System

Abstract

An object of the system according to the embodiment is to analyze elements of success in quitting smoking and provide support tailored to the user.SOLUTION: The system includes a generating AI, a smoking cessation success factor analyzer, a support provider, a visualizer, and a sharer. The generated AI analyzes the user's smoking cessation success component. The smoking cessation success factor analyzer analyzes the user's smoking cessation success factor according to the creation AI. The support providing unit provides support tailored to the user based on the element analyzed by the smoking cessation success element analysis unit. The visualizer visualizes the user's motivation or benefit to quit smoking based on the generated AI. The sharer shares the user's smoking cessation progress with other users through the generated AI.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] Conventional technologies do not adequately analyze the factors that contribute to successful smoking cessation and provide support tailored to each user, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the factors that lead to success in quitting smoking and provide support tailored to the user. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a quit smoking success factor analysis unit, a support provision unit, a visualization unit, and a sharing unit. The generation AI analyzes the user's quit smoking success factors. The quit smoking success factor analysis unit analyzes the user's quit smoking success factors using the generation AI. The support provision unit provides support tailored to the user based on the factors analyzed by the quit smoking success factor analysis unit. The visualization unit visualizes the user's motivations or benefits for quitting smoking using the generation AI. The sharing unit shares the user's progress in quitting smoking with other users using the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can analyze factors that contribute to successful smoking cessation and provide support tailored to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The smoking cessation support application according to an embodiment of the present invention is a system in which a generative AI analyzes factors that contribute to a user's success in quitting smoking and provides optimal support to each individual. As a result, the smoking cessation support application visualizes the user's motivations and benefits for quitting smoking, and enables users to share their progress in quitting smoking, enabling them to support each other and share their success stories.

[0029] A smoking cessation support application according to an embodiment includes a generation AI, a quitting smoking success factor analysis unit, a support providing unit, a visualization unit, and a sharing unit. The generation AI collects and analyzes data related to the user's smoking cessation and identifies factors that contribute to successful quitting smoking. For example, the generation AI analyzes the user's past quitting smoking attempts, current lifestyle habits, stress levels, etc., to determine which factors contribute to successful quitting smoking. The quitting smoking success factor analysis unit uses the generation AI to analyze the user's factors that contribute to successful quitting smoking. For example, the quitting smoking success factor analysis unit analyzes the user's data related to quitting smoking and identifies factors that contribute to successful quitting smoking. The support providing unit provides customized support to the user based on the factors analyzed by the quitting smoking success factor analysis unit. For example, if the user is prone to stress, the support providing unit provides advice on relaxation techniques and stress management. The visualization unit uses the generation AI to visualize the user's motivations and benefits for quitting smoking. For example, the visualization unit displays health improvements and economic benefits of quitting smoking in graphs and charts to motivate the user. The sharing unit uses the generation AI to share the user's smoking cessation progress with other users. For example, the sharing unit can record the user's progress in quitting smoking within the app and share it with other users, thereby enabling the smoking cessation support application to effectively support the user in quitting smoking.

[0030] The quitting smoking success factor analysis unit can analyze lifestyle rhythms or eating patterns to identify factors that contribute to successful quitting smoking. The quitting smoking success factor analysis unit, for example, analyzes the user's lifestyle rhythms to identify factors that contribute to successful quitting smoking. For example, it analyzes how a regular lifestyle rhythm affects successful quitting smoking. The quitting smoking success factor analysis unit also analyzes the user's eating patterns to identify factors that contribute to successful quitting smoking. For example, it analyzes whether a specific diet helps reduce stress while quitting smoking. The quitting smoking success factor analysis unit also performs an integrated analysis of the user's lifestyle rhythms and eating patterns to identify factors that contribute to successful quitting smoking. For example, it analyzes how eating a good breakfast affects successful quitting smoking. This makes it possible to analyze the impact of lifestyle rhythms and eating patterns on successful quitting smoking.

[0031] The quitting smoking success factor analysis unit can analyze social media activity and evaluate the social influence on quitting smoking. For example, the quitting smoking success factor analysis unit analyzes a user's social media activity to evaluate the social influence on quitting smoking. For example, it analyzes the number of posts and comments about quitting smoking to clarify how social support affects successful quitting smoking. The quitting smoking success factor analysis unit also analyzes a user's interactions on social media to evaluate the social influence on quitting smoking. For example, it analyzes the type of reaction a user who declares they are quitting smoking receives from other users. The quitting smoking success factor analysis unit also analyzes the relationship between a user's social media activity and successful quitting smoking to evaluate social influence. For example, it analyzes the type of support a user who successfully quit smoking received on social media. This makes it possible to evaluate the social influence of social media activity on quitting smoking.

[0032] The quitting smoking success factor analysis unit can collect exercise habit or fitness data and analyze physical factors related to successful quitting smoking. The quitting smoking success factor analysis unit, for example, analyzes the user's exercise habit and identifies physical factors related to successful quitting smoking. For example, it analyzes how regular exercise affects successful quitting smoking. The quitting smoking success factor analysis unit also collects fitness data and identifies physical factors related to successful quitting smoking. For example, it analyzes how heart rate and calorie consumption affect successful quitting smoking. The quitting smoking success factor analysis unit also analyzes the user's exercise habit and fitness data in an integrated manner and identifies physical factors related to successful quitting smoking. For example, it analyzes how the relaxation effect after exercise affects successful quitting smoking. This makes it possible to analyze the impact of exercise habit and fitness data on successful quitting smoking.

