system

Abstract

The system according to this embodiment aims to reduce physical and mental stress during the smoking cessation process and to support successful smoking cessation. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a support unit. The collection unit collects the user's physiological and behavioral data. The analysis unit analyzes the data collected by the collection unit. The support unit provides support using medical and psychological approaches based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the physical and mental stress in the smoking cessation process has not been sufficiently effectively reduced, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the physical and mental stress in the smoking cessation process and support smoking cessation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a support unit. The collection unit collects the user's physiological data and behavioral data. The analysis unit analyzes the data collected by the collection unit. The support unit provides support in medical and psychological approaches based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce physical and mental stress during the smoking cessation process and support successful smoking cessation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). [[ID=**16]]

[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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smoking cessation support system according to an embodiment of the present invention is a system that supports smoking cessation while removing many of the stresses in the smoking cessation process by utilizing a generating AI agent. This smoking cessation support system collects and analyzes the user's physiological and behavioral data. Next, based on the analysis results, it provides support through medical and psychological approaches. For example, the medical approach provides advice to relieve or avoid physical stress. The psychological approach provides support to relieve or avoid mental stress. As a result, the user can achieve stress-free smoking cessation. First, the smoking cessation support system collects the user's physiological and behavioral data. In this process, data such as heart rate, activity level, and location information is collected using wearable devices or smartphones. For example, it is possible to understand what kind of behavior the user engages in on a daily basis and under what circumstances the urge to smoke increases. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI analyzes the user's stress level and patterns of smoking cravings. For example, if it is found that the urge to smoke increases at a specific time or place, countermeasures can be taken at that time or place. Based on the analysis results, the generating AI provides support through medical and psychological approaches. The medical approach provides advice on relieving or avoiding physical stress. For example, when cravings for smoking increase, it suggests methods to reduce stress, such as deep breathing or light exercise. It can also automatically schedule online consultations with doctors and arrange for medication delivery as needed. The psychological approach provides support for relieving or avoiding mental stress. For example, the generative AI can send encouraging messages to the user or suggest relaxing music or videos. It can also provide coupons usable at nearby stores or news of interest to help curb smoking urges. In this way, by utilizing the generative AI agent, users can achieve stress-free smoking cessation. Quitting smoking is an expression of love for oneself and those around them, and it not only reduces the risk of health damage but also prevents losses for society as a whole.This allows the smoking cessation support system to collect users' physiological and behavioral data and provide support through medical and psychological approaches based on the analysis results, thereby assisting users in quitting smoking.

[0029] The smoking cessation support system according to this embodiment comprises a data collection unit, an analysis unit, and a support unit. The data collection unit collects the user's physiological and behavioral data. For example, the data collection unit can collect physiological data such as heart rate, blood pressure, and body temperature. The data collection unit can also collect behavioral data such as steps taken, sleep patterns, and exercise levels. The data collection unit can collect data using wearable devices or smartphones. For example, the data collection unit can monitor heart rate and activity levels using a smartwatch. The data collection unit can also collect location information using the GPS function of a smartphone. Furthermore, the data collection unit can record exercise levels using a fitness tracker. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can perform statistical analysis of the data. The analysis unit can also analyze the data using machine learning algorithms. Based on the collected data, the analysis unit analyzes the user's stress level and patterns of smoking cravings. For example, the analysis unit can evaluate stress levels by analyzing heart rate variability. The analysis unit can also evaluate stress levels using self-report questionnaires. Furthermore, the analysis department can also identify patterns in smoking cravings by analyzing smoking frequency at different times of the day. The support department provides support through medical and psychological approaches based on the analysis results obtained by the analysis department. The support department can provide medical approaches such as pharmacotherapy and exercise therapy. The support department can also provide psychological approaches such as counseling and cognitive behavioral therapy. The support department provides advice on relieving or avoiding physical stress. For example, the support department can suggest ways to reduce stress, such as deep breathing or light exercise. The support department also provides support for relieving or avoiding mental stress. For example, the support department can send encouraging messages or suggest relaxing music or videos. In addition, the support department can provide coupons usable at nearby stores or news of interest to help curb smoking urges.As a result, the smoking cessation support system according to the embodiment can support smoking cessation by collecting the user's physiological and behavioral data and providing support through medical and psychological approaches based on the analysis results.

