System

Abstract

A system according to an embodiment aims to support people attempting to quit smoking and increase the success rate of quitting smoking.SOLUTION: A system according to an embodiment includes a knowledge providing unit, a progress check unit, an advice unit, and a conversation support unit. The knowledge providing unit provides knowledge of smoking cessation. The progress check unit checks daily progress. The advice unit provides advice and encouragement. The conversation support unit provides support through a conversation when it is difficult.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, people who try to quit smoking often do so alone, making it difficult to continue.

[0005] The system according to the embodiment aims to support people who are trying to quit smoking and increase the success rate of quitting smoking. [Means for solving the problem]

[0006] The system according to the embodiment includes a knowledge providing unit, a progress checking unit, an advice unit, and a conversation support unit. The knowledge providing unit provides knowledge about quitting smoking. The progress checking unit checks daily progress. The advice unit provides advice and encouragement. The conversation support unit supports the user through conversation when they are struggling. [Effects of the Invention]

[0007] Systems according to embodiments can support people trying to quit smoking and increase the success rate of quitting smoking. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The smoking cessation support system according to an embodiment of the present invention provides knowledge about quitting smoking, checks daily progress, gives advice and encouragement, and supports through conversation when people are struggling. This allows people who want to quit smoking to succeed with the support of AI, rather than fighting alone.

[0029] The smoking cessation support system according to the embodiment includes a knowledge provision unit, a progress check unit, an advice unit, and a conversation support unit. The knowledge provision unit provides knowledge about quitting smoking. For example, the generation AI provides the user with basic knowledge and information about quitting smoking. The generation AI explains the health effects of smoking, the benefits of quitting smoking, and specific methods for quitting smoking. When the user launches the app, the generation AI receives prompts to teach the basics of smoking cessation and provides appropriate information based on the prompts. For example, in response to the question, "What are the health benefits of quitting smoking?", the generation AI generates an answer such as, "Quitting smoking improves lung function and reduces the risk of heart disease." The progress check unit checks daily progress. For example, the generation AI has a function to check the user's daily progress in quitting smoking. The user logs in to the app every day and reports their smoking cessation status. The generation AI analyzes the data and evaluates the user's progress. For example, if the user reports, "I didn't smoke today," the generation AI generates an encouraging message such as, "Great! Keep it up!" The advice unit provides advice and encouragement. For example, the generation AI provides appropriate advice and encouragement to the user. If the user reports, "I feel like smoking," the generation AI generates advice such as, "Take a deep breath and calm yourself. Drinking water can also be effective." Furthermore, on the day the user successfully quits smoking, the generation AI sends an encouraging message such as, "Congratulations! You've made a step forward." The conversation support unit provides support through conversation when the user is struggling. For example, the generation AI has a function to support the user through conversation when they feel struggling while quitting smoking. If the user reports, "I really want to smoke right now," the generation AI can begin the conversation by saying, "I understand how you feel. Let's talk a bit." The generation AI listens to the user's feelings, empathizes, and offers words of encouragement to support the user in continuing to quit smoking. In this way, the smoking cessation support system according to the embodiment allows people who want to quit smoking to succeed with AI support, rather than fighting alone.For example, by learning about quitting smoking, checking daily progress, and receiving appropriate advice and encouragement, the success rate of quitting smoking will increase. Also, when you are feeling down, you can calm yourself down by talking to AI. This is expected to help many people live healthier lives.

[0030] The knowledge provider can analyze a user's past smoking history and health status and provide an individually customized smoking cessation plan. For example, the knowledge provider's generation AI collects the user's past smoking history and creates an individual smoking cessation plan based on data such as the age at which they started smoking, the number of cigarettes smoked, and the duration of smoking. For example, for a user who has been smoking for a long time, it can propose a plan to gradually reduce the number of cigarettes smoked. The knowledge provider also analyzes the user's health status and specifically shows the health improvement effects of quitting smoking. For example, it can present the predicted improvement effects after quitting smoking based on the lung function and heart health status. The knowledge provider also takes into account the user's lifestyle habits and stress level to propose the optimal smoking cessation method. For example, it can provide a smoking cessation plan that incorporates relaxation techniques for a user with high stress levels. By providing the optimal smoking cessation plan to the user, the success rate of quitting smoking is improved.

[0031] The knowledge provider can collect the latest medical research and news related to smoking cessation in real time and provide it to users. For example, the generation AI periodically scans the latest medical research databases to notify users of new research findings related to smoking cessation. For example, it can provide research results on the effectiveness of new smoking cessation aids. The knowledge provider can also monitor news sites and blogs related to smoking cessation and deliver important news to users in real time. For example, it can provide information on anti-smoking campaigns and new anti-smoking regulations. The generation AI can also create a customized news feed based on the user's interests to provide the latest information related to smoking cessation. For example, it can provide the latest information on specific smoking cessation methods that interest the user. This allows users to always have the latest smoking cessation knowledge by providing the latest information.

[0032] The knowledge provision unit can also provide knowledge about quitting smoking as video and audio content, promoting learning through visual and audio means. For example, the knowledge provision unit provides basic knowledge about quitting smoking as video content to promote learning in a visually easy-to-understand format. For example, the benefits and methods of quitting smoking are explained using animations. The knowledge provision unit also provides audio content about quitting smoking, allowing users to listen to and learn while commuting or exercising. For example, interviews with people who have successfully quit smoking and advice from experts are distributed in podcast format. The knowledge provision unit also creates multimedia teaching materials that combine video and audio content, allowing users to learn effectively through visual and audio means. For example, it provides a video explaining the steps to quitting smoking and an audio guide summarizing the key points. This promotes learning through visual and audio means, allowing users to effectively acquire knowledge about quitting smoking.