[0033] The quitting smoking success factor analysis unit can analyze sleep patterns and identify factors that contribute to successful quitting smoking. The quitting smoking success factor analysis unit, for example, analyzes the user's sleep patterns and identifies factors that contribute to successful quitting smoking. For example, it analyzes how sufficient sleep affects successful quitting smoking. The quitting smoking success factor analysis unit also analyzes sleep quality and identifies factors that contribute to successful quitting smoking. For example, it analyzes whether deep sleep helps reduce stress while quitting smoking. The quitting smoking success factor analysis unit also analyzes the relationship between the user's sleep patterns and successful quitting smoking and identifies factors that contribute to successful quitting smoking. For example, it analyzes how staying up late affects successful quitting smoking. This makes it possible to analyze the impact of sleep patterns on successful quitting smoking.

[0034] The support providing unit can analyze the user's past smoking cessation attempt data and compare successful and unsuccessful methods to provide optimal support. The support providing unit, for example, analyzes the user's past smoking cessation attempt data and compares successful and unsuccessful methods. For example, provides optimal support based on the successful methods. The support providing unit can also analyze the user's past smoking cessation attempt data and identify successful and unsuccessful methods. For example, provides customized advice based on the successful methods. The support providing unit can also analyze the user's past smoking cessation attempt data and compare successful and unsuccessful methods to provide optimal support. For example, provides advice to avoid unsuccessful methods. In this way, optimal support can be provided based on the past smoking cessation attempt data.

[0035] The support providing unit can analyze the user's health data and provide smoking cessation support based on the health condition. The support providing unit analyzes health data such as the user's heart rate and blood pressure, and provides smoking cessation support based on the health condition. For example, it suggests relaxation methods when the heart rate is high. The support providing unit also customizes the user's smoking cessation support based on the health data. For example, it provides advice on reducing stress when blood pressure is high. The support providing unit also analyzes the user's health data and provides smoking cessation support based on the health condition. For example, it provides appropriate support based on fluctuations in heart rate and blood pressure. This makes it possible to provide smoking cessation support based on health data.

[0036] The support providing unit can suggest activities that can be enjoyed while quitting smoking based on the user's hobbies and interests. The support providing unit, for example, analyzes the user's hobbies and interests and suggests activities that can be enjoyed while quitting smoking. For example, it suggests activities such as reading or watching movies. The support providing unit also customizes activities that can be enjoyed while quitting smoking based on the user's hobbies and interests. For example, it suggests sports or outdoor activities. The support providing unit also suggests activities that can be enjoyed while quitting smoking based on the user's hobbies and interests. For example, it suggests arts and crafts activities. In this way, it is possible to suggest activities that can be enjoyed while quitting smoking based on the user's hobbies and interests.

[0037] The support providing unit can analyze the user's eating patterns and provide a meal plan suitable for smoking cessation. The support providing unit, for example, analyzes the user's eating patterns and provides a meal plan suitable for smoking cessation. For example, it suggests meals that reduce stress while quitting smoking. The support providing unit also customizes a meal plan suitable for smoking cessation based on the eating patterns. For example, it suggests nutritionally balanced meals. The support providing unit also analyzes the user's eating patterns and provides a meal plan suitable for smoking cessation. For example, it suggests meals that suppress appetite while quitting smoking. In this way, a meal plan suitable for smoking cessation can be provided based on the eating patterns.

[0038] The visualization unit can visualize the specific health improvement effects of quitting smoking based on the user's health data. The visualization unit visualizes the specific health improvement effects of quitting smoking based on the user's health data, for example. For example, it displays changes in heart rate and blood pressure after quitting smoking in a graph. The visualization unit also analyzes the health data and visualizes the health improvement effects of quitting smoking. For example, it displays improvements in lung function after quitting smoking in a chart. The visualization unit also visualizes the specific health improvement effects of quitting smoking based on the user's health data. For example, it visually displays changes in weight and improvements in energy level after quitting smoking. In this way, the specific health improvement effects of quitting smoking can be visualized based on the health data.

[0039] The visualization unit can analyze the user's economic data and display the economic benefits of quitting smoking in detail. The visualization unit, for example, analyzes the user's economic data and displays the economic benefits of quitting smoking in detail. For example, it displays the amount of money that can be saved by quitting smoking by month in a graph. The visualization unit also visualizes the economic benefits of quitting smoking based on the economic data. For example, it displays the total amount of money that can be saved per year by quitting smoking in a chart. The visualization unit also analyzes the user's economic data and displays the economic benefits of quitting smoking in detail. For example, it displays the amount of money that can be saved by quitting smoking in comparison with other expenditure items. In this way, the economic benefits of quitting smoking can be displayed in detail based on the economic data.

[0040] The visualization unit can visualize the support from the user's family and friends and emphasize the importance of social support. The visualization unit, for example, visualizes the support from the user's family and friends and emphasizes the importance of social support. For example, it displays encouraging messages from family and friends. The visualization unit also visualizes the support from family and friends and emphasizes the importance of social support for successfully quitting smoking. For example, it displays video messages from family and friends encouraging the user to quit smoking. The visualization unit also visualizes the support from the user's family and friends and emphasizes the importance of social support. For example, it displays comments from family and friends encouraging the user to quit smoking. In this way, the support from family and friends can be visualized and the importance of social support can be emphasized.