[0030] The data collection unit collects the user's physiological and behavioral data. For example, the unit can collect physiological data such as heart rate, blood pressure, and body temperature. Specifically, heart rate is measured in real time using a heart rate sensor, and blood pressure is recorded using a device that measures periodically. Body temperature is often measured using a skin-contact sensor or an ear thermometer. The data collection unit can also collect behavioral data such as steps taken, sleep patterns, and exercise levels. Step counts are measured using wearable devices with built-in accelerometers or smartphones, and sleep patterns are analyzed by monitoring heart rate and body movement. Exercise levels are measured using fitness trackers or smartwatches to collect detailed data such as the type, intensity, and duration of exercise. The data collection unit can collect data using wearable devices or smartphones. For example, the unit can monitor heart rate and activity levels using a smartwatch. Smartwatches are equipped with heart rate sensors, accelerometers, and gyroscopes, and these sensors are used to collect detailed physiological and behavioral data. The data collection unit can also collect location information using the GPS function of a smartphone. Location information is crucial for understanding user movement patterns and activity ranges, and is also used to track changes in smoking habits. Furthermore, the data collection unit can record exercise levels using fitness trackers. Fitness trackers meticulously record exercise type, intensity, and duration, helping to understand user exercise habits. This allows the data collection unit to gather a wide range of data from various devices and understand the situation in real time. Moreover, the data collection unit can centrally manage this data and integrate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analytics and support departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can perform statistical analysis of the data. In statistical analysis, basic statistics such as the mean, median, and standard deviation of the collected data are calculated to understand the distribution and trends of the data. The analysis department can also analyze the data using machine learning algorithms. Machine learning algorithms are used to extract patterns and features from large amounts of data and to build predictive models. For example, the analysis department can analyze user stress levels and patterns of smoking cravings based on the collected data. Stress levels can be evaluated by analyzing heart rate variability. Heart rate variability is an indicator that evaluates the state of stress and relaxation by analyzing the range of fluctuations and rhythm of heart rate. Stress levels can also be evaluated using self-report questionnaires. Subjective stress levels are evaluated based on questionnaires that users answer regularly, and a comprehensive stress assessment is performed by combining this with objective data. Furthermore, the analysis department can identify patterns of smoking cravings by analyzing smoking frequency by time of day. Smoking frequency data is aggregated by time of day to understand the strength and frequency of smoking cravings at specific time periods. This allows the analytics department to quickly and accurately analyze collected data, enabling real-time understanding of user stress levels and smoking craving patterns. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past stress level and smoking frequency data, it can predict risk fluctuations over a specific period and formulate future countermeasures. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The support department provides support through medical and psychological approaches based on the analysis results obtained by the analysis department. For example, the support department can offer medical approaches such as pharmacotherapy and exercise therapy. In pharmacotherapy, users are provided with smoking cessation aids such as nicotine patches and nicotine gum to reduce cravings. In exercise therapy, moderate exercise is recommended to reduce stress and increase the success rate of quitting smoking. Furthermore, the support department can also offer psychological approaches such as counseling and cognitive behavioral therapy. In counseling, professional counselors engage with users to identify the causes of smoking and stressors and propose appropriate countermeasures. In cognitive behavioral therapy, users are instructed on specific methods to change their thought and behavior patterns, and to acquire techniques to control their cravings. The support department also provides advice on relieving or avoiding physical stress. For example, the support department can suggest methods to reduce stress through deep breathing and light exercise. Deep breathing has a relaxing effect and is easy to practice when feeling stressed. Light exercise promotes the secretion of endorphins and has the effect of improving mood. Furthermore, the support department provides assistance to alleviate or avoid mental stress. For example, they can send encouraging messages or suggest relaxing music and videos. Encouraging messages are important for users to maintain their motivation to quit smoking, and relaxing music and videos are effective means of reducing stress and promoting relaxation. In addition, the support department can provide coupons usable at nearby stores or news of interest to curb smoking urges. Coupons serve as an incentive for users to avoid smoking, and news of interest serves as a distraction to forget smoking urges. In this way, the support department can provide users with multifaceted support to help them successfully quit smoking.

[0033] The data collection unit can collect physiological and behavioral data using wearable devices and smartphones. For example, the data collection unit can monitor heart rate and activity levels using a smartwatch. The data collection unit can also collect location information using the GPS function of a smartphone. The data collection unit can also record exercise levels using a fitness tracker. This allows for the efficient collection of user physiological and behavioral data using wearable devices and smartphones. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data acquired from a smartwatch into a generating AI and have the generating AI perform analysis of the heart rate data.

