can refer to the user's past health data and provide advice according to their health condition. The service provider can analyze the user's past feedback history and provide optimal advice. This allows for the provision of more appropriate advice by referring to past data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0085] The service provider can apply different advice methods to each user category when providing feedback and advice. For example, the service provider can provide advice tailored to the user's age, gender, or exercise experience. By applying different advice methods to each user category, more appropriate advice can be provided. 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 user category data into a generating AI and have the generating AI apply the advice method.

[0086] The service provider can estimate the user's emotions and adjust the importance of feedback and advice based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed feedback. If the user is in a hurry, the service provider can provide concise feedback. If the user is excited, the service provider can provide visually stimulating feedback. This allows for the provision of more appropriate information by adjusting the importance of feedback and advice 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 service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The service provider can analyze how advice changes based on the user's submission timing when providing feedback and advice. For example, if a user submits early in the morning, the service provider can provide advice about morning walks. If a user submits at night, the service provider can provide advice about evening walks. If a user submits on the weekend, the service provider can provide advice about weekend walks. By analyzing how advice changes based on the user's submission timing, the service provider can provide more appropriate advice. 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 user submission timing data into a generating AI and have the generating AI perform an analysis of how advice changes.

[0088] The service provider can analyze advice by referring to the user's relevant market data when providing feedback and advice. For example, the service provider can provide appropriate advice based on health market data in the user's area. The service provider can provide optimal advice by referring to market data corresponding to the user's age group. The service provider can provide appropriate advice by referring to market data corresponding to the user's gender. This allows for the provision of more appropriate advice by referring to the user's relevant market data. 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 relevant market data into a generating AI and have the generating AI perform the analysis of the advice.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The walking support system can further enhance motivation by leveraging the user's social network. For example, the data collection unit can collect walking data from the user's friends and family, and the generation unit can generate a collaborative walking plan based on that data. The progress collection unit can compare the user's progress with that of their friends and family, and the provision unit can provide the results as feedback. This allows the user to continue walking while feeling a sense of social connection, thus improving their motivation. Furthermore, the provision unit can also display encouraging messages from friends and family. This encourages the user and increases their desire to continue walking.

[0091] The walking support system can also collect user meal data and incorporate it into the walking plan. The data collection unit provides an interface for users to input their daily meal contents, and the generation unit can generate a walking plan that takes calorie consumption into account based on that data. The progress collection unit integrates the user's meal data and walking data, and the provision unit can provide the results as feedback. This allows users to maintain their health while balancing diet and exercise. Furthermore, the provision unit can also provide dietary advice and recipes. This enables users to lead a healthier lifestyle.

[0092] The walking support system can also collect user sleep data and incorporate it into the walking plan. The collection unit collects sleep data from the user's smartwatch or fitness tracker, and the generation unit can generate an optimal walking plan based on that data. The progress collection unit integrates the user's sleep data and walking data, and the provision unit can provide the results as feedback. This allows the user to continue walking while improving the quality of their sleep. Furthermore, the provision unit can also provide advice on sleep and relaxation methods. This helps the user get better sleep.

[0093] The walking support system can further monitor the user's stress level and incorporate it into the walking plan. The data collection unit collects biometric data such as the user's heart rate and skin electrical activity, and the generation unit can generate a stress-reducing walking plan based on this data. The progress collection unit integrates the user's stress level with the walking data, and the provision unit can provide the results as feedback. This allows the user to continue walking while reducing stress. Furthermore, the provision unit can also provide advice on stress management and relaxation techniques. This allows the user to enjoy walking in a more relaxed state.

[0094] The walking support system can further customize walking plans by taking into account the user's hobbies and interests. The data collection unit collects information about the user's hobbies and interests, and the generation unit can generate walking plans that include routes and activities that are likely to interest the user based on this data. The progress collection unit integrates the user's walking data with data about their hobbies, and the provision unit can provide the results as feedback. This allows users to enjoy walking that suits their interests and improves their motivation. Furthermore, the provision unit can also provide information about events and activities related to the user's hobbies. This allows users to discover new hobbies and interests through walking.

[0095] The walking support system can further optimize walking plans by utilizing the user's health checkup data. The data collection unit collects the user's past health checkup results, and the generation unit generates a walking plan tailored to the user's health condition based on that data. The progress collection unit integrates the user's health checkup data and walking data, and the provision unit provides the results as feedback. This allows the user to execute a walking plan best suited to their health condition. Furthermore, the provision unit can also provide health management advice based on the health checkup results, enabling the user to maintain their health more effectively.

[0096] The walking support system can further customize walking plans to take into account the user's work environment. The data collection unit collects information about the user's work environment, and the generation unit generates a workplace walking plan based on that data. The progress collection unit collects the user's walking data at work, and the provision unit provides the results as feedback. This allows users to continue walking at work and maintain their health. Furthermore, the provision unit can also provide advice on workplace health management, enabling users to manage their health in a way that is appropriate for their work environment.

[0097] The walking support system can further customize walking plans to match the user's travel plans. The data collection unit gathers information about the user's travel plans, and the generation unit generates walking plans for the travel destination based on this data. The progress collection unit collects walking data during the user's trip, and the provision unit provides the results as feedback. This allows users to maintain their health even while traveling. Furthermore, the provision unit can suggest walking routes that include tourist spots and landmarks at the travel destination. This allows users to continue walking while enjoying their trip.