[0041] The visualization unit visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. The visualization unit, for example, visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. For example, the visualization unit displays the number of days a user has quit smoking and the successful experiences achieved in the past in a graph. The visualization unit also visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. For example, the visualization unit visually displays the feelings and experiences experienced when a user successfully quits smoking. The visualization unit also visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. For example, the user can share their past successful experiences of quitting smoking with other users and encourage each other. This makes it possible to visualize the user's past successful experiences, thereby increasing the user's sense of self-efficacy.

[0042] The sharing unit can record the user's progress in quitting smoking in detail and provide individualized advice based on the progress data. The sharing unit, for example, can record the user's progress in quitting smoking in detail and provide individualized advice based on the progress data. For example, it can provide advice based on the number of days since quitting smoking or successful experiences. The sharing unit can also analyze the quitting smoking progress data and provide individualized advice. For example, it can suggest relaxation methods based on the progress in quitting smoking. The sharing unit can also record the user's progress in quitting smoking in detail and provide individualized advice based on the progress data. For example, it can provide advice on stress management based on the progress in quitting smoking. This makes it possible to record the user's progress in quitting smoking in detail and provide individualized advice.

[0043] The sharing unit can provide interactive graphs and charts to visualize the user's progress in quitting smoking and enhance the sense of accomplishment. The sharing unit, for example, provides interactive graphs to visualize the user's progress in quitting smoking and enhance the sense of accomplishment. For example, it may display the number of days since quitting smoking or success stories in a graph. The sharing unit also provides interactive charts to visualize the progress in quitting smoking and enhance the sense of accomplishment. For example, it may display a chart showing the health improvements achieved by quitting smoking. The sharing unit also provides interactive graphs and charts to visualize the user's progress in quitting smoking and enhance the sense of accomplishment. For example, it may visually display the economic benefits of quitting smoking. In this way, it is possible to provide interactive graphs and charts to visualize the progress in quitting smoking and enhance the sense of accomplishment.

[0044] The sharing unit allows the user to share their progress in quitting smoking on social media and receive widespread support. The sharing unit, for example, shares the user's progress in quitting smoking on social media and receives widespread support. For example, the number of days since quitting smoking and success stories are posted on SNS. The sharing unit also shares the user's progress in quitting smoking through social media and receives encouragement and support from other users. For example, the user's feelings and experiences when they successfully quit smoking are shared on SNS. The sharing unit also shares the user's progress in quitting smoking on social media and receives widespread support. For example, the user's health improvements and financial benefits from quitting smoking are posted on SNS. In this way, the user can share their progress in quitting smoking on social media and receive widespread support.

[0045] The sharing unit can introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. The sharing unit can, for example, introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. For example, it can provide a ranking system in which users compete based on the number of days without smoking or successful experiences. The sharing unit can also introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. For example, it can provide rewards or badges for users who successfully quit smoking. The sharing unit can also introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. For example, it can provide a challenge between users who have successfully quit smoking. This can introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation.

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

[0047] Smoking cessation support applications can also analyze a user's exercise habits and identify physical factors related to successful quitting smoking. For example, they can analyze how regular exercise affects successful quitting smoking. They can also collect fitness data and analyze how heart rate and calorie consumption affect successful quitting smoking. They can also analyze how the relaxation effect after exercise affects successful quitting smoking. This allows for a detailed analysis of the impact of exercise habits and fitness data on successful quitting smoking.

[0048] The smoking cessation support application can further analyze the user's sleep patterns to identify factors that contribute to successful quitting smoking. For example, it can analyze how getting enough sleep affects successful quitting smoking. It can also analyze sleep quality to determine whether deep sleep helps reduce stress while quitting smoking. It can also analyze how staying up late affects successful quitting smoking. This allows for a detailed analysis of the impact that sleep patterns have on successful quitting smoking.

[0049] Smoking cessation support applications can also analyze users' social media activity to evaluate the social influence on quitting smoking. For example, analyzing the number of posts and comments about quitting smoking can clarify how social support affects successful quitting. It is also possible to analyze users' interactions on social media to evaluate the social influence on quitting smoking. It is also possible to analyze the type of support users who successfully quit smoking received on social media. This allows for a detailed evaluation of the social influence of social media activity on quitting smoking.

[0050] Smoking cessation support applications can also analyze the user's dietary patterns to identify factors that contribute to successful smoking cessation. For example, they can analyze whether a particular diet helps reduce stress while quitting smoking. They can also perform an integrated analysis of the user's lifestyle and dietary patterns to identify factors that contribute to successful smoking cessation. They can also analyze how eating a good breakfast affects successful smoking cessation. This allows for a detailed analysis of the impact that lifestyle and dietary patterns have on successful smoking cessation.

[0051] The smoking cessation support application can further analyze the user's health data and provide smoking cessation support based on the user's health condition. For example, it can analyze health data such as heart rate and blood pressure and provide smoking cessation support based on the user's health condition. For example, it can suggest relaxation methods when the heart rate is high. It can also provide advice on stress reduction when blood pressure is high. It can also provide appropriate support based on fluctuations in heart rate and blood pressure. This makes it possible to provide smoking cessation support based on health data.