[0034] The analysis unit can analyze the user's stress level and smoking craving patterns based on the collected data. For example, the analysis unit can assess stress levels by analyzing heart rate variability. The analysis unit can also assess stress levels using self-report questionnaires. The analysis unit can also identify smoking craving patterns by analyzing smoking frequency by time of day. This allows for the provision of appropriate support by analyzing stress levels and smoking craving patterns based on the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of stress levels and smoking craving patterns.

[0035] The support unit can provide advice on relieving or avoiding physical stress through a medical approach. For example, the support unit may suggest ways to reduce stress, such as deep breathing or light exercise. The support unit can also automate tasks such as scheduling online consultations with doctors or arranging for the delivery of medications. This reduces the user's physical stress by providing advice on relieving or avoiding physical stress through a medical approach. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's stress level data into a generative AI and have the generative AI generate advice for stress reduction.

[0036] The support department can provide support to relieve or avoid mental stress through psychological approaches. For example, the support department can send encouraging messages. For example, the support department can suggest relaxing music or videos. For example, the support department can provide counseling or cognitive behavioral therapy. In this way, the user's mental stress can be reduced by providing support to relieve or avoid mental stress through psychological approaches. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the user's mental stress data into a generative AI and have the generative AI perform stress relief support.

[0037] The support unit can provide coupons usable at nearby stores and news of interest to help curb smoking urges. For example, the support unit can provide discount coupons usable at nearby stores. The support unit can also provide news feeds based on the user's interests. The support unit can also suggest relaxation techniques to help curb smoking urges. In this way, the user's smoking urge can be reduced by providing coupons usable at nearby stores and news of interest to help curb smoking urges. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's smoking urge data into a generative AI and have the generative AI provide coupons and news to help curb smoking urges.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using devices that the user has preferred to use in the past. The data collection unit may also suggest the optimal data collection method based on the types of data the user has collected in the past. The data collection unit may also predict and suggest the optimal collection timing based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and behavioral patterns. For example, the unit can refrain from collecting data when the user is working and collect it during breaks. For example, the unit can prioritize collecting exercise data when the user is exercising. For example, the unit can collect sleep data and refrain from collecting other data when the user is sleeping. This allows for the collection of appropriate data by filtering based on the user's current lifestyle and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering of data collection.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, if the user is on the move, the data collection unit can also prioritize the collection of data related to movement. For example, if the user is at home, the data collection unit can also prioritize the collection of data related to activities at home. This allows for the collection of appropriate data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is experiencing stress on social media, the data collection unit can collect data related to that stress. For example, if a user is relaxing on social media, the data collection unit can also collect data related to that relaxation. For example, if a user is in a hurry on social media, the data collection unit can also collect data related to that urgency. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can analyze patterns of stress levels and smoking cravings at specific times and locations during data analysis. For example, if a user experiences stress at a particular time, the analysis unit will prioritize analyzing data from that time period. For example, if a user experiences stress at a particular location, the analysis unit can also prioritize analyzing data from that location. For example, if a user experiences increased smoking cravings at a particular time or location, the analysis unit can also prioritize analyzing data from that location. By analyzing patterns of stress levels and smoking cravings at specific times and locations, appropriate countermeasures can be taken. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data from specific times and locations into a generative AI and have the generative AI perform the analysis of stress levels and smoking craving patterns.

[0043] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior data during data analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past behavior data. The analysis unit can also extract specific patterns from the user's past behavior data and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis algorithm by referring to the user's past behavior data. In this way, by referring to the user's past behavior data, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past behavior data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can perform data analysis while considering the geographical distribution of users. For example, if a user is in a specific region, the analysis unit will prioritize analyzing data for that region. For example, if a user is on the move, the analysis unit can prioritize analyzing data related to movement. For example, if a user is at home, the analysis unit can prioritize analyzing data related to activities at home. By performing analysis while considering the geographical distribution of users, it is possible to understand region-specific stressors and patterns of smoking cravings. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user geographical distribution data into a generative AI and have the generative AI perform an analysis of region-specific stressors and patterns of smoking cravings.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during data analysis. For example, the analysis unit can improve the accuracy of its analysis algorithms by referring to relevant literature. The analysis unit can also improve the reliability of its analysis results by referring to relevant research data. The analysis unit can also optimize its analysis methods based on relevant literature and research data. This allows the analysis to improve accuracy by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input relevant literature and research data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0046] The support unit can select the optimal support method by referring to the user's past stress levels and smoking craving patterns during support. For example, the support unit can select the optimal support method based on the user's past stress levels. The support unit can also select the optimal support method based on the user's past smoking craving patterns. The support unit can also improve the accuracy of the support method by referring to the user's past data. This allows the optimal support method to be selected by referring to the user's past stress levels and smoking craving patterns. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input past stress level and smoking craving data into a generative AI and have the generative AI select the optimal support method.