[0033] The knowledge providing unit can cooperate with other health apps to provide smoking cessation knowledge as part of comprehensive health management. For example, the knowledge providing unit cooperates with other health management apps to provide knowledge about quitting smoking as part of comprehensive health management. For example, the knowledge providing unit cooperates with a fitness app to explain the synergistic effects of exercise and quitting smoking. The knowledge providing unit also comprehensively evaluates the user's health status based on data from the health app and specifically explains the benefits of quitting smoking. For example, the knowledge providing unit cooperates with a weight management app to predict weight changes due to quitting smoking. The knowledge providing unit also cooperates with other health apps to centrally manage information about quitting smoking and make it easy for users to access. For example, the knowledge providing unit cooperates with a diet management app to provide dietary advice for quitting smoking. In this way, by coordinating with other health apps, it is possible to provide smoking cessation knowledge as part of comprehensive health management.

[0034] The progress check unit can analyze the user's progress data over the long term and identify patterns and trends for successful smoking cessation. For example, the progress check unit uses a generation AI to collect the user's daily progress data and analyze long-term trends. For example, it identifies common patterns for success based on data from people who have successfully quit smoking. The progress check unit also analyzes the user's progress data and identifies individual trends for successful smoking cessation. For example, if a user tends to feel the urge to smoke during a specific time of day, it will suggest measures to take during that time. The progress check unit also predicts the user's success rate in quitting smoking based on long-term data analysis and provides necessary support. For example, it provides additional advice and encouragement to users whose progress has stagnated. This makes it possible to identify patterns and trends for successful smoking cessation through long-term data analysis and provide appropriate support to users.

[0035] The progress check unit can gamify the progress check, allowing the user to continue quitting smoking while having fun. The progress check unit, for example, gamifies the progress check, allowing the user to continue quitting smoking while having fun. For example, a system is introduced whereby points are earned and rewards are obtained according to the number of days without smoking. The progress check unit also develops a progress check function that incorporates game elements to increase the user's motivation to continue quitting smoking. For example, a ranking of those who have successfully quit smoking is displayed to stimulate a competitive spirit. The progress check unit also gamifies the progress check, allowing the user to continue quitting smoking while having fun. For example, a system is introduced whereby avatars and badges can be earned according to the number of days without smoking. In this way, gamification allows the user to continue quitting smoking while having fun.

[0036] The progress check unit can link with other health data to check overall health progress. The progress check unit, for example, links with other health data (e.g., exercise and diet) to build a system that checks overall health progress. For example, it integrates exercise data and smoking cessation data to perform a comprehensive health assessment. The progress check unit also comprehensively analyzes the health data to evaluate the correlation between smoking cessation progress and other health factors. For example, it can show that an increase in the amount of exercise increases the success rate of quitting smoking. The progress check unit also links with other health data to develop a system that checks the user's overall health progress. For example, it can integrate diet data and smoking cessation data to provide healthy dietary advice. This makes it possible to check overall health progress by linking with other health data.

[0037] The advice unit can analyze the user's past behavioral data and provide the most effective advice and encouragement. For example, the advice unit uses a generation AI to collect the user's past behavioral data and provide effective advice for successful smoking cessation. For example, it provides similar advice based on methods that have been successful in the past. The advice unit also analyzes the user's behavioral data and generates the most effective encouraging message. For example, it reuses messages that have generated positive responses in the past. The advice unit also predicts the user's success rate in quitting smoking based on the behavioral data and provides the necessary support. For example, it provides additional advice and encouragement to users whose progress has stagnated. In this way, by analyzing past behavioral data, it is possible to provide the user with the most appropriate advice and encouragement.

[0038] The advice unit can provide advice appropriate to the timing, taking into account the user's lifestyle rhythm and stress level. For example, the generation AI analyzes the user's lifestyle rhythm and provides advice at the optimal time. For example, it may suggest relaxation techniques during the morning commute. The advice unit also monitors the user's stress level in real time and provides appropriate advice when stress is high. For example, it may suggest deep breathing techniques when stress is high. The advice unit also comprehensively evaluates the user's lifestyle rhythm and stress level and provides the most appropriate advice to the user. For example, it may send a message reaffirming the benefits of quitting smoking during the evening relaxation time. This improves the success rate of quitting smoking by providing advice tailored to the user's lifestyle rhythm and stress level.

[0039] The advice unit can share advice and encouragement with the user's family and friends, allowing the user to receive support from those around them. For example, the advice unit provides a function that enables the generation AI to share the user's advice and encouraging messages with family and friends. For example, on the day the user successfully quits smoking, the advice unit sends a notification to family. The advice unit also shares the user's progress with family and friends, allowing the user to receive support from those around them. For example, the advice unit provides a function that enables family members to send encouraging messages to the user. The advice unit also allows the user to continue quitting smoking while receiving support from those around them by sharing advice and encouraging messages. For example, friends can check the user's progress and send encouraging messages. In this way, by sharing advice and encouragement with family and friends, the user can receive support from those around them and improve the success rate of quitting smoking.

[0040] The advice unit can cooperate with other health apps to provide comprehensive health advice. For example, the advice unit cooperates with other health apps to provide comprehensive health advice. For example, it cooperates with a fitness app to explain the synergistic effects of exercise and quitting smoking. The advice unit also comprehensively evaluates the user's health status based on data from the health app and specifically indicates the benefits of quitting smoking. For example, it cooperates with a weight management app to predict weight changes due to quitting smoking. The advice unit also cooperates with other health apps to centrally manage information about quitting smoking and make it easy for users to access. For example, it cooperates with a diet management app to provide dietary advice for quitting smoking. In this way, by cooperating with other health apps, comprehensive health advice can be provided.

[0041] The conversation support unit can analyze the user's past conversation data and identify the most effective support method. For example, the conversation support unit uses a generation AI to collect the user's past conversation data and identify the most effective support method when the user is struggling to quit smoking. For example, it may reuse words of encouragement that have been effective in the past. The conversation support unit also analyzes the user's conversation data and identifies the most effective support method. For example, it may provide topics that will help the user relax. The conversation support unit also predicts the user's success rate in quitting smoking based on the conversation data and provides the necessary support. For example, it provides additional encouragement and advice to users whose progress has stalled. In this way, the optimal support method for the user can be identified by analyzing past conversation data.

