system

Abstract

The system according to this embodiment aims to suggest optimal walking courses and daily goals by utilizing the user's step count and location information. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a guide unit, an optimization unit, and a feedback unit. The collection unit collects the user's step count and location information. The suggestion unit suggests an optimal walking course and daily goals based on the data collected by the collection unit. The guide unit provides voice guidance based on the walking course and goals suggested by the suggestion unit. The optimization unit uses real-time GPS tracking and an optimization algorithm. The feedback unit provides an interactive feedback system using voice recognition.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully carried out to utilize the user's walking steps and position information to propose an optimal walking course and goals, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize the user's walking steps and position information to propose an optimal walking course and daily goals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a suggestion unit, a guide unit, an optimization unit, and a feedback unit. The data collection unit collects the user's step count and location information. The suggestion unit suggests an optimal walking course and daily goals based on the data collected by the data collection unit. The guide unit provides voice guidance based on the walking course and goals suggested by the suggestion unit. The optimization unit uses real-time GPS tracking and an optimization algorithm. The feedback unit provides an interactive feedback system using voice recognition. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal walking courses and daily goals by utilizing the user's step count and location information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The walking support system according to an embodiment of the present invention is an application that collects the user's step count and location information and proposes the optimal walking course and daily goals. This walking support system collects the user's step count and location information and proposes the optimal walking course and daily goals. Furthermore, it improves the user's motivation and supports health maintenance through a voice guidance function. For example, the walking support system uses real-time GPS tracking and an optimization algorithm to make customized suggestions based on the user's behavior patterns. It also has an interactive feedback system that uses voice recognition and optimizes the feedback based on the user's response. As a result, the user's step count increase rate improves by an average of 20%, and the rate of maintaining exercise habits improves. It also contributes to reducing health risks. The target users are those who want to overcome a lack of exercise, those who want to maintain their health while having fun, and those who want to develop a regular exercise habit. This walking support system provides an AI-powered individually optimized walking plan, incorporates gamification elements, and offers an interactive experience combining voice and visuals. As a result, the walking support system can collect the user's step count and location information, propose the optimal walking course and daily goals, improve motivation through a voice guidance function, and support health maintenance.

[0029] The walking support system according to the embodiment comprises a data collection unit, a suggestion unit, a guide unit, an optimization unit, and a feedback unit. The data collection unit collects the user's step count and location information. The data collection unit includes, for example, a sensor for counting the user's steps. The data collection unit can also acquire the user's location information using GPS functionality. The data collection unit can collect the user's step count and location information in real time, for example, using sensors mounted on a smartphone or wearable device. The suggestion unit proposes the optimal walking course and daily goals based on the data collected by the data collection unit. The suggestion unit can, for example, use AI to analyze the user's behavior patterns and propose the optimal walking course and goals. The suggestion unit can, for example, propose an individually optimized walking plan based on the user's past data. The guide unit provides voice guidance based on the walking course and goals proposed by the suggestion unit. The guide unit can, for example, use speech synthesis technology to provide voice guidance to the user in real time. The guide unit can, for example, provide directions to the next destination based on the user's current location information. The optimization unit uses real-time GPS tracking and optimization algorithms to provide customized suggestions based on the user's behavior patterns. For example, the optimization unit can suggest the optimal walking course in real time based on the user's current location information and past behavior patterns. For example, the optimization unit can analyze the user's behavior patterns using AI and suggest the optimal walking course. The feedback unit provides an interactive feedback system using speech recognition and optimizes the feedback based on the user's responses. For example, the feedback unit can analyze the user's voice input and provide appropriate feedback. For example, the feedback unit can adjust the content and timing of the feedback based on the user's responses. As a result, the walking support system according to the embodiment can collect the user's step count and location information, suggest the optimal walking course and daily goals, improve motivation through the voice guidance function, and support the maintenance of health.

[0030] The data collection unit collects user step counts and location information. For example, the unit is equipped with sensors to count the user's steps. Specifically, it can use acceleration sensors and gyroscope sensors to detect the user's walking motion and accurately count steps. The data collection unit can also acquire user location information using GPS functionality. GPS functionality can pinpoint the user's current location with high accuracy and track their movement path in real time. The data collection unit can collect user step counts and location information in real time using sensors installed in smartphones or wearable devices. This allows the data collection unit to gain a detailed understanding of the user's exercise status and movement path, and to accumulate data. Furthermore, the data collection unit can transmit this data to a cloud server and share it with other departments. For example, by making the collected data accessible to the proposal and optimization departments, the overall system collaboration can be strengthened. Additionally, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to user needs and situations. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The suggestion unit proposes optimal walking routes and daily goals based on data collected by the data collection unit. For example, the suggestion unit can use AI to analyze user behavior patterns and propose optimal walking routes and goals. Specifically, the AI ​​analyzes the user's past walking data and location information to learn their exercise habits and preferences. This allows the suggestion unit to individually propose optimal walking routes and goals for each user. For instance, it can propose a new walking route considering the user's past routes, frequency, and time of day. Furthermore, the suggestion unit can adjust daily step count targets and exercise intensity according to the user's health condition and exercise goals. This enables the suggestion unit to support users in maintaining their health and improving their exercise habits. Additionally, the suggestion unit can collect user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, it can adjust future suggestions based on the user's experience and impressions after actually walking the suggested route. The suggestion unit can also propose optimal walking routes considering external factors such as season, weather, and time of day. This allows the suggestion unit to consistently provide users with the most suitable walking plan and support their health maintenance.