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

[0053] Step 1: The generating AI collects and analyzes data on the user's smoking cessation and identifies factors that contribute to successful smoking cessation. For example, the generating AI analyzes the user's past attempts to quit smoking, current lifestyle habits, stress levels, etc., to identify factors that contribute to successful smoking cessation. Step 2: The quit smoking success factor analysis unit uses the generation AI to analyze the factors that contribute to the user's success in quitting smoking. For example, the quit smoking success factor analysis unit analyzes data related to the user's quitting smoking and identifies the factors that contribute to successful quitting smoking. Step 3: The support provider provides support tailored to the user based on the factors analyzed by the smoking cessation success factor analyzer. For example, if the user is prone to stress, the support provider provides advice on relaxation techniques and stress management. Step 4: The visualization unit uses generative AI to visualize the user's motivations and benefits for quitting smoking. For example, the visualization unit may display the health improvements and economic benefits of quitting smoking in graphs and charts, motivating the user to quit smoking. Step 5: The sharing unit shares the user's progress in quitting smoking with other users using the generation AI. For example, the sharing unit can record the user's progress in quitting smoking within the app and share it with other users.

[0054] (Example 2) The smoking cessation support application according to an embodiment of the present invention is a system in which a generative AI analyzes factors that contribute to a user's success in quitting smoking and provides optimal support to each individual. As a result, the smoking cessation support application visualizes the user's motivations and benefits for quitting smoking, and enables users to share their progress in quitting smoking, enabling them to support each other and share their success stories.

[0055] A smoking cessation support application according to an embodiment includes a generation AI, a quitting smoking success factor analysis unit, a support providing unit, a visualization unit, and a sharing unit. The generation AI collects and analyzes data related to the user's smoking cessation and identifies factors that contribute to successful quitting smoking. For example, the generation AI analyzes the user's past quitting smoking attempts, current lifestyle habits, stress levels, etc., to determine which factors contribute to successful quitting smoking. The quitting smoking success factor analysis unit uses the generation AI to analyze the user's factors that contribute to successful quitting smoking. For example, the quitting smoking success factor analysis unit analyzes the user's data related to quitting smoking and identifies factors that contribute to successful quitting smoking. The support providing unit provides customized support to the user based on the factors analyzed by the quitting smoking success factor analysis unit. For example, if the user is prone to stress, the support providing unit provides advice on relaxation techniques and stress management. The visualization unit uses the generation AI to visualize the user's motivations and benefits for quitting smoking. For example, the visualization unit displays health improvements and economic benefits of quitting smoking in graphs and charts to motivate the user. The sharing unit uses the generation AI to share the user's smoking cessation progress with other users. For example, the sharing unit can record the user's progress in quitting smoking within the app and share it with other users, thereby enabling the smoking cessation support application to effectively support the user in quitting smoking.

[0056] The quitting smoking success factor analysis unit can use the emotion estimation function to track changes in emotions regarding quitting smoking and analyze the impact of emotional fluctuations on successful quitting smoking. The quitting smoking success factor analysis unit, for example, uses the user's emotion estimation function to track changes in emotions regarding quitting smoking in real time. For example, it records emotional fluctuations from the time the user starts quitting smoking and analyzes how emotional changes affect successful quitting smoking. The quitting smoking success factor analysis unit also uses the emotion estimation function to measure the level of stress and anxiety the user feels while quitting smoking and identifies factors for successful quitting smoking based on that data. For example, it can reveal that quitting smoking is more likely to fail when stress levels are high. The quitting smoking success factor analysis unit also analyzes the user's emotion data to identify positive and negative emotions that contribute to successful quitting smoking. For example, it analyzes how joy and a sense of accomplishment affect successful quitting smoking. This makes it possible to analyze the impact of emotional fluctuations on successful quitting smoking.

[0057] The quitting smoking success factor analysis unit can analyze lifestyle rhythms or eating patterns to identify factors that contribute to successful quitting smoking. The quitting smoking success factor analysis unit, for example, analyzes the user's lifestyle rhythms to identify factors that contribute to successful quitting smoking. For example, it analyzes how a regular lifestyle rhythm affects successful quitting smoking. The quitting smoking success factor analysis unit also analyzes the user's eating patterns to identify factors that contribute to successful quitting smoking. For example, it analyzes whether a specific diet helps reduce stress while quitting smoking. The quitting smoking success factor analysis unit also performs an integrated analysis of the user's lifestyle rhythms and eating patterns to identify factors that contribute to successful quitting smoking. For example, it analyzes how eating a good breakfast affects successful quitting smoking. This makes it possible to analyze the impact of lifestyle rhythms and eating patterns on successful quitting smoking.

[0058] The quitting smoking success factor analysis unit can analyze social media activity and evaluate the social influence on quitting smoking. For example, the quitting smoking success factor analysis unit analyzes a user's social media activity to evaluate the social influence on quitting smoking. For example, it analyzes the number of posts and comments about quitting smoking to clarify how social support affects successful quitting smoking. The quitting smoking success factor analysis unit also analyzes a user's interactions on social media to evaluate the social influence on quitting smoking. For example, it analyzes the type of reaction a user who declares they are quitting smoking receives from other users. The quitting smoking success factor analysis unit also analyzes the relationship between a user's social media activity and successful quitting smoking to evaluate social influence. For example, it analyzes the type of support a user who successfully quit smoking received on social media. This makes it possible to evaluate the social influence of social media activity on quitting smoking.