[0042] The conversation support unit can link the conversation support with a voice assistant and a chatbot to achieve more natural dialogue. For example, the conversation support unit links the conversation support with a voice assistant to enable the user to receive support through natural dialogue. For example, the voice assistant answers the user's questions in real time. The conversation support unit also links with a chatbot to enable the user to receive support through text-based dialogue. For example, the chatbot provides advice based on the user's emotional state. The conversation support unit also combines the voice assistant and the chatbot to enable the user to receive support in a manner selected by the user. For example, conversations are conducted using both voice and text. In this way, by linking with the voice assistant or chatbot, more natural dialogue can be achieved and support for the user can be enhanced.

[0043] The conversation support unit can introduce a peer support function with other users to form a community where empathy and encouragement can be shared. The conversation support unit, for example, introduces a peer support function with other users to form a community where empathy and encouragement can be shared. For example, it provides a forum where users who are trying to quit smoking can encourage each other. The conversation support unit also enables users to share their experiences and gain empathy with other non-smokers through the peer support function. For example, they can share success stories and ways of overcoming difficulties. The conversation support unit also enhances the community function to enable users to interact with other non-smokers in real time. For example, it provides support through chat rooms and video calls. In this way, by introducing the peer support function with other users, a community where empathy and encouragement can be shared can be formed, improving the success rate of quitting smoking.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The knowledge provider can analyze a user's past smoking history and health status and provide an individually customized smoking cessation plan. For example, the generation AI collects the user's past smoking history and creates an individual smoking cessation plan based on data such as the age at which they started smoking, the number of cigarettes smoked, and the duration of smoking. For example, for a user who has been smoking for a long time, it can propose a plan to gradually reduce the number of cigarettes smoked. The knowledge provider also analyzes the user's health status and shows specific health improvements that will result from quitting smoking. For example, it can present predicted improvements after quitting smoking based on lung function and cardiac health. The knowledge provider also takes into account the user's lifestyle and stress level to propose the optimal method for quitting smoking. For example, it can provide a smoking cessation plan that incorporates relaxation techniques for a user with high stress levels. By providing the optimal smoking cessation plan for the user, the success rate of quitting smoking is improved.

[0046] The knowledge provider can collect the latest medical research and news related to smoking cessation in real time and provide it to users. For example, the generation AI can regularly scan the latest medical research database and notify users of new research findings related to smoking cessation. For example, it can provide research results on the effectiveness of new smoking cessation aids. The knowledge provider can also monitor news sites and blogs related to smoking cessation and deliver important news to users in real time. For example, it can provide information on anti-smoking campaigns and new anti-smoking regulations. The knowledge provider can also create a customized news feed based on the user's interests and provide the latest information related to smoking cessation. For example, it can provide the latest information on specific smoking cessation methods that interest the user. This allows users to always have the latest smoking cessation knowledge by providing the latest information.

[0047] The knowledge provision unit can also provide knowledge about quitting smoking as video and audio content, promoting learning through visual and auditory means. For example, basic knowledge about quitting smoking can be provided as video content to promote learning in a visually easy-to-understand format. For example, the benefits and methods of quitting smoking can be explained using animations. The knowledge provision unit can also provide audio content about quitting smoking, allowing users to listen to and learn while commuting or exercising. For example, interviews with people who have successfully quit smoking and advice from experts can be distributed in podcast format. The knowledge provision unit can also create multimedia teaching materials that combine video and audio content to enable users to learn effectively through both visual and auditory means. For example, a video explaining the steps to quitting smoking and an audio guide summarizing the key points can be provided. This promotes learning through visual and auditory means, allowing users to effectively acquire knowledge about quitting smoking.

[0048] The knowledge providing unit can work with other health apps to provide smoking cessation knowledge as part of comprehensive health management. For example, data can be linked with other health management apps to provide knowledge about quitting smoking as part of comprehensive health management. For example, it can work with a fitness app to explain the synergistic effects of exercise and quitting smoking. The knowledge providing unit can also use data from the health app to comprehensively evaluate the user's health status and specifically show the benefits of quitting smoking. For example, it can work with a weight management app to predict weight changes due to quitting smoking. The knowledge providing unit can also work with other health apps to centrally manage information about quitting smoking and make it easy for users to access. For example, it can work with a diet management app to provide dietary advice for quitting smoking. In this way, by working with other health apps, it is possible to provide smoking cessation knowledge as part of comprehensive health management.

[0049] The progress check unit can analyze a user's progress data over the long term and identify patterns and trends for successful smoking cessation. For example, the generation AI collects a user's daily progress data and analyzes long-term trends. For example, it can identify common patterns for success based on data from people who have successfully quit smoking. The progress check unit also analyzes the user's progress data and identifies individual trends for successful smoking cessation. For example, if a user tends to have the urge to smoke at a certain time of day, it can suggest measures to take at that time. The progress check unit can also predict a user's success rate in quitting smoking based on long-term data analysis and provide necessary support. For example, it can provide additional advice and encouragement to users whose progress has stagnated. This makes it possible to identify patterns and trends for successful smoking cessation through long-term data analysis and provide appropriate support to users.

[0050] The progress check unit can gamify the progress check, allowing the user to continue quitting smoking while having fun. For example, the progress check can be gamified, allowing the user to continue quitting smoking while having fun. For example, a system can be introduced whereby points can be earned and rewards can be obtained according to the number of days without smoking. The progress check unit can also develop a progress check function that incorporates game elements to increase the user's motivation to continue quitting smoking. For example, a ranking of those who have successfully quit smoking can be displayed to stimulate a competitive spirit. The progress check unit can also gamify the progress check, allowing the user to continue quitting smoking while having fun. For example, a system can be introduced whereby avatars and badges can be earned according to the number of days without smoking. In this way, gamification allows the user to continue quitting smoking while having fun.