[0047] The support unit can customize the means of support based on the user's current living situation when providing assistance. For example, if the user is at work, the support unit can provide support methods that do not interfere with their work. For example, if the user is exercising, the support unit can also provide support methods related to exercise. For example, if the user is sleeping, the support unit can also provide support methods that do not disturb their sleep. By customizing the means of support based on the user's current living situation, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the support means.

[0048] The support unit can select the optimal support method when providing assistance, taking into account the user's geographical location information. For example, if the user is in a specific location, the support unit can provide support methods related to that location. For example, if the user is on the move, the support unit can also provide support methods related to movement. For example, if the user is at home, the support unit can also provide support methods related to activities at home. By selecting the optimal support method while considering the user's geographical location information, appropriate support can be provided. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the support unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal support method.

[0049] The support unit can analyze a user's social media activity and propose support measures when providing assistance. For example, if a user is experiencing stress on social media, the support unit can provide support methods related to that stress. For example, if a user is relaxing on social media, the support unit can also provide support methods related to that relaxation. For example, if a user is in a hurry on social media, the support unit can also provide support methods related to that urgency. In this way, by analyzing a user's social media activity, the support unit can propose relevant support measures. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's social media activity data into a generative AI and have the generative AI propose support measures.

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

[0051] The smoking cessation support system can also include a reward system. This reward system provides rewards to users when they successfully quit smoking. For example, the reward system could award points to users who remain smoke-free for a certain period. It could also offer special benefits to users upon successful smoking cessation. Furthermore, the reward system could provide regular rewards to maintain user motivation to continue abstaining from smoking. This helps users stay motivated to quit smoking.

[0052] The smoking cessation support system can also include a community section. This community section provides a platform for users to interact with other people trying to quit smoking. For example, it could offer a forum where users can share their experiences and advice on quitting smoking. It could also provide a Q&A section where users can post questions about quitting smoking and receive answers from other users. Furthermore, the community section could form support groups to help users stay smoke-free. This allows users to interact with other people trying to quit smoking and receive support to help them continue their abstinence.

[0053] The smoking cessation support system can also include a gaming section. This section provides games to help users stay smoke-free in an enjoyable way. For example, the gaming section could offer games where users earn points for staying smoke-free and can use those points to purchase items within the game. It could also offer games where users level up and earn rewards for staying smoke-free. Furthermore, the gaming section could offer games where users compete with other smokers, increasing their motivation to continue abstaining. This makes quitting smoking more enjoyable for users.

[0054] The smoking cessation support system can also include a feedback unit. This unit provides feedback to help users stay smoke-free. For example, it can periodically report the health benefits the user gains from remaining smoke-free. It can also calculate and report the amount of money the user saves by staying smoke-free. Furthermore, it can report the social benefits the user gains from remaining smoke-free. This provides users with the motivation to continue quitting smoking.

[0055] The smoking cessation support system can also include a customization section. This customization section provides smoking cessation support plans tailored to the user's individual needs. For example, it can propose an optimal smoking cessation support plan based on the user's smoking history and health condition. Furthermore, it can adjust the plan according to the user's lifestyle and preferences. In addition, the customization section can update the smoking cessation support plan based on the user's progress. This ensures that the user receives the smoking cessation support plan best suited to them.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the user's physiological and behavioral data. The data collection unit can collect physiological data such as heart rate, blood pressure, and body temperature, as well as behavioral data such as steps taken, sleep patterns, and exercise levels. The data collection unit collects data using wearable devices and smartphones. For example, it can monitor heart rate and activity levels using a smartwatch, collect location information using the GPS function of a smartphone, and record exercise levels using a fitness tracker. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. Based on the collected data, the analysis unit analyzes the user's stress level and smoking craving patterns. For example, it can assess stress levels by analyzing heart rate variability and using self-report questionnaires. It can also identify patterns of smoking cravings by analyzing smoking frequency by time of day. Step 3: The support department provides support through medical and psychological approaches based on the analysis results obtained by the analysis department. The support department can provide medical approaches such as pharmacotherapy and exercise therapy. It can also provide psychological approaches such as counseling and cognitive behavioral therapy. The support department provides advice on how to relieve or avoid physical stress and provides support on how to relieve or avoid mental stress. For example, it can suggest ways to reduce stress, such as deep breathing or light exercise, send encouraging messages, or suggest relaxing music or videos. Furthermore, to curb smoking urges, it can provide coupons usable at nearby stores or news of interest.