[0032] The guide unit provides audio guidance based on the walking courses and goals proposed by the suggestion unit. The guide unit can provide real-time audio guidance to the user, for example, using speech synthesis technology. Specifically, the guide unit provides directions to the next destination based on the user's current location. Audio guidance is highly convenient because users can receive directions simply by listening, without having to operate their smartphones or wearable devices while walking. Furthermore, the guide unit can adjust the guidance content in real time according to the user's walking pace and circumstances. For example, if the user is walking faster than planned, it recalculates the distance and time to the next destination and provides guidance accordingly. The guide unit can also provide encouraging messages and information about the effects of exercise to boost user motivation. This allows the guide unit to support users in continuing to enjoy walking. Additionally, the guide unit can collect user feedback and continuously improve the accuracy and effectiveness of the guidance content. For example, if a user provides feedback on the content or timing of the audio guidance, the guide unit adjusts the guidance content based on that feedback. This allows the guide unit to always provide the user with the optimal audio guidance, maximizing the effectiveness of walking.

[0033] The optimization unit uses real-time GPS tracking and optimization algorithms to provide customized suggestions based on the user's behavior patterns. For example, the optimization unit can suggest the optimal walking course in real time based on the user's current location and past behavior patterns. Specifically, the optimization unit uses AI to analyze the user's behavior patterns and suggest the optimal walking course. The AI ​​learns from the user's past data to understand the user's preferences and exercise habits. This allows the optimization unit to suggest individually optimized walking courses for each user. For example, it suggests a new walking course considering the courses the user has walked in the past, the frequency of those walks, and the time of day. The optimization unit can also adjust daily step targets and exercise intensity according to the user's health condition and exercise goals. This allows the optimization unit to support the user in maintaining their health and improving their exercise habits. Furthermore, the optimization unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it adjusts the next suggestion based on the user's results and impressions after actually walking the suggested course. The optimization unit can also suggest the optimal walking course considering external factors such as season, weather, and time of day. This allows the optimization unit to always provide the user with the optimal walking plan, supporting them in maintaining their health.

[0034] The feedback unit provides an interactive feedback system utilizing speech recognition, optimizing feedback based on user responses. For example, the feedback unit can analyze user voice input and provide appropriate feedback. Specifically, it responds in real-time to voice commands and questions uttered by the user during walking. Using speech recognition technology, it accurately analyzes the user's statements and provides appropriate feedback. For instance, if a user asks, "Where is my next destination?", the feedback unit provides directions to the next destination based on the user's current location. Furthermore, if a user discusses fatigue or anxiety experienced during walking, the feedback unit can offer encouraging messages or suggest breaks. This allows the feedback unit to create an environment that makes it easier for users to continue walking. Additionally, the feedback unit can adjust the content and timing of feedback based on user responses. For example, if a user shows a positive response to a particular piece of feedback, it reinforces that feedback. Conversely, if a user shows a negative response, it reviews the content and timing of the feedback. This allows the feedback unit to provide optimal feedback to the user, maximizing the effectiveness of walking.

[0035] The data collection unit can collect the user's step count and location information in real time. The data collection unit is equipped with, for example, a sensor for counting the user's step count. The data collection unit can acquire the user's location information using, for example, GPS functionality. The data collection unit can collect the user's step count and location information in real time using, for example, sensors installed in smartphones or wearable devices. This allows for more accurate data to be used to make suggestions by collecting the user's step count and location information in real time. The specific definition and criteria of "real time" include, for example, the frequency and delay time of data collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can collect the user's step count and location information in real time and input that data into AI for analysis.

[0036] The suggestion unit can analyze the user's behavior patterns based on collected data and propose optimal walking routes and daily goals. For example, the suggestion unit can use AI to analyze the user's behavior patterns and propose optimal walking routes and goals. For example, the suggestion unit can propose individually optimized walking plans based on the user's past data. This allows for the proposal of individually optimized walking routes and goals by analyzing the user's behavior patterns. Specific methods and criteria for analyzing behavior patterns include, for example, data types and analysis algorithms. Some or all of the above-described processes in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input collected data into AI, which can then analyze the behavior patterns and propose optimal walking routes and goals.

[0037] The guide unit can provide audio guidance based on the proposed walking course and goals. The guide unit can provide real-time audio guidance to the user, for example, using speech synthesis technology. The guide unit can provide directions to the next destination based on the user's current location information. By providing audio guidance, it is possible to improve the user's motivation and support the maintenance of their health. The specific content and method of providing the audio guidance include, for example, the content of the guide, the type and tone of the voice. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the content of the audio guidance into the AI ​​based on the proposed walking course and goals, and the AI ​​can generate and provide the audio guidance.

[0038] The optimization unit can make customized suggestions based on the user's behavior patterns using real-time GPS tracking and optimization algorithms. For example, the optimization unit can suggest the optimal walking course in real time based on the user's current location information and past behavior patterns. For example, the optimization unit can analyze the user's behavior patterns using AI and suggest the optimal walking course. This makes it possible to make customized suggestions based on the user's behavior patterns by using real-time GPS tracking and optimization algorithms. The specific content and methods of the customized suggestions include, for example, the criteria for suggestions and the details of the algorithm. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input real-time GPS tracking data into AI, and the AI ​​can make customized suggestions using the optimization algorithm.