[0051] The progress check unit can link with other health data to check overall health progress. For example, a system can be built that links with other health data (e.g., exercise and diet) to check overall health progress. For example, exercise data and smoking cessation data can be integrated to perform a comprehensive health assessment. The progress check unit can also comprehensively analyze the health data to evaluate the correlation between smoking cessation progress and other health factors. For example, it can show that an increase in the amount of exercise increases the success rate of quitting smoking. The progress check unit can also link with other health data to develop a system that checks the user's overall health progress. For example, it can integrate diet data and smoking cessation data to provide healthy dietary advice. This makes it possible to check overall health progress by linking with other health data.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The knowledge provider provides knowledge about quitting smoking. For example, the generation AI provides users with basic knowledge and information about quitting smoking. The generation AI explains the health effects of smoking, the benefits of quitting smoking, and specific methods for quitting smoking. When a user launches the app, the generation AI receives prompts to teach them the basics of quitting smoking and provides appropriate information based on those prompts. For example, in response to the question, "What are the health benefits of quitting smoking?" the generation AI generates an answer such as, "Quitting smoking improves lung function and reduces the risk of heart disease." Step 2: The progress check section checks daily progress. For example, the generation AI has the function of checking the user's daily progress in quitting smoking. The user logs into the app every day and reports their smoking cessation status. The generation AI analyzes this data and evaluates the user's progress. For example, if the user reports that they "didn't smoke today," the generation AI generates an encouraging message such as "Great! Keep it up!" Step 3: The advice section provides advice and encouragement. For example, the generation AI provides appropriate advice and encouragement to the user. If the user reports that they "want to smoke," the generation AI generates advice such as "Take a deep breath and calm yourself. Drinking water can also be effective." In addition, on the day that the user successfully quits smoking, the generation AI sends an encouraging message such as "Congratulations! You've made one step forward." Step 4: The conversation support unit provides support through conversation when the user is struggling. For example, the generation AI has the function of providing support through conversation when the user is struggling to quit smoking. If the user reports, "I really want to smoke right now," the generation AI will begin the conversation by saying, "I understand how you feel. Let's talk a bit." The generation AI will listen to the user's feelings, empathize, and offer words of encouragement to support the user in continuing to quit smoking.

[0054] (Example 2) The smoking cessation support system according to an embodiment of the present invention provides knowledge about quitting smoking, checks daily progress, gives advice and encouragement, and supports through conversation when people are struggling. This allows people who want to quit smoking to succeed with the support of AI, rather than fighting alone.

[0055] The smoking cessation support system according to the embodiment includes a knowledge provision unit, a progress check unit, an advice unit, and a conversation support unit. The knowledge provision unit provides knowledge about quitting smoking. For example, the generation AI provides the user with basic knowledge and information about quitting smoking. The generation AI explains the health effects of smoking, the benefits of quitting smoking, and specific methods for quitting smoking. When the user launches the app, the generation AI receives prompts to teach the basics of smoking cessation and provides appropriate information based on the prompts. For example, in response to the question, "What are the health benefits of quitting smoking?", the generation AI generates an answer such as, "Quitting smoking improves lung function and reduces the risk of heart disease." The progress check unit checks daily progress. For example, the generation AI has a function to check the user's daily progress in quitting smoking. The user logs in to the app every day and reports their smoking cessation status. The generation AI analyzes the data and evaluates the user's progress. For example, if the user reports, "I didn't smoke today," the generation AI generates an encouraging message such as, "Great! Keep it up!" The advice unit provides advice and encouragement. For example, the generation AI provides appropriate advice and encouragement to the user. If the user reports, "I feel like smoking," the generation AI generates advice such as, "Take a deep breath and calm yourself. Drinking water can also be effective." Furthermore, on the day the user successfully quits smoking, the generation AI sends an encouraging message such as, "Congratulations! You've made a step forward." The conversation support unit provides support through conversation when the user is struggling. For example, the generation AI has a function to support the user through conversation when they feel struggling while quitting smoking. If the user reports, "I really want to smoke right now," the generation AI can begin the conversation by saying, "I understand how you feel. Let's talk a bit." The generation AI listens to the user's feelings, empathizes, and offers words of encouragement to support the user in continuing to quit smoking. In this way, the smoking cessation support system according to the embodiment allows people who want to quit smoking to succeed with AI support, rather than fighting alone.For example, by learning about quitting smoking, checking daily progress, and receiving appropriate advice and encouragement, the success rate of quitting smoking will increase. Also, when you are feeling down, you can calm yourself down by talking to AI. This is expected to help many people live healthier lives.

[0056] The knowledge provider can analyze a user's past smoking history and health status and provide an individually customized smoking cessation plan. For example, the knowledge provider's generation AI collects the user's past smoking history and creates an individual smoking cessation plan based on data such as the age at which they started smoking, the number of cigarettes smoked, and the duration of smoking. For example, for a user who has been smoking for a long time, it can propose a plan to gradually reduce the number of cigarettes smoked. The knowledge provider also analyzes the user's health status and specifically shows the health improvement effects of quitting smoking. For example, it can present the predicted improvement effects after quitting smoking based on the lung function and heart health status. The knowledge provider also takes into account the user's lifestyle habits and stress level to propose the optimal smoking cessation method. For example, it can provide a smoking cessation plan that incorporates relaxation techniques for a user with high stress levels. By providing the optimal smoking cessation plan to the user, the success rate of quitting smoking is improved.

[0057] The knowledge provider can collect the latest medical research and news related to smoking cessation in real time and provide it to users. For example, the generation AI periodically scans the latest medical research databases to notify users of new research findings related to smoking cessation. For example, it can provide research results on the effectiveness of new smoking cessation aids. The knowledge provider can also monitor news sites and blogs related to smoking cessation and deliver important news to users in real time. For example, it can provide information on anti-smoking campaigns and new anti-smoking regulations. The generation AI can also create a customized news feed based on the user's interests to provide the latest information related to smoking cessation. For example, it can provide the latest information on specific smoking cessation methods that interest the user. This allows users to always have the latest smoking cessation knowledge by providing the latest information.

[0058] The knowledge providing unit can use the emotion estimation function to analyze how the user feels about quitting smoking and provide knowledge according to those emotions. For example, the knowledge providing unit can use the emotion estimation function to analyze in real time the anxiety and stress the user feels about quitting smoking and provide knowledge according to those emotions. For example, it can provide information on relaxation techniques and stress management to a user with high anxiety. The knowledge providing unit can also provide knowledge to elicit positive emotions based on the user's emotion data. For example, it can introduce the experiences of people who have successfully quit smoking and methods with a high success rate. The knowledge providing unit can also customize and provide the smoking cessation information that the user is most interested in based on the emotion estimation data. For example, it can provide detailed information on smoking cessation aids that the user is interested in. In this way, providing knowledge according to the user's emotions improves the success rate of quitting smoking.