[0058] (Example of form 2) The smoking cessation support system according to an embodiment of the present invention is a system that supports smoking cessation while removing many of the stresses in the smoking cessation process by utilizing a generating AI agent. This smoking cessation support system collects and analyzes the user's physiological and behavioral data. Next, based on the analysis results, it provides support through medical and psychological approaches. For example, the medical approach provides advice to relieve or avoid physical stress. The psychological approach provides support to relieve or avoid mental stress. As a result, the user can achieve stress-free smoking cessation. First, the smoking cessation support system collects the user's physiological and behavioral data. In this process, data such as heart rate, activity level, and location information is collected using wearable devices or smartphones. For example, it is possible to understand what kind of behavior the user engages in on a daily basis and under what circumstances the urge to smoke increases. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI analyzes the user's stress level and patterns of smoking cravings. For example, if it is found that the urge to smoke increases at a specific time or place, countermeasures can be taken at that time or place. Based on the analysis results, the generating AI provides support through medical and psychological approaches. The medical approach provides advice on relieving or avoiding physical stress. For example, when cravings for smoking increase, it suggests methods to reduce stress, such as deep breathing or light exercise. It can also automatically schedule online consultations with doctors and arrange for medication delivery as needed. The psychological approach provides support for relieving or avoiding mental stress. For example, the generative AI can send encouraging messages to the user or suggest relaxing music or videos. It can also provide coupons usable at nearby stores or news of interest to help curb smoking urges. In this way, by utilizing the generative AI agent, users can achieve stress-free smoking cessation. Quitting smoking is an expression of love for oneself and those around them, and it not only reduces the risk of health damage but also prevents losses for society as a whole.This allows the smoking cessation support system to collect users' physiological and behavioral data and provide support through medical and psychological approaches based on the analysis results, thereby assisting users in quitting smoking.

[0059] The smoking cessation support system according to this embodiment comprises a data collection unit, an analysis unit, and a support unit. The data collection unit collects the user's physiological and behavioral data. For example, the data collection unit can collect physiological data such as heart rate, blood pressure, and body temperature. The data collection unit can also collect behavioral data such as steps taken, sleep patterns, and exercise levels. The data collection unit can collect data using wearable devices or smartphones. For example, the data collection unit can monitor heart rate and activity levels using a smartwatch. The data collection unit can also collect location information using the GPS function of a smartphone. Furthermore, the data collection unit can record exercise levels using a fitness tracker. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can perform statistical analysis of the data. The analysis unit can also analyze the data using machine learning algorithms. Based on the collected data, the analysis unit analyzes the user's stress level and patterns of smoking cravings. For example, the analysis unit can evaluate stress levels by analyzing heart rate variability. The analysis unit can also evaluate stress levels using self-report questionnaires. Furthermore, the analysis department can also identify patterns in smoking cravings by analyzing smoking frequency at different times of the day. The support department provides support through medical and psychological approaches based on the analysis results obtained by the analysis department. The support department can provide medical approaches such as pharmacotherapy and exercise therapy. The support department can also provide psychological approaches such as counseling and cognitive behavioral therapy. The support department provides advice on relieving or avoiding physical stress. For example, the support department can suggest ways to reduce stress, such as deep breathing or light exercise. The support department also provides support for relieving or avoiding mental stress. For example, the support department can send encouraging messages or suggest relaxing music or videos. In addition, the support department can provide coupons usable at nearby stores or news of interest to help curb smoking urges.As a result, the smoking cessation support system according to the embodiment can support smoking cessation by collecting the user's physiological and behavioral data and providing support through medical and psychological approaches based on the analysis results.