[0098] The walking support system can further estimate the user's emotions and adjust the difficulty of the walking plan based on those emotions. The data collection unit collects the user's emotional data, and the generation unit generates a walking plan tailored to those emotions based on that data. The progress collection unit integrates the user's emotional data and walking data, and the provision unit provides the results as feedback. This allows the user to execute a walking plan that is optimal for their emotional state. For example, if the user is feeling stressed, a relaxing route can be suggested. This allows the user to enjoy walking in a way that suits their emotions.

[0099] The walking support system can further customize walking plans by taking into account the user's seasonal health data. The data collection unit collects the user's seasonal health data, and the generation unit generates seasonal walking plans based on that data. The progress collection unit collects the user's seasonal walking data, and the provision unit provides the results as feedback. This allows the user to manage their health according to the season. Furthermore, the provision unit can also provide advice on seasonal health management, enabling the user to manage their health according to the season.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection unit gathers information such as the user's fitness level, daily activity level, and target healthy life expectancy. For example, the data collection unit collects data to evaluate the user's cardiopulmonary function, muscle strength, and endurance, and measures daily activity levels using devices such as pedometers and calorie counters. It also collects information about the user's target healthy life expectancy through questionnaires and interviews. Step 2: The generation unit generates personalized walking plans based on the information collected by the collection unit. For example, it uses a generation AI to generate customized walking plans based on the user's fitness level and activity level, and also generates walking plans based on the user's target healthy lifespan. Step 3: The progress collection unit collects the progress of users walking based on the walking plan generated by the generation unit. For example, it uses GPS devices or smartphone apps to measure the user's walking achievement and progress, and collects data periodically. Step 4: The service provider provides feedback and advice based on the progress collected by the progress collection unit. For example, it provides feedback on areas for improvement in exercise and setting the next goal based on the user's walking progress, and uses generative AI to provide appropriate advice.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the collection unit, generation unit, progress collection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's fitness level and daily activity level using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A analyzes this data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates an individualized walking plan based on the collected data. The progress collection unit collects the user's walking progress using the GPS function of the smart device 14 or a smartphone app, and transmits it to the data processing unit 12 via the control unit 46A. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates feedback and advice based on the progress, and provides it to the user through the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the collection unit, generation unit, progress collection unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's fitness level and daily activity level using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes this data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates an individualized walking plan based on the collected data. The progress collection unit collects the user's walking progress using, for example, the GPS function of the smart glasses 214 or a smartphone app, and transmits it to the data processing unit 12 by the control unit 46A. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates feedback and advice based on the progress, and provides it to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, generation unit, progress collection unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's fitness level and daily activity level using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A analyzes this data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates an individualized walking plan based on the collected data. The progress collection unit collects the user's walking progress using, for example, the GPS function of the headset terminal 314 or a smartphone app, and transmits it to the data processing unit 12 by the control unit 46A. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates feedback and advice based on the progress, and provides it to the user through the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, generation unit, progress collection unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's fitness level and daily activity level using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes this data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates an individualized walking plan based on the collected data. The progress collection unit collects the user's walking progress using, for example, the GPS function of the robot 414 or a smartphone app, and transmits it to the data processing unit 12 by the control unit 46A. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates feedback and advice based on the progress, and provides it to the user through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A data collection unit that collects information such as the user's physical fitness level, daily activity level, and target healthy lifespan, A generation unit that generates an individualized walking plan based on the information collected by the collection unit, A progress collection unit collects the progress of users who are walking based on the walking plan generated by the generation unit, The system includes a provisioning unit that provides feedback and advice based on the progress status collected by the progress collection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past health data and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current living situation and health status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is The system estimates the user's emotions and adjusts how the walking plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating a walking plan, adjust the level of detail in the plan based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating walking plans, different plan generation algorithms are applied depending on the user's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the length of the walking plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating walking plans, the system prioritizes plans based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating walking plans, the order of the plans is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned progress collection unit, We estimate user sentiment and adjust progress tracking criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress collection unit, When collecting progress data, consider the interrelationships between walking activities to improve the accuracy of the data collection. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress collection unit, When collecting progress data, user attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress collection unit, It estimates the user's sentiment and adjusts the order in which progress collection results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress collection unit, When collecting progress data, the geographical distribution of walking should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress collection unit, When collecting progress data, refer to relevant literature on walking to improve the accuracy of the data collection. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback and advice are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing feedback or advice, we refer to past data to provide the best possible advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing feedback and advice, we apply different advice methods depending on the user category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the importance of feedback and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing feedback and advice, analyze how the advice changes based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing feedback and advice, we analyze the advice by referring to relevant market data for the user. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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 data collection unit that collects information such as the user's physical fitness level, daily activity level, and target healthy lifespan, A generation unit that generates an individualized walking plan based on the information collected by the collection unit, A progress collection unit collects the progress of users who are walking based on the walking plan generated by the generation unit, The system includes a provisioning unit that provides feedback and advice based on the progress status collected by the progress collection unit. A system characterized by the following features.

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

3. The aforementioned collection unit is Analyze the user's past health data and select the optimal method for collecting information. The system according to feature 1.

4. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current living situation and health status. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

8. The generating unit is The system estimates the user's emotions and adjusts how the walking plan is presented based on those estimated emotions. The system according to feature 1.

9. The generating unit is When generating a walking plan, adjust the level of detail in the plan based on the user's health goals. The system according to feature 1.

10. The generating unit is When generating walking plans, different plan generation algorithms are applied depending on the user's activity history. The system according to feature 1.

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