[0059] The knowledge provision unit can also provide knowledge about quitting smoking as video and audio content, promoting learning through visual and audio means. For example, the knowledge provision unit provides basic knowledge about quitting smoking as video content to promote learning in a visually easy-to-understand format. For example, the benefits and methods of quitting smoking are explained using animations. The knowledge provision unit also provides audio content about quitting smoking, allowing users to listen to and learn while commuting or exercising. For example, interviews with people who have successfully quit smoking and advice from experts are distributed in podcast format. The knowledge provision unit also creates multimedia teaching materials that combine video and audio content, allowing users to learn effectively through visual and audio means. For example, it provides a video explaining the steps to quitting smoking and an audio guide summarizing the key points. This promotes learning through visual and audio means, allowing users to effectively acquire knowledge about quitting smoking.

[0060] The knowledge providing unit can cooperate with other health apps to provide smoking cessation knowledge as part of comprehensive health management. For example, the knowledge providing unit cooperates with other health management apps to provide knowledge about quitting smoking as part of comprehensive health management. For example, the knowledge providing unit cooperates with a fitness app to explain the synergistic effects of exercise and quitting smoking. The knowledge providing unit also comprehensively evaluates the user's health status based on data from the health app and specifically explains the benefits of quitting smoking. For example, the knowledge providing unit cooperates with a weight management app to predict weight changes due to quitting smoking. The knowledge providing unit also cooperates with other health apps to centrally manage information about quitting smoking and make it easy for users to access. For example, the knowledge providing unit cooperates with a diet management app to provide dietary advice for quitting smoking. In this way, by coordinating with other health apps, it is possible to provide smoking cessation knowledge as part of comprehensive health management.

[0061] The knowledge providing unit can use the emotion estimation function to monitor the emotions of the user when receiving knowledge in real time and provide content that elicits positive emotions. The knowledge providing unit, for example, uses the emotion estimation function to monitor the emotions of the user when receiving knowledge in real time and provides content that elicits positive emotions. For example, it provides detailed information on topics in which the user has shown interest. The knowledge providing unit also creates interactive content that elicits positive emotions based on the user's emotion data. For example, it provides content that allows the user to learn about quitting smoking in the form of a quiz. The knowledge providing unit also customizes and provides content that elicits the most positive emotions for the user based on the emotion estimation data. For example, it provides content that includes success stories and encouraging messages. In this way, by providing content that corresponds to the user's emotions, positive emotions are elicited and the success rate of quitting smoking is improved.

[0062] The progress check unit can analyze the user's progress data over the long term and identify patterns and trends for successful smoking cessation. For example, the progress check unit uses a generation AI to collect the user's daily progress data and analyze long-term trends. For example, it identifies common patterns for success based on data from people who have successfully quit smoking. The progress check unit also analyzes the user's progress data and identifies individual trends for successful smoking cessation. For example, if a user tends to feel the urge to smoke during a specific time of day, it will suggest measures to take during that time. The progress check unit also predicts the user's success rate in quitting smoking based on long-term data analysis and provides necessary support. For example, it provides additional advice and encouragement to users whose progress has stagnated. This makes it possible to identify patterns and trends for successful smoking cessation through long-term data analysis and provide appropriate support to users.

[0063] The progress check unit can use the emotion estimation function to analyze the emotion of the user when reporting progress and provide feedback according to the emotion. For example, the progress check unit uses the emotion estimation function to analyze the emotion of the user when reporting progress in real time and provide feedback according to the emotion. For example, an encouraging message is sent to a user who is feeling positive. The progress check unit also provides advice according to the emotion of the user when reporting progress based on the user's emotion data. For example, relaxation techniques are suggested for a user who is feeling negative. The progress check unit also analyzes the emotion of the user when reporting progress based on the emotion estimation data and provides support according to the emotion. For example, stress management advice is provided to a user who is highly stressed. In this way, by providing feedback according to the user's emotion, the success rate of quitting smoking is improved.

[0064] The progress check unit can gamify the progress check, allowing the user to continue quitting smoking while having fun. The progress check unit, for example, gamifies the progress check, allowing the user to continue quitting smoking while having fun. For example, a system is introduced whereby points are earned and rewards are obtained according to the number of days without smoking. The progress check unit also develops a progress check function that incorporates game elements to increase the user's motivation to continue quitting smoking. For example, a ranking of those who have successfully quit smoking is displayed to stimulate a competitive spirit. The progress check unit also gamifies the progress check, allowing the user to continue quitting smoking while having fun. For example, a system is introduced whereby avatars and badges can be earned according to the number of days without smoking. In this way, gamification allows the user to continue quitting smoking while having fun.

[0065] The progress check unit can link with other health data to check overall health progress. The progress check unit, for example, links with other health data (e.g., exercise and diet) to build a system that checks overall health progress. For example, it integrates exercise data and smoking cessation data to perform a comprehensive health assessment. The progress check unit also comprehensively analyzes the health data to evaluate the correlation between smoking cessation progress and other health factors. For example, it can show that an increase in the amount of exercise increases the success rate of quitting smoking. The progress check unit also links with other health data to develop a system that checks the user's overall health progress. For example, it can integrate diet data and smoking cessation data to provide healthy dietary advice. This makes it possible to check overall health progress by linking with other health data.

[0066] The progress check unit can use the emotion estimation function to monitor the user's emotions during the progress check in real time and provide interactions to elicit positive emotions. The progress check unit, for example, uses the emotion estimation function to monitor the user's emotions during the progress check in real time and provide interactions to elicit positive emotions. For example, sending an encouraging message when the user is feeling positive emotions. The progress check unit also customizes the interactions during the progress check based on the user's emotion data. For example, suggesting relaxation techniques to users who are feeling negative emotions. The progress check unit also monitors the user's emotions during the progress check in real time based on the emotion estimation data and provides interactions to elicit positive emotions. For example, introducing success stories when the user is feeling positive emotions. In this way, by providing interactions according to the user's emotions, positive emotions are elicited and the success rate of quitting smoking is improved.