[0039] The feedback unit provides an interactive feedback system utilizing speech recognition and can optimize feedback based on user responses. For example, the feedback unit can analyze the user's voice input and provide appropriate feedback. The feedback unit can also adjust the content and timing of feedback based on user responses. This allows for the optimization of feedback based on user responses by providing an interactive feedback system utilizing speech recognition. Specific details and methods of the interactive feedback system include, for example, the method of interaction with the user and the format of the feedback. Some or all of the above-described processes in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input user voice input data into AI, which can then generate and provide interactive feedback.

[0040] The data collection unit can analyze the user's past step count and location information to select the optimal data collection method. For example, the data collection unit can identify the time periods when the user walked the most in the past and concentrate data collection during those times. For example, the data collection unit can adjust the frequency of data collection at specific locations based on the user's past location information. For example, the data collection unit can customize the data collection method based on the user's past step count data. This allows for efficient data collection by analyzing the user's past data and selecting the optimal data collection method. Specific details and criteria for the optimal data collection method include, for example, the frequency of data collection and the sensors used. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's past step count and location information into the AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current health status and activity level when collecting step counts and location information. For example, if the user is tired, the data collection unit can reduce the collection frequency to lessen the burden. For example, if the user is actively engaged in activities, the data collection unit can increase the collection frequency to collect more detailed data. For example, the data collection unit can adjust the type of data collected according to the user's health status. This allows for more appropriate data collection by filtering data based on the user's health status and activity level. Specific filtering methods and criteria include, for example, filtering conditions and the algorithms used. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input data on the user's health status and activity level into the AI, which can then perform the filtering.

[0042] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the walking course and goals. For example, it can provide detailed suggestions for important walking courses, or concise suggestions for less important goals. The suggestion unit can adjust the level of detail in its suggestions according to their importance. This allows for more appropriate suggestions by adjusting the level of detail based on the importance of the walking course and goals. Specific criteria and evaluation methods for importance include, for example, the degree of goal achievement and the user's health status. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input data on the importance of walking courses and goals into the AI, which can then adjust the level of detail in its suggestions.

[0043] The guide unit can analyze the user's past responses to select the optimal guiding method when providing audio guidance. For example, the guide unit can prioritize providing guiding methods that the user has preferred in the past. For example, the guide unit can select the optimal guidance content based on the user's past responses. For example, the guide unit can analyze the user's past responses and customize the guiding method. This allows for the selection of the optimal guiding method and more appropriate guidance by analyzing the user's past responses. Specific details and criteria for the optimal guiding method include, for example, the format of the guide and adjustment methods based on user responses. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input data on the user's past responses into AI, which can then select the optimal guiding method.

[0044] The optimization unit can analyze the user's past behavior patterns and optimize the optimization algorithm during the optimization process. For example, the optimization unit can identify the time periods when the user has walked the most in the past and concentrate optimization during those times. For example, the optimization unit can adjust the optimization frequency at specific locations based on the user's past behavior patterns. For example, the optimization unit can customize the optimization algorithm based on the user's past behavior data. This allows for more appropriate optimization by analyzing the user's past behavior patterns and optimizing the optimization algorithm. The specific content and methods of the optimization algorithm include, for example, the type of algorithm used and the optimization criteria. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's past behavior pattern data into AI, which can then optimize the optimization algorithm.

[0045] The feedback unit can analyze the user's past responses to select the optimal feedback method when providing feedback. For example, the feedback unit can prioritize providing feedback methods that the user has preferred in the past. For example, the feedback unit can select the optimal feedback content based on the user's past responses. For example, the feedback unit can analyze the user's past responses and customize the feedback method. This allows for the selection of the optimal feedback method and more appropriate feedback by analyzing the user's past responses. Specific details and criteria for the optimal feedback method include, for example, the format of the feedback and the adjustment method based on the user's response. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's past response data into AI, which can then select the optimal feedback method.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The data collection unit collects user health data and can adjust the frequency and content of data collection based on the user's health status. For example, if the user's heart rate is high, the collection frequency can be reduced to lessen the burden. If the user is in good health, detailed data can be collected. If the user is fatigued, the type of data collected can be adjusted. This enables appropriate data collection tailored to the user's health status.

[0048] The optimization unit can analyze the user's past behavior patterns and adjust the optimization algorithm based on those patterns. For example, it can identify the time periods when the user walked the most in the past and concentrate optimization during those times. It can also adjust the optimization frequency in specific locations based on the user's past behavior patterns. The optimization algorithm can be customized based on the user's past behavior data. This enables optimal suggestions based on the user's past behavior patterns.

[0049] The data collection unit can analyze the user's past step count and location information to select the optimal collection method. For example, it can identify the time periods when the user walked the most in the past and concentrate data collection during those times. It can also adjust the collection frequency at specific locations based on the user's past location information. Furthermore, it can customize the collection method based on the user's past step count data. This enables efficient data collection based on the user's past data.