[0059] The quitting smoking success factor analysis unit can collect exercise habit or fitness data and analyze physical factors related to successful quitting smoking. The quitting smoking success factor analysis unit, for example, analyzes the user's exercise habit and identifies physical factors related to successful quitting smoking. For example, it analyzes how regular exercise affects successful quitting smoking. The quitting smoking success factor analysis unit also collects fitness data and identifies physical factors related to successful quitting smoking. For example, it analyzes how heart rate and calorie consumption affect successful quitting smoking. The quitting smoking success factor analysis unit also analyzes the user's exercise habit and fitness data in an integrated manner and identifies physical factors related to successful quitting smoking. For example, it analyzes how the relaxation effect after exercise affects successful quitting smoking. This makes it possible to analyze the impact of exercise habit and fitness data on successful quitting smoking.

[0060] The quitting smoking success factor analysis unit can analyze sleep patterns and identify factors that contribute to successful quitting smoking. The quitting smoking success factor analysis unit, for example, analyzes the user's sleep patterns and identifies factors that contribute to successful quitting smoking. For example, it analyzes how sufficient sleep affects successful quitting smoking. The quitting smoking success factor analysis unit also analyzes sleep quality and identifies factors that contribute to successful quitting smoking. For example, it analyzes whether deep sleep helps reduce stress while quitting smoking. The quitting smoking success factor analysis unit also analyzes the relationship between the user's sleep patterns and successful quitting smoking and identifies factors that contribute to successful quitting smoking. For example, it analyzes how staying up late affects successful quitting smoking. This makes it possible to analyze the impact of sleep patterns on successful quitting smoking.

[0061] The smoking cessation success element analysis unit can use the emotion estimation function to track changes in the user's motivation to quit smoking in real time and provide appropriate support. The smoking cessation success element analysis unit, for example, uses the emotion estimation function to track changes in the user's motivation to quit smoking in real time. For example, it sends an encouraging message when the user's motivation to continue quitting smoking decreases. The smoking cessation success element analysis unit also analyzes the user's emotion data to identify changes in the user's motivation to quit smoking. For example, it predicts when motivation will decrease and provides appropriate support. The smoking cessation success element analysis unit also uses the emotion estimation function to track changes in the user's motivation to quit smoking in real time and provides appropriate support. For example, it suggests relaxation methods when motivation decreases. This makes it possible to track changes in motivation in real time and provide appropriate support.

[0062] The support providing unit can use the emotion estimation function to monitor the user's stress level in real time and provide customized advice for stress reduction. The support providing unit, for example, uses the emotion estimation function to monitor the user's stress level in real time. For example, when stress increases, advice on deep breathing or meditation is provided. The support providing unit also analyzes the user's stress level and provides customized advice for stress reduction. For example, relaxation methods are suggested during times of high stress. The support providing unit also uses the emotion estimation function to monitor the user's stress level in real time and provide customized advice for stress reduction. For example, when stress increases, relaxing music is suggested. In this way, stress levels can be monitored in real time and customized advice can be provided.

[0063] The support providing unit can analyze the user's past smoking cessation attempt data and compare successful and unsuccessful methods to provide optimal support. The support providing unit, for example, analyzes the user's past smoking cessation attempt data and compares successful and unsuccessful methods. For example, provides optimal support based on the successful methods. The support providing unit can also analyze the user's past smoking cessation attempt data and identify successful and unsuccessful methods. For example, provides customized advice based on the successful methods. The support providing unit can also analyze the user's past smoking cessation attempt data and compare successful and unsuccessful methods to provide optimal support. For example, provides advice to avoid unsuccessful methods. In this way, optimal support can be provided based on the past smoking cessation attempt data.

[0064] The support providing unit can analyze the user's health data and provide smoking cessation support based on the health condition. The support providing unit analyzes health data such as the user's heart rate and blood pressure, and provides smoking cessation support based on the health condition. For example, it suggests relaxation methods when the heart rate is high. The support providing unit also customizes the user's smoking cessation support based on the health data. For example, it provides advice on reducing stress when blood pressure is high. The support providing unit also analyzes the user's health data and provides smoking cessation support based on the health condition. For example, it provides appropriate support based on fluctuations in heart rate and blood pressure. This makes it possible to provide smoking cessation support based on health data.

[0065] The support providing unit can suggest activities that can be enjoyed while quitting smoking based on the user's hobbies and interests. The support providing unit, for example, analyzes the user's hobbies and interests and suggests activities that can be enjoyed while quitting smoking. For example, it suggests activities such as reading or watching movies. The support providing unit also customizes activities that can be enjoyed while quitting smoking based on the user's hobbies and interests. For example, it suggests sports or outdoor activities. The support providing unit also suggests activities that can be enjoyed while quitting smoking based on the user's hobbies and interests. For example, it suggests arts and crafts activities. In this way, it is possible to suggest activities that can be enjoyed while quitting smoking based on the user's hobbies and interests.

[0066] The support providing unit can analyze the user's eating patterns and provide a meal plan suitable for smoking cessation. The support providing unit, for example, analyzes the user's eating patterns and provides a meal plan suitable for smoking cessation. For example, it suggests meals that reduce stress while quitting smoking. The support providing unit also customizes a meal plan suitable for smoking cessation based on the eating patterns. For example, it suggests nutritionally balanced meals. The support providing unit also analyzes the user's eating patterns and provides a meal plan suitable for smoking cessation. For example, it suggests meals that suppress appetite while quitting smoking. In this way, a meal plan suitable for smoking cessation can be provided based on the eating patterns.