[0067] The advice unit can analyze the user's past behavioral data and provide the most effective advice and encouragement. For example, the advice unit uses a generation AI to collect the user's past behavioral data and provide effective advice for successful smoking cessation. For example, it provides similar advice based on methods that have been successful in the past. The advice unit also analyzes the user's behavioral data and generates the most effective encouraging message. For example, it reuses messages that have generated positive responses in the past. The advice unit also predicts the user's success rate in quitting smoking based on the behavioral data and provides the necessary support. For example, it provides additional advice and encouragement to users whose progress has stagnated. In this way, by analyzing past behavioral data, it is possible to provide the user with the most appropriate advice and encouragement.

[0068] The advice unit can provide advice appropriate to the timing, taking into account the user's lifestyle rhythm and stress level. For example, the generation AI analyzes the user's lifestyle rhythm and provides advice at the optimal time. For example, it may suggest relaxation techniques during the morning commute. The advice unit also monitors the user's stress level in real time and provides appropriate advice when stress is high. For example, it may suggest deep breathing techniques when stress is high. The advice unit also comprehensively evaluates the user's lifestyle rhythm and stress level and provides the most appropriate advice to the user. For example, it may send a message reaffirming the benefits of quitting smoking during the evening relaxation time. This improves the success rate of quitting smoking by providing advice tailored to the user's lifestyle rhythm and stress level.

[0069] The advice unit can use the emotion estimation function to provide advice and encouragement according to the user's emotional state. For example, the advice unit uses the emotion estimation function to analyze the user's emotional state in real time and provide advice according to that emotion. For example, when the user is feeling anxious, the advice unit suggests relaxation techniques. The advice unit also generates optimal encouraging messages based on the user's emotional data. For example, it sends a message such as "Great! Keep it up" to a user who is feeling positive. The advice unit also provides advice and encouragement according to the user's emotional state based on the emotion estimation data. For example, it provides stress management advice to a user who is highly stressed. In this way, by providing advice and encouragement according to the user's emotional state, the success rate of quitting smoking is improved.

[0070] The advice unit can share advice and encouragement with the user's family and friends, allowing the user to receive support from those around them. For example, the advice unit provides a function that enables the generation AI to share the user's advice and encouraging messages with family and friends. For example, on the day the user successfully quits smoking, the advice unit sends a notification to family. The advice unit also shares the user's progress with family and friends, allowing the user to receive support from those around them. For example, the advice unit provides a function that enables family members to send encouraging messages to the user. The advice unit also allows the user to continue quitting smoking while receiving support from those around them by sharing advice and encouraging messages. For example, friends can check the user's progress and send encouraging messages. In this way, by sharing advice and encouragement with family and friends, the user can receive support from those around them and improve the success rate of quitting smoking.

[0071] The advice unit can cooperate with other health apps to provide comprehensive health advice. For example, the advice unit cooperates with other health apps to provide comprehensive health advice. For example, it cooperates with a fitness app to explain the synergistic effects of exercise and quitting smoking. The advice unit also comprehensively evaluates the user's health status based on data from the health app and specifically indicates the benefits of quitting smoking. For example, it cooperates with a weight management app to predict weight changes due to quitting smoking. The advice unit also cooperates with other health apps to centrally manage information about quitting smoking and make it easy for users to access. For example, it cooperates with a diet management app to provide dietary advice for quitting smoking. In this way, by cooperating with other health apps, comprehensive health advice can be provided.

[0072] The conversation support unit can analyze the user's past conversation data and identify the most effective support method. For example, the conversation support unit uses a generation AI to collect the user's past conversation data and identify the most effective support method when the user is struggling to quit smoking. For example, it may reuse words of encouragement that have been effective in the past. The conversation support unit also analyzes the user's conversation data and identifies the most effective support method. For example, it may provide topics that will help the user relax. The conversation support unit also predicts the user's success rate in quitting smoking based on the conversation data and provides the necessary support. For example, it provides additional encouragement and advice to users whose progress has stalled. In this way, the optimal support method for the user can be identified by analyzing past conversation data.

[0073] The conversation support unit can monitor the user's stress level and emotional state in real time and start a conversation at the appropriate time. For example, the generation AI in the conversation support unit monitors the user's stress level and emotional state in real time and starts a conversation at the appropriate time. For example, it suggests relaxation methods when stress is high. The conversation support unit also starts a conversation at the optimal time based on the user's emotional data. For example, it offers words of encouragement when the user is feeling anxious. The conversation support unit also comprehensively evaluates the stress level and emotional state and provides the user with optimal support. For example, it starts a conversation to reaffirm the benefits of quitting smoking during an evening relaxation session. This makes it possible to provide effective support by starting a conversation at the appropriate time depending on the user's stress level and emotional state.

[0074] The conversation support unit can use the emotion estimation function to provide empathy and words of encouragement that correspond to the user's emotions. For example, the conversation support unit uses the emotion estimation function to analyze the user's emotional state in real time and provide empathy and words of encouragement that correspond to those emotions. For example, when a user is feeling anxious, the conversation support unit can say empathetic words such as, "I understand how you feel." The conversation support unit also generates optimal encouraging messages based on the user's emotional data. For example, a user who is feeling positive can receive a message such as, "Great! Keep it up!" The conversation support unit also provides empathy and words of encouragement that correspond to the user's emotional state based on the emotion estimation data. For example, a user who is highly stressed can receive advice such as, "Relax and take a deep breath." In this way, providing empathy and words of encouragement that correspond to the user's emotions improves the success rate of quitting smoking.

[0075] The conversation support unit can link the conversation support with a voice assistant and a chatbot to achieve more natural dialogue. For example, the conversation support unit links the conversation support with a voice assistant to enable the user to receive support through natural dialogue. For example, the voice assistant answers the user's questions in real time. The conversation support unit also links with a chatbot to enable the user to receive support through text-based dialogue. For example, the chatbot provides advice based on the user's emotional state. The conversation support unit also combines the voice assistant and the chatbot to enable the user to receive support in a manner selected by the user. For example, conversations are conducted using both voice and text. In this way, by linking with the voice assistant or chatbot, more natural dialogue can be achieved and support for the user can be enhanced.