[0050] The guide unit can analyze the user's past responses when providing audio guides to select the optimal guiding method. For example, it can prioritize providing guiding methods that the user has preferred in the past. It can select the optimal guide content based on the user's past responses. It can analyze the user's past responses and customize the guiding method. This makes it possible to provide optimal guidance based on the user's past responses.

[0051] The feedback unit can analyze the user's past responses to select the optimal feedback method when providing feedback. For example, it can prioritize providing feedback methods that the user has preferred in the past. It can select the optimal feedback content based on the user's past responses. It can analyze the user's past responses and customize the feedback method. This makes it possible to provide optimal feedback based on the user's past responses.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The data collection unit collects the user's step count and location information. The data collection unit is equipped with a sensor to count the user's steps, for example, and can acquire the user's location information using GPS functionality. The data collection unit collects the user's step count and location information in real time using sensors installed in smartphones and wearable devices. Step 2: The suggestion unit proposes optimal walking routes and daily goals based on the data collected by the data collection unit. The suggestion unit can use AI to analyze the user's behavior patterns and propose optimal walking routes and goals. Based on the user's past data, the suggestion unit proposes a personalized walking plan. Step 3: The guide unit provides audio guidance based on the walking course and goals proposed by the suggestion unit. The guide unit provides real-time audio guidance to the user using speech synthesis technology. The guide unit provides directions to the next destination based on the user's current location information. Step 4: The optimization unit uses real-time GPS tracking and optimization algorithms to provide customized suggestions based on the user's behavior patterns. The optimization unit proposes the optimal walking course in real time based on the user's current location information and past behavior patterns. The optimization unit uses AI to analyze the user's behavior patterns and propose the optimal walking course. Step 5: The feedback unit provides an interactive feedback system using speech recognition and optimizes the feedback based on the user's responses. The feedback unit analyzes the user's voice input and provides appropriate feedback. The feedback unit adjusts the content and timing of the feedback based on the user's responses.

[0054] (Example of form 2) The walking support system according to an embodiment of the present invention is an application that collects the user's step count and location information and proposes the optimal walking course and daily goals. This walking support system collects the user's step count and location information and proposes the optimal walking course and daily goals. Furthermore, it improves the user's motivation and supports health maintenance through a voice guidance function. For example, the walking support system uses real-time GPS tracking and an optimization algorithm to make customized suggestions based on the user's behavior patterns. It also has an interactive feedback system that uses voice recognition and optimizes the feedback based on the user's response. As a result, the user's step count increase rate improves by an average of 20%, and the rate of maintaining exercise habits improves. It also contributes to reducing health risks. The target users are those who want to overcome a lack of exercise, those who want to maintain their health while having fun, and those who want to develop a regular exercise habit. This walking support system provides an AI-powered individually optimized walking plan, incorporates gamification elements, and offers an interactive experience combining voice and visuals. As a result, the walking support system can collect the user's step count and location information, propose the optimal walking course and daily goals, improve motivation through a voice guidance function, and support health maintenance.

[0055] The walking support system according to the embodiment comprises a data collection unit, a suggestion unit, a guide unit, an optimization unit, and a feedback unit. The data collection unit collects the user's step count and location information. The data collection unit includes, for example, a sensor for counting the user's steps. The data collection unit can also acquire the user's location information using GPS functionality. The data collection unit can collect the user's step count and location information in real time, for example, using sensors mounted on a smartphone or wearable device. The suggestion unit proposes the optimal walking course and daily goals based on the data collected by the data collection unit. The suggestion unit can, for example, use AI to analyze the user's behavior patterns and propose the optimal walking course and goals. The suggestion unit can, for example, propose an individually optimized walking plan based on the user's past data. The guide unit provides voice guidance based on the walking course and goals proposed by the suggestion unit. The guide unit can, for example, use speech synthesis technology to provide voice guidance to the user in real time. The guide unit can, for example, provide directions to the next destination based on the user's current location information. The optimization unit uses real-time GPS tracking and optimization algorithms to provide customized suggestions based on the user's behavior patterns. For example, the optimization unit can suggest the optimal walking course in real time based on the user's current location information and past behavior patterns. For example, the optimization unit can analyze the user's behavior patterns using AI and suggest the optimal walking course. The feedback unit provides an interactive feedback system using speech recognition and optimizes the feedback based on the user's responses. For example, the feedback unit can analyze the user's voice input and provide appropriate feedback. For example, the feedback unit can adjust the content and timing of the feedback based on the user's responses. As a result, the walking support system according to the embodiment can collect the user's step count and location information, suggest the optimal walking course and daily goals, improve motivation through the voice guidance function, and support the maintenance of health.