[0060] The data collection unit collects the user's physiological and behavioral data. For example, the unit can collect physiological data such as heart rate, blood pressure, and body temperature. Specifically, heart rate is measured in real time using a heart rate sensor, and blood pressure is recorded using a device that measures periodically. Body temperature is often measured using a skin-contact sensor or an ear thermometer. The data collection unit can also collect behavioral data such as steps taken, sleep patterns, and exercise levels. Step counts are measured using wearable devices with built-in accelerometers or smartphones, and sleep patterns are analyzed by monitoring heart rate and body movement. Exercise levels are measured using fitness trackers or smartwatches to collect detailed data such as the type, intensity, and duration of exercise. The data collection unit can collect data using wearable devices or smartphones. For example, the unit can monitor heart rate and activity levels using a smartwatch. Smartwatches are equipped with heart rate sensors, accelerometers, and gyroscopes, and these sensors are used to collect detailed physiological and behavioral data. The data collection unit can also collect location information using the GPS function of a smartphone. Location information is crucial for understanding user movement patterns and activity ranges, and is also used to track changes in smoking habits. Furthermore, the data collection unit can record exercise levels using fitness trackers. Fitness trackers meticulously record exercise type, intensity, and duration, helping to understand user exercise habits. This allows the data collection unit to gather a wide range of data from various devices and understand the situation in real time. Moreover, the data collection unit can centrally manage this data and integrate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analytics and support departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can perform statistical analysis of the data. In statistical analysis, basic statistics such as the mean, median, and standard deviation of the collected data are calculated to understand the distribution and trends of the data. The analysis department can also analyze the data using machine learning algorithms. Machine learning algorithms are used to extract patterns and features from large amounts of data and to build predictive models. For example, the analysis department can analyze user stress levels and patterns of smoking cravings based on the collected data. Stress levels can be evaluated by analyzing heart rate variability. Heart rate variability is an indicator that evaluates the state of stress and relaxation by analyzing the range of fluctuations and rhythm of heart rate. Stress levels can also be evaluated using self-report questionnaires. Subjective stress levels are evaluated based on questionnaires that users answer regularly, and a comprehensive stress assessment is performed by combining this with objective data. Furthermore, the analysis department can identify patterns of smoking cravings by analyzing smoking frequency by time of day. Smoking frequency data is aggregated by time of day to understand the strength and frequency of smoking cravings at specific time periods. This allows the analytics department to quickly and accurately analyze collected data, enabling real-time understanding of user stress levels and smoking craving patterns. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past stress level and smoking frequency data, it can predict risk fluctuations over a specific period and formulate future countermeasures. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0062] The support department provides support through medical and psychological approaches based on the analysis results obtained by the analysis department. For example, the support department can offer medical approaches such as pharmacotherapy and exercise therapy. In pharmacotherapy, users are provided with smoking cessation aids such as nicotine patches and nicotine gum to reduce cravings. In exercise therapy, moderate exercise is recommended to reduce stress and increase the success rate of quitting smoking. Furthermore, the support department can also offer psychological approaches such as counseling and cognitive behavioral therapy. In counseling, professional counselors engage with users to identify the causes of smoking and stressors and propose appropriate countermeasures. In cognitive behavioral therapy, users are instructed on specific methods to change their thought and behavior patterns, and to acquire techniques to control their cravings. The support department also provides advice on relieving or avoiding physical stress. For example, the support department can suggest methods to reduce stress through deep breathing and light exercise. Deep breathing has a relaxing effect and is easy to practice when feeling stressed. Light exercise promotes the secretion of endorphins and has the effect of improving mood. Furthermore, the support department provides assistance to alleviate or avoid mental stress. For example, they can send encouraging messages or suggest relaxing music and videos. Encouraging messages are important for users to maintain their motivation to quit smoking, and relaxing music and videos are effective means of reducing stress and promoting relaxation. In addition, the support department can provide coupons usable at nearby stores or news of interest to curb smoking urges. Coupons serve as an incentive for users to avoid smoking, and news of interest serves as a distraction to forget smoking urges. In this way, the support department can provide users with multifaceted support to help them successfully quit smoking.

[0063] The data collection unit can collect physiological and behavioral data using wearable devices and smartphones. For example, the data collection unit can monitor heart rate and activity levels using a smartwatch. The data collection unit can also collect location information using the GPS function of a smartphone. The data collection unit can also record exercise levels using a fitness tracker. This allows for the efficient collection of user physiological and behavioral data using wearable devices and smartphones. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data acquired from a smartwatch into a generating AI and have the generating AI perform analysis of the heart rate data.

[0064] The analysis unit can analyze the user's stress level and smoking craving patterns based on the collected data. For example, the analysis unit can assess stress levels by analyzing heart rate variability. The analysis unit can also assess stress levels using self-report questionnaires. The analysis unit can also identify smoking craving patterns by analyzing smoking frequency by time of day. This allows for the provision of appropriate support by analyzing stress levels and smoking craving patterns based on the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of stress levels and smoking craving patterns.