[0076] The conversation support unit can introduce a peer support function with other users to form a community where empathy and encouragement can be shared. The conversation support unit, for example, introduces a peer support function with other users to form a community where empathy and encouragement can be shared. For example, it provides a forum where users who are trying to quit smoking can encourage each other. The conversation support unit also enables users to share their experiences and gain empathy with other non-smokers through the peer support function. For example, they can share success stories and ways of overcoming difficulties. The conversation support unit also enhances the community function to enable users to interact with other non-smokers in real time. For example, it provides support through chat rooms and video calls. In this way, by introducing the peer support function with other users, a community where empathy and encouragement can be shared can be formed, improving the success rate of quitting smoking.

[0077] The conversation support unit can use the emotion estimation function to monitor the user's emotions during a conversation in real time and provide optimal support. The conversation support unit, for example, uses the emotion estimation function to monitor the user's emotions during a conversation in real time and provide optimal support. For example, it can suggest relaxation techniques when the user is feeling anxious. The conversation support unit also adjusts the support content during a conversation based on the user's emotion data. For example, it can offer words of empathy to a user who is feeling negative. The conversation support unit also monitors the user's emotions during a conversation in real time based on the emotion estimation data and provides optimal support. For example, it can introduce a success story when the user is feeling positive. In this way, optimal support can be provided by monitoring the user's emotions during a conversation in real time.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The knowledge provider can analyze a user's past smoking history and health status and provide an individually customized smoking cessation plan. For example, the generation AI collects the user's past smoking history and creates an individual smoking cessation plan based on data such as the age at which they started smoking, the number of cigarettes smoked, and the duration of smoking. For example, for a user who has been smoking for a long time, it can propose a plan to gradually reduce the number of cigarettes smoked. The knowledge provider also analyzes the user's health status and shows specific health improvements that will result from quitting smoking. For example, it can present predicted improvements after quitting smoking based on lung function and cardiac health. The knowledge provider also takes into account the user's lifestyle and stress level to propose the optimal method for quitting smoking. For example, it can provide a smoking cessation plan that incorporates relaxation techniques for a user with high stress levels. By providing the optimal smoking cessation plan for the user, the success rate of quitting smoking is improved.

[0080] The knowledge provider can collect the latest medical research and news related to smoking cessation in real time and provide it to users. For example, the generation AI can regularly scan the latest medical research database and notify users of new research findings related to smoking cessation. For example, it can provide research results on the effectiveness of new smoking cessation aids. The knowledge provider can also monitor news sites and blogs related to smoking cessation and deliver important news to users in real time. For example, it can provide information on anti-smoking campaigns and new anti-smoking regulations. The knowledge provider can also create a customized news feed based on the user's interests and provide the latest information related to smoking cessation. For example, it can provide the latest information on specific smoking cessation methods that interest the user. This allows users to always have the latest smoking cessation knowledge by providing the latest information.

[0081] The knowledge provision unit can use the emotion estimation function to analyze how the user feels about quitting smoking and provide knowledge according to those emotions. For example, the emotion estimation function can be used to analyze in real time the anxiety and stress the user feels about quitting smoking and provide knowledge according to those emotions. For example, a user with high anxiety can be provided with information on relaxation techniques and stress management. The knowledge provision unit also provides knowledge to elicit positive emotions based on the user's emotion data. For example, it can introduce the experiences of people who have successfully quit smoking and methods with a high success rate. The knowledge provision unit also customizes and provides the smoking cessation information that the user is most interested in based on the emotion estimation data. For example, it can provide detailed information on smoking cessation aids that the user is interested in. In this way, providing knowledge according to the user's emotions improves the success rate of quitting smoking.

[0082] The knowledge provision unit can also provide knowledge about quitting smoking as video and audio content, promoting learning through visual and auditory means. For example, basic knowledge about quitting smoking can be provided as video content to promote learning in a visually easy-to-understand format. For example, the benefits and methods of quitting smoking can be explained using animations. The knowledge provision unit can also provide audio content about quitting smoking, allowing users to listen to and learn while commuting or exercising. For example, interviews with people who have successfully quit smoking and advice from experts can be distributed in podcast format. The knowledge provision unit can also create multimedia teaching materials that combine video and audio content to enable users to learn effectively through both visual and auditory means. For example, a video explaining the steps to quitting smoking and an audio guide summarizing the key points can be provided. This promotes learning through visual and auditory means, allowing users to effectively acquire knowledge about quitting smoking.

[0083] The knowledge providing unit can work with other health apps to provide smoking cessation knowledge as part of comprehensive health management. For example, data can be linked with other health management apps to provide knowledge about quitting smoking as part of comprehensive health management. For example, it can work with a fitness app to explain the synergistic effects of exercise and quitting smoking. The knowledge providing unit can also use data from the health app to comprehensively evaluate the user's health status and specifically show the benefits of quitting smoking. For example, it can work with a weight management app to predict weight changes due to quitting smoking. The knowledge providing unit can also work with other health apps to centrally manage information about quitting smoking and make it easy for users to access. For example, it can work with a diet management app to provide dietary advice for quitting smoking. In this way, by working with other health apps, it is possible to provide smoking cessation knowledge as part of comprehensive health management.

[0084] The knowledge providing unit can use the emotion estimation function to monitor the emotions of the user when receiving knowledge in real time and provide content that elicits positive emotions. For example, the emotion estimation function can be used to monitor the emotions of the user when receiving knowledge in real time and provide content that elicits positive emotions. For example, detailed information on topics in which the user has shown interest can be provided. The knowledge providing unit also creates interactive content that elicits positive emotions based on the user's emotion data. For example, it can provide content that allows users to learn about quitting smoking in the form of a quiz. The knowledge providing unit also customizes and provides content that elicits the most positive emotions for the user based on the emotion estimation data. For example, it can provide content that includes success stories and encouraging messages. In this way, providing content that matches the user's emotions elicits positive emotions and improves the success rate of quitting smoking.