[0067] The support providing unit can use the emotion estimation function to provide an encouraging message or a motivational video according to the user's emotional state. For example, the support providing unit uses the emotion estimation function to provide an encouraging message according to the user's emotional state. For example, an encouraging message is sent when the user is feeling down. The support providing unit also analyzes the user's emotional state and provides a motivational video according to the emotion. For example, a video that helps the user relax when the user is feeling stressed. The support providing unit also uses the emotion estimation function to provide an encouraging message or a motivational video according to the user's emotional state. For example, a video that will increase the user's motivation when the user's motivation to continue quitting smoking is reduced. This makes it possible to provide an encouraging message or a motivational video according to the user's emotional state.

[0068] The visualization unit can use the emotion estimation function to analyze the user's emotional response to the motivation to quit smoking and provide emotion-based motivation. The visualization unit, for example, uses the emotion estimation function to analyze the user's emotional response to the motivation to quit smoking. For example, it measures the intensity of the user's emotions regarding the reasons for their decision to quit smoking and provides motivation based on that data. The visualization unit also analyzes the user's emotional response to the motivation to quit smoking and provides emotion-based motivation. For example, if the user has strong positive emotions regarding health improvement, the benefits of the improvement are emphasized. The visualization unit also uses the emotion estimation function to analyze the user's emotional response to the motivation to quit smoking in real time and provide emotion-based motivation. For example, if the user has strong emotions regarding economic benefits, the benefits are emphasized. This allows emotion-based motivation.

[0069] The visualization unit can visualize the specific health improvement effects of quitting smoking based on the user's health data. The visualization unit visualizes the specific health improvement effects of quitting smoking based on the user's health data, for example. For example, it displays changes in heart rate and blood pressure after quitting smoking in a graph. The visualization unit also analyzes the health data and visualizes the health improvement effects of quitting smoking. For example, it displays improvements in lung function after quitting smoking in a chart. The visualization unit also visualizes the specific health improvement effects of quitting smoking based on the user's health data. For example, it visually displays changes in weight and improvements in energy level after quitting smoking. In this way, the specific health improvement effects of quitting smoking can be visualized based on the health data.

[0070] The visualization unit can analyze the user's economic data and display the economic benefits of quitting smoking in detail. The visualization unit, for example, analyzes the user's economic data and displays the economic benefits of quitting smoking in detail. For example, it displays the amount of money that can be saved by quitting smoking by month in a graph. The visualization unit also visualizes the economic benefits of quitting smoking based on the economic data. For example, it displays the total amount of money that can be saved per year by quitting smoking in a chart. The visualization unit also analyzes the user's economic data and displays the economic benefits of quitting smoking in detail. For example, it displays the amount of money that can be saved by quitting smoking in comparison with other expenditure items. In this way, the economic benefits of quitting smoking can be displayed in detail based on the economic data.

[0071] The visualization unit can visualize the support from the user's family and friends and emphasize the importance of social support. The visualization unit, for example, visualizes the support from the user's family and friends and emphasizes the importance of social support. For example, it displays encouraging messages from family and friends. The visualization unit also visualizes the support from family and friends and emphasizes the importance of social support for successfully quitting smoking. For example, it displays video messages from family and friends encouraging the user to quit smoking. The visualization unit also visualizes the support from the user's family and friends and emphasizes the importance of social support. For example, it displays comments from family and friends encouraging the user to quit smoking. In this way, the support from family and friends can be visualized and the importance of social support can be emphasized.

[0072] The visualization unit visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. The visualization unit, for example, visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. For example, the visualization unit displays the number of days a user has quit smoking and the successful experiences achieved in the past in a graph. The visualization unit also visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. For example, the visualization unit visually displays the feelings and experiences experienced when a user successfully quits smoking. The visualization unit also visualizes the user's past successful experiences, thereby increasing the user's sense of self-efficacy. For example, the user can share their past successful experiences of quitting smoking with other users and encourage each other. This makes it possible to visualize the user's past successful experiences, thereby increasing the user's sense of self-efficacy.

[0073] The visualization unit uses the emotion estimation function to display the user's emotional response to the motivation to quit smoking in real time, thereby maintaining motivation. The visualization unit, for example, uses the emotion estimation function to display the user's emotional response to the motivation to quit smoking in real time. For example, it emphasizes positive emotions toward the motivation to quit smoking. The visualization unit also displays the user's emotional response to the motivation to quit smoking in real time, thereby maintaining motivation. For example, it displays changes in emotions toward the motivation to quit smoking in a graph. The visualization unit also uses the emotion estimation function to display the user's emotional response to the motivation to quit smoking in real time, thereby maintaining motivation. For example, it visually displays the intensity of emotions toward the motivation to quit smoking. In this way, emotional responses can be displayed in real time, thereby maintaining motivation.