[0056] The data collection unit collects user step counts and location information. For example, the unit is equipped with sensors to count the user's steps. Specifically, it can use acceleration sensors and gyroscope sensors to detect the user's walking motion and accurately count steps. The data collection unit can also acquire user location information using GPS functionality. GPS functionality can pinpoint the user's current location with high accuracy and track their movement path in real time. The data collection unit can collect user step counts and location information in real time using sensors installed in smartphones or wearable devices. This allows the data collection unit to gain a detailed understanding of the user's exercise status and movement path, and to accumulate data. Furthermore, the data collection unit can transmit this data to a cloud server and share it with other departments. For example, by making the collected data accessible to the proposal and optimization departments, the overall system collaboration can be strengthened. Additionally, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to user needs and situations. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0057] The suggestion unit proposes optimal walking routes and daily goals based on data collected by the data collection unit. For example, the suggestion unit can use AI to analyze user behavior patterns and propose optimal walking routes and goals. Specifically, the AI ​​analyzes the user's past walking data and location information to learn their exercise habits and preferences. This allows the suggestion unit to individually propose optimal walking routes and goals for each user. For instance, it can propose a new walking route considering the user's past routes, frequency, and time of day. Furthermore, the suggestion unit can adjust daily step count targets and exercise intensity according to the user's health condition and exercise goals. This enables the suggestion unit to support users in maintaining their health and improving their exercise habits. Additionally, the suggestion unit can collect user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, it can adjust future suggestions based on the user's experience and impressions after actually walking the suggested route. The suggestion unit can also propose optimal walking routes considering external factors such as season, weather, and time of day. This allows the suggestion unit to consistently provide users with the most suitable walking plan and support their health maintenance.

[0058] The guide unit provides audio guidance based on the walking courses and goals proposed by the suggestion unit. The guide unit can provide real-time audio guidance to the user, for example, using speech synthesis technology. Specifically, the guide unit provides directions to the next destination based on the user's current location. Audio guidance is highly convenient because users can receive directions simply by listening, without having to operate their smartphones or wearable devices while walking. Furthermore, the guide unit can adjust the guidance content in real time according to the user's walking pace and circumstances. For example, if the user is walking faster than planned, it recalculates the distance and time to the next destination and provides guidance accordingly. The guide unit can also provide encouraging messages and information about the effects of exercise to boost user motivation. This allows the guide unit to support users in continuing to enjoy walking. Additionally, the guide unit can collect user feedback and continuously improve the accuracy and effectiveness of the guidance content. For example, if a user provides feedback on the content or timing of the audio guidance, the guide unit adjusts the guidance content based on that feedback. This allows the guide unit to always provide the user with the optimal audio guidance, maximizing the effectiveness of walking.

[0059] The optimization unit uses real-time GPS tracking and optimization algorithms to provide customized suggestions based on the user's behavior patterns. For example, the optimization unit can suggest the optimal walking course in real time based on the user's current location and past behavior patterns. Specifically, the optimization unit uses AI to analyze the user's behavior patterns and suggest the optimal walking course. The AI ​​learns from the user's past data to understand the user's preferences and exercise habits. This allows the optimization unit to suggest individually optimized walking courses for each user. For example, it suggests a new walking course considering the courses the user has walked in the past, the frequency of those walks, and the time of day. The optimization unit can also adjust daily step targets and exercise intensity according to the user's health condition and exercise goals. This allows the optimization unit to support the user in maintaining their health and improving their exercise habits. Furthermore, the optimization unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it adjusts the next suggestion based on the user's results and impressions after actually walking the suggested course. The optimization unit can also suggest the optimal walking course considering external factors such as season, weather, and time of day. This allows the optimization unit to always provide the user with the optimal walking plan, supporting them in maintaining their health.

[0060] The feedback unit provides an interactive feedback system utilizing speech recognition, optimizing feedback based on user responses. For example, the feedback unit can analyze user voice input and provide appropriate feedback. Specifically, it responds in real-time to voice commands and questions uttered by the user during walking. Using speech recognition technology, it accurately analyzes the user's statements and provides appropriate feedback. For instance, if a user asks, "Where is my next destination?", the feedback unit provides directions to the next destination based on the user's current location. Furthermore, if a user discusses fatigue or anxiety experienced during walking, the feedback unit can offer encouraging messages or suggest breaks. This allows the feedback unit to create an environment that makes it easier for users to continue walking. Additionally, the feedback unit can adjust the content and timing of feedback based on user responses. For example, if a user shows a positive response to a particular piece of feedback, it reinforces that feedback. Conversely, if a user shows a negative response, it reviews the content and timing of the feedback. This allows the feedback unit to provide optimal feedback to the user, maximizing the effectiveness of walking.

[0061] The data collection unit can collect the user's step count and location information in real time. The data collection unit is equipped with, for example, a sensor for counting the user's step count. The data collection unit can acquire the user's location information using, for example, GPS functionality. The data collection unit can collect the user's step count and location information in real time using, for example, sensors installed in smartphones or wearable devices. This allows for more accurate data to be used to make suggestions by collecting the user's step count and location information in real time. The specific definition and criteria of "real time" include, for example, the frequency and delay time of data collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can collect the user's step count and location information in real time and input that data into AI for analysis.

[0062] The suggestion unit can analyze the user's behavior patterns based on collected data and propose optimal walking routes and daily goals. For example, the suggestion unit can use AI to analyze the user's behavior patterns and propose optimal walking routes and goals. For example, the suggestion unit can propose individually optimized walking plans based on the user's past data. This allows for the proposal of individually optimized walking routes and goals by analyzing the user's behavior patterns. Specific methods and criteria for analyzing behavior patterns include, for example, data types and analysis algorithms. Some or all of the above-described processes in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input collected data into AI, which can then analyze the behavior patterns and propose optimal walking routes and goals.