[0085] The progress check unit can analyze a user's progress data over the long term and identify patterns and trends for successful smoking cessation. For example, the generation AI collects a user's daily progress data and analyzes long-term trends. For example, it can identify common patterns for success based on data from people who have successfully quit smoking. The progress check unit also analyzes the user's progress data and identifies individual trends for successful smoking cessation. For example, if a user tends to have the urge to smoke at a certain time of day, it can suggest measures to take at that time. The progress check unit can also predict a user's success rate in quitting smoking based on long-term data analysis and provide necessary support. For example, it can provide additional advice and encouragement to users whose progress has stagnated. This makes it possible to identify patterns and trends for successful smoking cessation through long-term data analysis and provide appropriate support to users.

[0086] The progress check unit can use the emotion estimation function to analyze the emotion of the user when reporting progress and provide feedback according to that emotion. For example, the emotion estimation function can be used to analyze the emotion of the user when reporting progress in real time and provide feedback according to that emotion. For example, an encouraging message can be sent to a user who is feeling positive. The progress check unit can also provide advice according to the emotion of the user when reporting progress based on the user's emotion data. For example, a relaxation method can be suggested for a user who is feeling negative. The progress check unit can also analyze the emotion of the user when reporting progress based on the emotion estimation data and provide support according to that emotion. For example, stress management advice can be provided to a user who is highly stressed. In this way, providing feedback according to the user's emotion improves the success rate of quitting smoking.

[0087] The progress check unit can gamify the progress check, allowing the user to continue quitting smoking while having fun. For example, the progress check can be gamified, allowing the user to continue quitting smoking while having fun. For example, a system can be introduced whereby points can be earned and rewards can be obtained according to the number of days without smoking. The progress check unit can also develop a progress check function that incorporates game elements to increase the user's motivation to continue quitting smoking. For example, a ranking of those who have successfully quit smoking can be displayed to stimulate a competitive spirit. The progress check unit can also gamify the progress check, allowing the user to continue quitting smoking while having fun. For example, a system can be introduced whereby avatars and badges can be earned according to the number of days without smoking. In this way, gamification allows the user to continue quitting smoking while having fun.

[0088] The progress check unit can link with other health data to check overall health progress. For example, a system can be built that links with other health data (e.g., exercise and diet) to check overall health progress. For example, exercise data and smoking cessation data can be integrated to perform a comprehensive health assessment. The progress check unit can also comprehensively analyze the health data to evaluate the correlation between smoking cessation progress and other health factors. For example, it can show that an increase in the amount of exercise increases the success rate of quitting smoking. The progress check unit can also link with other health data to develop a system that checks the user's overall health progress. For example, it can integrate diet data and smoking cessation data to provide healthy dietary advice. This makes it possible to check overall health progress by linking with other health data.

[0089] The progress check unit can use the emotion estimation function to monitor the user's emotions during the progress check in real time and provide interactions to elicit positive emotions. For example, the emotion estimation function can be used to monitor the user's emotions during the progress check in real time and provide interactions to elicit positive emotions. For example, an encouraging message can be sent when the user is feeling positive. The progress check unit also customizes the interactions during the progress check based on the user's emotion data. For example, it can suggest relaxation techniques to users who are feeling negative. The progress check unit also monitors the user's emotions during the progress check in real time based on the emotion estimation data and provides interactions to elicit positive emotions. For example, it can introduce success stories when the user is feeling positive. In this way, by providing interactions that correspond to the user's emotions, positive emotions can be elicited and the success rate of quitting smoking can be improved.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The knowledge provider provides knowledge about quitting smoking. For example, the generation AI provides users with basic knowledge and information about quitting smoking. The generation AI explains the health effects of smoking, the benefits of quitting smoking, and specific methods for quitting smoking. When a user launches the app, the generation AI receives prompts to teach them the basics of quitting smoking and provides appropriate information based on those prompts. For example, in response to the question, "What are the health benefits of quitting smoking?" the generation AI generates an answer such as, "Quitting smoking improves lung function and reduces the risk of heart disease." Step 2: The progress check section checks daily progress. For example, the generation AI has the function of checking the user's daily progress in quitting smoking. The user logs into the app every day and reports their smoking cessation status. The generation AI analyzes this data and evaluates the user's progress. For example, if the user reports that they "didn't smoke today," the generation AI generates an encouraging message such as "Great! Keep it up!" Step 3: The advice section provides advice and encouragement. For example, the generation AI provides appropriate advice and encouragement to the user. If the user reports that they "want to smoke," the generation AI generates advice such as "Take a deep breath and calm yourself. Drinking water can also be effective." In addition, on the day that the user successfully quits smoking, the generation AI sends an encouraging message such as "Congratulations! You've made one step forward." Step 4: The conversation support unit provides support through conversation when the user is struggling. For example, the generation AI has the function of providing support through conversation when the user is struggling to quit smoking. If the user reports, "I really want to smoke right now," the generation AI will begin the conversation by saying, "I understand how you feel. Let's talk a bit." The generation AI will listen to the user's feelings, empathize, and offer words of encouragement to support the user in continuing to quit smoking.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The information section provides information on quitting smoking, A progress check department that checks daily progress, an advising department that provides advice and encouragement; A conversation support unit that provides support through conversation when the user is in distress. A system characterized by:

2. The progress check unit Analyze user progress data over time to identify patterns and trends for successful quitting 2. The system of claim 1.

3. The advice unit Analyze users' past behavior data to provide the most effective advice and encouragement 2. The system of claim 1.

4. The conversation support unit Analyze users' past conversation data to identify the most effective ways to support them 2. The system of claim 1.

5. The knowledge providing unit Analyzes how users feel about quitting smoking and provides information based on those feelings 2. The system of claim 1.

6. The progress check unit Analyze the user's emotions when reporting progress and provide feedback according to those emotions 2. The system of claim 1.

7. The advice unit Providing the advice and encouragement according to the user's emotional state 2. The system of claim 1.

8. The conversation support unit Providing empathy and words of encouragement according to the user's emotions 2. The system of claim 1.

Images