[0074] The sharing unit can use the emotion estimation function to analyze the user's emotional response to the progress of quitting smoking and provide feedback based on the emotion. For example, the sharing unit can use the emotion estimation function to analyze the user's emotional response to the progress of quitting smoking. For example, the sharing unit can provide feedback that emphasizes positive emotions about the progress of quitting smoking. The sharing unit can also analyze the user's emotional response to the progress of quitting smoking and provide feedback based on the emotion. For example, the sharing unit can send an encouraging message based on changes in emotions about the progress of quitting smoking. The sharing unit can also use the emotion estimation function to analyze the user's emotional response to the progress of quitting smoking in real time and provide feedback based on the emotion. For example, the sharing unit can provide appropriate support based on the intensity of emotions about the progress of quitting smoking. In this way, the emotional response can be analyzed and feedback based on the emotion.

[0075] The sharing unit can record the user's progress in quitting smoking in detail and provide individualized advice based on the progress data. The sharing unit, for example, can record the user's progress in quitting smoking in detail and provide individualized advice based on the progress data. For example, it can provide advice based on the number of days since quitting smoking or successful experiences. The sharing unit can also analyze the quitting smoking progress data and provide individualized advice. For example, it can suggest relaxation methods based on the progress in quitting smoking. The sharing unit can also record the user's progress in quitting smoking in detail and provide individualized advice based on the progress data. For example, it can provide advice on stress management based on the progress in quitting smoking. This makes it possible to record the user's progress in quitting smoking in detail and provide individualized advice.

[0076] The sharing unit can provide interactive graphs and charts to visualize the user's progress in quitting smoking and enhance the sense of accomplishment. The sharing unit, for example, provides interactive graphs to visualize the user's progress in quitting smoking and enhance the sense of accomplishment. For example, it may display the number of days since quitting smoking or success stories in a graph. The sharing unit also provides interactive charts to visualize the progress in quitting smoking and enhance the sense of accomplishment. For example, it may display a chart showing the health improvements achieved by quitting smoking. The sharing unit also provides interactive graphs and charts to visualize the user's progress in quitting smoking and enhance the sense of accomplishment. For example, it may visually display the economic benefits of quitting smoking. In this way, it is possible to provide interactive graphs and charts to visualize the progress in quitting smoking and enhance the sense of accomplishment.

[0077] The sharing unit allows the user to share their progress in quitting smoking on social media and receive widespread support. The sharing unit, for example, shares the user's progress in quitting smoking on social media and receives widespread support. For example, the number of days since quitting smoking and success stories are posted on SNS. The sharing unit also shares the user's progress in quitting smoking through social media and receives encouragement and support from other users. For example, the user's feelings and experiences when they successfully quit smoking are shared on SNS. The sharing unit also shares the user's progress in quitting smoking on social media and receives widespread support. For example, the user's health improvements and financial benefits from quitting smoking are posted on SNS. In this way, the user can share their progress in quitting smoking on social media and receive widespread support.

[0078] The sharing unit can introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. The sharing unit can, for example, introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. For example, it can provide a ranking system in which users compete based on the number of days without smoking or successful experiences. The sharing unit can also introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. For example, it can provide rewards or badges for users who successfully quit smoking. The sharing unit can also introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation. For example, it can provide a challenge between users who have successfully quit smoking. This can introduce a competitive element with other users based on the user's progress in quitting smoking, thereby increasing motivation.

[0079] The sharing unit uses the emotion estimation function to share the user's emotional reactions to the progress of quitting smoking in real time, thereby promoting empathy within the community. The sharing unit, for example, uses the emotion estimation function to share the user's emotional reactions to the progress of quitting smoking in real time. For example, the joy and sense of accomplishment felt when successfully quitting smoking is shared. The sharing unit also shares the user's emotional reactions to the progress of quitting smoking in real time, thereby promoting empathy within the community. For example, the emotions felt when successfully quitting smoking are shared with other users. The sharing unit also uses the emotion estimation function to share the user's emotional reactions to the progress of quitting smoking in real time, thereby promoting empathy within the community. For example, the emotions felt when successfully quitting smoking are visually displayed. This allows emotional reactions to be shared in real time, thereby promoting empathy within the community.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] Smoking cessation support applications can also analyze a user's exercise habits and identify physical factors related to successful quitting smoking. For example, they can analyze how regular exercise affects successful quitting smoking. They can also collect fitness data and analyze how heart rate and calorie consumption affect successful quitting smoking. They can also analyze how the relaxation effect after exercise affects successful quitting smoking. This allows for a detailed analysis of the impact of exercise habits and fitness data on successful quitting smoking.

[0082] The smoking cessation support application can further analyze the user's sleep patterns to identify factors that contribute to successful quitting smoking. For example, it can analyze how getting enough sleep affects successful quitting smoking. It can also analyze sleep quality to determine whether deep sleep helps reduce stress while quitting smoking. It can also analyze how staying up late affects successful quitting smoking. This allows for a detailed analysis of the impact that sleep patterns have on successful quitting smoking.

[0083] Smoking cessation support applications can also analyze users' social media activity to evaluate the social influence on quitting smoking. For example, analyzing the number of posts and comments about quitting smoking can clarify how social support affects successful quitting. It is also possible to analyze users' interactions on social media to evaluate the social influence on quitting smoking. It is also possible to analyze the type of support users who successfully quit smoking received on social media. This allows for a detailed evaluation of the social influence of social media activity on quitting smoking.