[0065] The support unit can provide advice on relieving or avoiding physical stress through a medical approach. For example, the support unit may suggest ways to reduce stress, such as deep breathing or light exercise. The support unit can also automate tasks such as scheduling online consultations with doctors or arranging for the delivery of medications. This reduces the user's physical stress by providing advice on relieving or avoiding physical stress through a medical approach. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's stress level data into a generative AI and have the generative AI generate advice for stress reduction.

[0066] The support department can provide support to relieve or avoid mental stress through psychological approaches. For example, the support department can send encouraging messages. For example, the support department can suggest relaxing music or videos. For example, the support department can provide counseling or cognitive behavioral therapy. In this way, the user's mental stress can be reduced by providing support to relieve or avoid mental stress through psychological approaches. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the user's mental stress data into a generative AI and have the generative AI perform stress relief support.

[0067] The support unit can provide coupons usable at nearby stores and news of interest to help curb smoking urges. For example, the support unit can provide discount coupons usable at nearby stores. The support unit can also provide news feeds based on the user's interests. The support unit can also suggest relaxation techniques to help curb smoking urges. In this way, the user's smoking urge can be reduced by providing coupons usable at nearby stores and news of interest to help curb smoking urges. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's smoking urge data into a generative AI and have the generative AI provide coupons and news to help curb smoking urges.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it later. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using devices that the user has preferred to use in the past. The data collection unit may also suggest the optimal data collection method based on the types of data the user has collected in the past. The data collection unit may also predict and suggest the optimal collection timing based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0070] The data collection unit can filter data based on the user's current lifestyle and behavioral patterns. For example, the unit can refrain from collecting data when the user is working and collect it during breaks. For example, the unit can prioritize collecting exercise data when the user is exercising. For example, the unit can collect sleep data and refrain from collecting other data when the user is sleeping. This allows for the collection of appropriate data by filtering based on the user's current lifestyle and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering of data collection.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit may also prioritize collecting relaxation-related data. For example, if the user is in a hurry, the data collection unit may also prioritize collecting data related to urgent actions. This allows for the priority collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, if the user is on the move, the data collection unit can also prioritize the collection of data related to movement. For example, if the user is at home, the data collection unit can also prioritize the collection of data related to activities at home. This allows for the collection of appropriate data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0073] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is experiencing stress on social media, the data collection unit can collect data related to that stress. For example, if a user is relaxing on social media, the data collection unit can also collect data related to that relaxation. For example, if a user is in a hurry on social media, the data collection unit can also collect data related to that urgency. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing stress-related data. For example, if the user is relaxed, the analysis unit may prioritize analyzing relaxation-related data. For example, if the user is in a hurry, the analysis unit may prioritize analyzing data related to hurried behavior. This allows for appropriate analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0075] The analysis unit can analyze patterns of stress levels and smoking cravings at specific times and locations during data analysis. For example, if a user experiences stress at a particular time, the analysis unit will prioritize analyzing data from that time period. For example, if a user experiences stress at a particular location, the analysis unit can also prioritize analyzing data from that location. For example, if a user experiences increased smoking cravings at a particular time or location, the analysis unit can also prioritize analyzing data from that location. By analyzing patterns of stress levels and smoking cravings at specific times and locations, appropriate countermeasures can be taken. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data from specific times and locations into a generative AI and have the generative AI perform the analysis of stress levels and smoking craving patterns.

[0076] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior data during data analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past behavior data. The analysis unit can also extract specific patterns from the user's past behavior data and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis algorithm by referring to the user's past behavior data. In this way, by referring to the user's past behavior data, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past behavior data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0078] The analysis unit can perform data analysis while considering the geographical distribution of users. For example, if a user is in a specific region, the analysis unit will prioritize analyzing data for that region. For example, if a user is on the move, the analysis unit can prioritize analyzing data related to movement. For example, if a user is at home, the analysis unit can prioritize analyzing data related to activities at home. By performing analysis while considering the geographical distribution of users, it is possible to understand region-specific stressors and patterns of smoking cravings. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user geographical distribution data into a generative AI and have the generative AI perform an analysis of region-specific stressors and patterns of smoking cravings.

[0079] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during data analysis. For example, the analysis unit can improve the accuracy of its analysis algorithms by referring to relevant literature. The analysis unit can also improve the reliability of its analysis results by referring to relevant research data. The analysis unit can also optimize its analysis methods based on relevant literature and research data. This allows the analysis to improve accuracy by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input relevant literature and research data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0080] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is feeling stressed, the support unit can provide advice for stress relief. For example, if the user is relaxed, the support unit can also provide support to help them maintain that relaxation. For example, if the user is in a hurry, the support unit can provide support methods that allow for a quick response. This allows for more appropriate support to be provided by adjusting the support methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support methods.