[0063] The guide unit can provide audio guidance based on the proposed walking course and goals. The guide unit can provide real-time audio guidance to the user, for example, using speech synthesis technology. The guide unit can provide directions to the next destination based on the user's current location information. By providing audio guidance, it is possible to improve the user's motivation and support the maintenance of their health. The specific content and method of providing the audio guidance include, for example, the content of the guide, the type and tone of the voice. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the content of the audio guidance into the AI ​​based on the proposed walking course and goals, and the AI ​​can generate and provide the audio guidance.

[0064] The optimization unit can make customized suggestions based on the user's behavior patterns using real-time GPS tracking and optimization algorithms. For example, the optimization unit can suggest the optimal walking course in real time based on the user's current location information and past behavior patterns. For example, the optimization unit can analyze the user's behavior patterns using AI and suggest the optimal walking course. This makes it possible to make customized suggestions based on the user's behavior patterns by using real-time GPS tracking and optimization algorithms. The specific content and methods of the customized suggestions include, for example, the criteria for suggestions and the details of the algorithm. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input real-time GPS tracking data into AI, and the AI ​​can make customized suggestions using the optimization algorithm.

[0065] The feedback unit provides an interactive feedback system utilizing speech recognition and can optimize feedback based on user responses. For example, the feedback unit can analyze the user's voice input and provide appropriate feedback. The feedback unit can also adjust the content and timing of feedback based on user responses. This allows for the optimization of feedback based on user responses by providing an interactive feedback system utilizing speech recognition. Specific details and methods of the interactive feedback system include, for example, the method of interaction with the user and the format of the feedback. Some or all of the above-described processes in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input user voice input data into AI, which can then generate and provide interactive feedback.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of collecting step counts and location information based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the collection timing to collect only the important data. By adjusting the collection timing based on the user's emotions, the burden on the user is reduced and more appropriate data collection becomes possible. Specific methods and criteria for estimating emotions include, for example, the sensors and algorithms used. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into AI, which can estimate the emotions and adjust the collection timing.

[0067] The data collection unit can analyze the user's past step count and location information to select the optimal data collection method. For example, the data collection unit can identify the time periods when the user walked the most in the past and concentrate data collection during those times. For example, the data collection unit can adjust the frequency of data collection at specific locations based on the user's past location information. For example, the data collection unit can customize the data collection method based on the user's past step count data. This allows for efficient data collection by analyzing the user's past data and selecting the optimal data collection method. Specific details and criteria for the optimal data collection method include, for example, the frequency of data collection and the sensors used. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's past step count and location information into the AI, which can then select the optimal data collection method.

[0068] The data collection unit can filter data based on the user's current health status and activity level when collecting step counts and location information. For example, if the user is tired, the data collection unit can reduce the collection frequency to lessen the burden. For example, if the user is actively engaged in activities, the data collection unit can increase the collection frequency to collect more detailed data. For example, the data collection unit can adjust the type of data collected according to the user's health status. This allows for more appropriate data collection by filtering data based on the user's health status and activity level. Specific filtering methods and criteria include, for example, filtering conditions and the algorithms used. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input data on the user's health status and activity level into the AI, which can then perform the filtering.

[0069] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide simple suggestions. If the user is in a hurry, the suggestion unit can provide quick suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Specific details and criteria for the presentation of suggestions include, for example, the format of the suggestion and the tone of expression. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user emotion data into AI, which can then adjust the way suggestions are presented.

[0070] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the walking course and goals. For example, it can provide detailed suggestions for important walking courses, or concise suggestions for less important goals. The suggestion unit can adjust the level of detail in its suggestions according to their importance. This allows for more appropriate suggestions by adjusting the level of detail based on the importance of the walking course and goals. Specific criteria and evaluation methods for importance include, for example, the degree of goal achievement and the user's health status. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input data on the importance of walking courses and goals into the AI, which can then adjust the level of detail in its suggestions.

[0071] The guide unit can estimate the user's emotions and adjust the content and tone of the voice guide based on the estimated emotions. For example, if the user is relaxed, the guide unit can provide guidance in a calm tone. For example, if the user is stressed, the guide unit can provide guidance that includes words of encouragement. For example, if the user is in a hurry, the guide unit can provide quick and concise guidance. By adjusting the content and tone of the voice guide based on the user's emotions, more appropriate guidance becomes possible. Specific methods for adjusting the content and tone of the voice guide include, for example, the content of the guide, the type of voice, and the tone. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input user emotion data into AI, and the AI ​​can adjust the content and tone of the voice guide.

[0072] The guide unit can analyze the user's past responses to select the optimal guiding method when providing audio guidance. For example, the guide unit can prioritize providing guiding methods that the user has preferred in the past. For example, the guide unit can select the optimal guidance content based on the user's past responses. For example, the guide unit can analyze the user's past responses and customize the guiding method. This allows for the selection of the optimal guiding method and more appropriate guidance by analyzing the user's past responses. Specific details and criteria for the optimal guiding method include, for example, the format of the guide and adjustment methods based on user responses. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input data on the user's past responses into AI, which can then select the optimal guiding method.

[0073] The optimization unit can estimate the user's emotions and adjust the parameters of the optimization algorithm based on the estimated emotions. For example, if the user is relaxed, the optimization unit can perform detailed optimization. For example, if the user is stressed, the optimization unit can perform simple optimization. For example, if the user is in a hurry, the optimization unit can perform rapid optimization. By adjusting the parameters of the optimization algorithm based on the user's emotions, more appropriate optimization becomes possible. Specific methods and criteria for adjusting the parameters of the optimization algorithm include, for example, the type of algorithm and the criteria for adjustment. Some or all of the above-described processes in the optimization unit may be performed using AI, or not. For example, the optimization unit can input user emotion data into the AI, and the AI ​​can adjust the parameters of the optimization algorithm.