[0084] Smoking cessation support applications can also analyze the user's dietary patterns to identify factors that contribute to successful smoking cessation. For example, they can analyze whether a particular diet helps reduce stress while quitting smoking. They can also perform an integrated analysis of the user's lifestyle and dietary patterns to identify factors that contribute to successful smoking cessation. They can also analyze how eating a good breakfast affects successful smoking cessation. This allows for a detailed analysis of the impact that lifestyle and dietary patterns have on successful smoking cessation.

[0085] The smoking cessation support application can further analyze the user's health data and provide smoking cessation support based on the user's health condition. For example, it can analyze health data such as heart rate and blood pressure and provide smoking cessation support based on the user's health condition. For example, it can suggest relaxation methods when the heart rate is high. It can also provide advice on stress reduction when blood pressure is high. It can also provide appropriate support based on fluctuations in heart rate and blood pressure. This makes it possible to provide smoking cessation support based on health data.

[0086] The smoking cessation support application can also use the emotion estimation function to track changes in the user's motivation to quit smoking in real time and provide appropriate support. For example, it can send an encouraging message when the user's motivation to continue quitting decreases. It can also analyze the user's emotion data to predict when motivation will decrease and provide appropriate support. It can also suggest relaxation methods when motivation decreases. This makes it possible to track changes in motivation in real time and provide appropriate support.

[0087] The smoking cessation support application can further use the emotion estimation function to monitor the user's stress level in real time and provide customized advice for stress reduction. For example, when stress levels rise, it can provide advice on deep breathing or meditation. It can also analyze the user's stress level and suggest relaxation methods for times when stress levels rise. It can also suggest relaxing music when stress levels rise. This makes it possible to monitor stress levels in real time and provide customized advice.

[0088] The smoking cessation support application can further use the emotion estimation function to provide encouraging messages and motivational videos according to the user's emotional state. For example, an encouraging message can be sent when the user is feeling down. It can also analyze the user's emotional state and provide a motivational video according to the emotion. It can also suggest a video that will motivate the user when their motivation to continue quitting smoking decreases. This makes it possible to provide encouraging messages and motivational videos according to the user's emotional state.

[0089] The smoking cessation support application can further use the emotion estimation function to analyze the user's emotional response to their motivation to quit smoking and provide emotion-based motivation. For example, it can measure the intensity of the user's emotions regarding the reasons for their decision to quit smoking and provide motivation based on that data. It can also analyze the user's emotional response to their motivation to quit smoking and, if they have strong positive emotions regarding health improvements, it can emphasize the benefits. Furthermore, if they have strong emotions regarding economic benefits, it can emphasize these points. This allows for emotion-based motivation.

[0090] The smoking cessation support application can also use the emotion estimation function to share users' emotional reactions to their progress in quitting smoking in real time, promoting empathy within the community. For example, users can share the joy and sense of accomplishment they felt when they successfully quit smoking. Users can also share their emotional reactions to their progress in quitting smoking in real time, allowing them to share their feelings when they successfully quit smoking with other users. It can also visually display the feelings they had when they successfully quit smoking. This allows users to share their emotional reactions in real time, promoting empathy within the community.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The generating AI collects and analyzes data on the user's smoking cessation and identifies factors that contribute to successful smoking cessation. For example, the generating AI analyzes the user's past attempts to quit smoking, current lifestyle habits, stress levels, etc., to identify factors that contribute to successful smoking cessation. Step 2: The quit smoking success factor analysis unit uses the generation AI to analyze the factors that contribute to the user's success in quitting smoking. For example, the quit smoking success factor analysis unit analyzes data related to the user's quitting smoking and identifies the factors that contribute to successful quitting smoking. Step 3: The support provider provides support tailored to the user based on the factors analyzed by the smoking cessation success factor analyzer. For example, if the user is prone to stress, the support provider provides advice on relaxation techniques and stress management. Step 4: The visualization unit uses generative AI to visualize the user's motivations and benefits for quitting smoking. For example, the visualization unit may display the health improvements and economic benefits of quitting smoking in graphs and charts, motivating the user to quit smoking. Step 5: The sharing unit shares the user's progress in quitting smoking with other users using the generation AI. For example, the sharing unit can record the user's progress in quitting smoking within the app and share it with other users.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

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

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0129] The 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.

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

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0140] 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.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. Equipped with generative AI, A quitting smoking success factor analysis unit that analyzes the user's quitting smoking success factors using the generation AI; a support providing unit that provides support tailored to the user based on the factors analyzed by the quit smoking success factor analysis unit; A visualization unit that visualizes the user's motivation or benefits for quitting smoking using the generation AI; a sharing unit that shares the user's progress in quitting smoking with other users using the generation AI; A system characterized by:

2. The quit smoking success factor analysis unit Track changes in feelings toward quitting smoking and analyze the impact of these changes on quitting smoking success 2. The system of claim 1.

3. The quit smoking success factor analysis unit Analyze lifestyle or dietary patterns to identify factors that contribute to successful smoking cessation 2. The system of claim 1.

4. The quit smoking success factor analysis unit Analyzing social media activity to assess social influences on smoking cessation 2. The system of claim 1.

5. The quit smoking success factor analysis unit Collect exercise habits or fitness data to analyze physical factors related to successful smoking cessation 2. The system of claim 1.

6. The quit smoking success factor analysis unit Analyze sleep patterns and identify factors that contribute to successful smoking cessation 2. The system of claim 1.

Images