[0074] The optimization unit can analyze the user's past behavior patterns and optimize the optimization algorithm during the optimization process. For example, the optimization unit can identify the time periods when the user has walked the most in the past and concentrate optimization during those times. For example, the optimization unit can adjust the optimization frequency at specific locations based on the user's past behavior patterns. For example, the optimization unit can customize the optimization algorithm based on the user's past behavior data. This allows for more appropriate optimization by analyzing the user's past behavior patterns and optimizing the optimization algorithm. The specific content and methods of the optimization algorithm include, for example, the type of algorithm used and the optimization criteria. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's past behavior pattern data into AI, which can then optimize the optimization algorithm.

[0075] The feedback unit can estimate the user's emotions and adjust the feedback content based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. For example, if the user is stressed, the feedback unit can provide simple feedback. For example, if the user is in a hurry, the feedback unit can provide quick feedback. This allows for more appropriate feedback by adjusting the feedback content based on the user's emotions. Specific methods and criteria for adjusting the feedback content include, for example, the format of the feedback and the details of its content. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into AI, and the AI ​​can adjust the feedback content.

[0076] The feedback unit can analyze the user's past responses to select the optimal feedback method when providing feedback. For example, the feedback unit can prioritize providing feedback methods that the user has preferred in the past. For example, the feedback unit can select the optimal feedback content based on the user's past responses. For example, the feedback unit can analyze the user's past responses and customize the feedback method. This allows for the selection of the optimal feedback method and more appropriate feedback by analyzing the user's past responses. Specific details and criteria for the optimal feedback method include, for example, the format of the feedback and the adjustment method based on the user's response. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's past response data into AI, which can then select the optimal feedback method.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The suggestion function can estimate the user's emotions and adjust the suggested walking routes based on those emotions. For example, if the user is feeling stressed, it can suggest a relaxing route surrounded by nature. If the user is relaxed, it can suggest a challenging route. If the user is in a hurry, it can suggest a short and effective route. This makes it possible to suggest the optimal walking route according to the user's emotions.

[0079] The data collection unit collects user health data and can adjust the frequency and content of data collection based on the user's health status. For example, if the user's heart rate is high, the collection frequency can be reduced to lessen the burden. If the user is in good health, detailed data can be collected. If the user is fatigued, the type of data collected can be adjusted. This enables appropriate data collection tailored to the user's health status.

[0080] The guide unit can estimate the user's emotions and adjust the content of the audio guide based on those estimates. For example, if the user is relaxed, the guide can be delivered in a calm tone. If the user is stressed, the guide can include words of encouragement. If the user is in a hurry, the guide can be delivered quickly and concisely. This makes it possible to provide the most appropriate audio guide tailored to the user's emotions.

[0081] The optimization unit can analyze the user's past behavior patterns and adjust the optimization algorithm based on those patterns. For example, it can identify the time periods when the user walked the most in the past and concentrate optimization during those times. It can also adjust the optimization frequency in specific locations based on the user's past behavior patterns. The optimization algorithm can be customized based on the user's past behavior data. This enables optimal suggestions based on the user's past behavior patterns.

[0082] The feedback unit can estimate the user's emotions and adjust the feedback content based on those emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is stressed, it can provide simple feedback. If the user is in a hurry, it can provide quick feedback. This makes it possible to provide optimal feedback tailored to the user's emotions.

[0083] The data collection unit can analyze the user's past step count and location information to select the optimal collection method. For example, it can identify the time periods when the user walked the most in the past and concentrate data collection during those times. It can also adjust the collection frequency at specific locations based on the user's past location information. Furthermore, it can customize the collection method based on the user's past step count data. This enables efficient data collection based on the user's past data.

[0084] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can offer detailed suggestions. If the user is stressed, it can offer simple suggestions. If the user is in a hurry, it can offer quick suggestions. This allows for optimal suggestions tailored to the user's emotions.

[0085] The guide unit can analyze the user's past responses when providing audio guides to select the optimal guiding method. For example, it can prioritize providing guiding methods that the user has preferred in the past. It can select the optimal guide content based on the user's past responses. It can analyze the user's past responses and customize the guiding method. This makes it possible to provide optimal guidance based on the user's past responses.

[0086] The optimization unit can estimate the user's emotions and adjust the parameters of the optimization algorithm based on those emotions. For example, if the user is relaxed, detailed optimization can be performed. If the user is stressed, simple optimization can be performed. If the user is in a hurry, rapid optimization can be performed. This enables optimal optimization tailored to the user's emotions.

[0087] The feedback unit can analyze the user's past responses to select the optimal feedback method when providing feedback. For example, it can prioritize providing feedback methods that the user has preferred in the past. It can select the optimal feedback content based on the user's past responses. It can analyze the user's past responses and customize the feedback method. This makes it possible to provide optimal feedback based on the user's past responses.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The data collection unit collects the user's step count and location information. The data collection unit is equipped with a sensor to count the user's steps, for example, and can acquire the user's location information using GPS functionality. The data collection unit collects the user's step count and location information in real time using sensors installed in smartphones and wearable devices. Step 2: The suggestion unit proposes optimal walking routes and daily goals based on the data collected by the data collection unit. The suggestion unit can use AI to analyze the user's behavior patterns and propose optimal walking routes and goals. Based on the user's past data, the suggestion unit proposes a personalized walking plan. Step 3: The guide unit provides audio guidance based on the walking course and goals proposed by the suggestion unit. The guide unit provides real-time audio guidance to the user using speech synthesis technology. The guide unit provides directions to the next destination based on the user's current location information. Step 4: The optimization unit uses real-time GPS tracking and optimization algorithms to provide customized suggestions based on the user's behavior patterns. The optimization unit proposes the optimal walking course in real time based on the user's current location information and past behavior patterns. The optimization unit uses AI to analyze the user's behavior patterns and propose the optimal walking course. Step 5: The feedback unit provides an interactive feedback system using speech recognition and optimizes the feedback based on the user's responses. The feedback unit analyzes the user's voice input and provides appropriate feedback. The feedback unit adjusts the content and timing of the feedback based on the user's responses.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] Each of the multiple elements described above, including the data collection unit, proposal unit, guide unit, optimization unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's step count and location information in real time using the sensors of the smart device 14. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal walking course and daily goals based on the collected data. The guide unit is implemented in the specific processing unit 46A of the smart device 14 and provides voice guidance. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes customized suggestions using real-time GPS tracking and an optimization algorithm. The feedback unit is implemented in the specific processing unit 46A of the smart device 14 and provides an interactive feedback system using voice recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the data collection unit, suggestion unit, guide unit, optimization unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's step count and location information in real time using the sensors of the smart glasses 214. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an optimal walking course and daily goals based on the collected data. The guide unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides voice guidance. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes customized suggestions using real-time GPS tracking and optimization algorithms. The feedback unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides an interactive feedback system using voice recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the data collection unit, suggestion unit, guide unit, optimization unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's step count and location information in real time using the sensors of the headset terminal 314. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an optimal walking course and daily goals based on the collected data. The guide unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides voice guidance. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes customized suggestions using real-time GPS tracking and an optimization algorithm. The feedback unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides an interactive feedback system using voice recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

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

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

[0137] In 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.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0141] The data processing system 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.

[0142] Each of the multiple elements described above, including the data collection unit, suggestion unit, guide unit, optimization unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's step count and location information in real time using the sensors of the robot 414. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests an optimal walking course and daily goals based on the collected data. The guide unit is implemented in the control unit 46A of the robot 414 and provides voice guidance. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes customized suggestions using real-time GPS tracking and an optimization algorithm. The feedback unit is implemented in the control unit 46A of the robot 414 and provides an interactive feedback system using voice recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] (Note 1) A collection unit that collects the user's step count and location information, Based on the data collected by the aforementioned collection unit, the proposal unit suggests the optimal walking course and daily goals. A guide unit provides audio guidance based on the walking courses and goals proposed by the aforementioned proposal unit, An optimization unit that uses real-time GPS tracking and optimization algorithms, It comprises a feedback unit that provides an interactive feedback system using speech recognition. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user step count and location information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the collected data, the system analyzes user behavior patterns and suggests optimal walking routes and daily goals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is Based on the suggested walking route and goals, an audio guide will be provided. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, Using real-time GPS tracking and optimization algorithms, we provide customized suggestions based on user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is It provides an interactive feedback system that utilizes speech recognition and optimizes feedback based on user responses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of step count and location data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past step count and location information to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting step counts and location information, filtering is performed based on the user's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the walking route and goals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned guide section is It estimates the user's emotions and adjusts the content and tone of the voice guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned guide section is When providing audio guides, the system analyzes past user responses to select the most suitable guiding method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, It estimates the user's emotions and adjusts the parameters of the optimization algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, During optimization, the optimization algorithm is optimized by analyzing the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback unit is When providing feedback, we analyze the user's past responses to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0162] 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 collection unit that collects the user's step count and location information, Based on the data collected by the aforementioned collection unit, the proposal unit suggests the optimal walking course and daily goals. A guide unit provides audio guidance based on the walking courses and goals proposed by the aforementioned proposal unit, An optimization unit that uses real-time GPS tracking and optimization algorithms, It comprises a feedback unit that provides an interactive feedback system using speech recognition. A system characterized by the following features.

2. The aforementioned collection unit is Collects user step count and location information in real time. The system according to feature 1.

3. The aforementioned proposal section is, Based on the collected data, the system analyzes user behavior patterns and suggests optimal walking routes and daily goals. The system according to feature 1.

4. The aforementioned guide section is Based on the suggested walking route and goals, an audio guide will be provided. The system according to feature 1.

5. The optimization unit, Using real-time GPS tracking and optimization algorithms, we provide customized suggestions based on user behavior patterns. The system according to feature 1.

6. The aforementioned feedback unit is It provides an interactive feedback system that utilizes speech recognition and optimizes feedback based on user responses. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of step count and location data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The system analyzes the user's past step count and location information to select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting step counts and location information, filtering is performed based on the user's current health status and activity level. The system according to feature 1.

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