[0081] The support unit can select the optimal support method by referring to the user's past stress levels and smoking craving patterns during support. For example, the support unit can select the optimal support method based on the user's past stress levels. The support unit can also select the optimal support method based on the user's past smoking craving patterns. The support unit can also improve the accuracy of the support method by referring to the user's past data. This allows the optimal support method to be selected by referring to the user's past stress levels and smoking craving patterns. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input past stress level and smoking craving data into a generative AI and have the generative AI select the optimal support method.

[0082] The support unit can customize the means of support based on the user's current living situation when providing assistance. For example, if the user is at work, the support unit can provide support methods that do not interfere with their work. For example, if the user is exercising, the support unit can also provide support methods related to exercise. For example, if the user is sleeping, the support unit can also provide support methods that do not disturb their sleep. By customizing the means of support based on the user's current living situation, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the support means.

[0083] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize stress relief support. For example, if the user is relaxed, the support unit may also prioritize support to maintain that relaxation. For example, if the user is in a hurry, the support unit may also prioritize support that can be provided quickly. This allows for the priority provision of important support by determining the priority of support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of support.

[0084] The support unit can select the optimal support method when providing assistance, taking into account the user's geographical location information. For example, if the user is in a specific location, the support unit can provide support methods related to that location. For example, if the user is on the move, the support unit can also provide support methods related to movement. For example, if the user is at home, the support unit can also provide support methods related to activities at home. By selecting the optimal support method while considering the user's geographical location information, appropriate support can be provided. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the support unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal support method.

[0085] The support unit can analyze a user's social media activity and propose support measures when providing assistance. For example, if a user is experiencing stress on social media, the support unit can provide support methods related to that stress. For example, if a user is relaxing on social media, the support unit can also provide support methods related to that relaxation. For example, if a user is in a hurry on social media, the support unit can also provide support methods related to that urgency. In this way, by analyzing a user's social media activity, the support unit can propose relevant support measures. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's social media activity data into a generative AI and have the generative AI propose support measures.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The smoking cessation support system can also include a reward system. This reward system provides rewards to users when they successfully quit smoking. For example, the reward system could award points to users who remain smoke-free for a certain period. It could also offer special benefits to users upon successful smoking cessation. Furthermore, the reward system could provide regular rewards to maintain user motivation to continue abstaining from smoking. This helps users stay motivated to quit smoking.

[0088] The smoking cessation support system can also include a community section. This community section provides a platform for users to interact with other people trying to quit smoking. For example, it could offer a forum where users can share their experiences and advice on quitting smoking. It could also provide a Q&A section where users can post questions about quitting smoking and receive answers from other users. Furthermore, the community section could form support groups to help users stay smoke-free. This allows users to interact with other people trying to quit smoking and receive support to help them continue their abstinence.

[0089] The smoking cessation support system can also include a gaming section. This section provides games to help users stay smoke-free in an enjoyable way. For example, the gaming section could offer games where users earn points for staying smoke-free and can use those points to purchase items within the game. It could also offer games where users level up and earn rewards for staying smoke-free. Furthermore, the gaming section could offer games where users compete with other smokers, increasing their motivation to continue abstaining. This makes quitting smoking more enjoyable for users.

[0090] The smoking cessation support system can also include a feedback unit. This unit provides feedback to help users stay smoke-free. For example, it can periodically report the health benefits the user gains from remaining smoke-free. It can also calculate and report the amount of money the user saves by staying smoke-free. Furthermore, it can report the social benefits the user gains from remaining smoke-free. This provides users with the motivation to continue quitting smoking.

[0091] The smoking cessation support system can also include a customization section. This customization section provides smoking cessation support plans tailored to the user's individual needs. For example, it can propose an optimal smoking cessation support plan based on the user's smoking history and health condition. Furthermore, it can adjust the plan according to the user's lifestyle and preferences. In addition, the customization section can update the smoking cessation support plan based on the user's progress. This ensures that the user receives the smoking cessation support plan best suited to them.

[0092] A smoking cessation support system can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is feeling stressed, it can provide relaxing music or videos. If the user is feeling down, it can send encouraging messages. Furthermore, if the user is happy, it can provide positive feedback to help maintain that feeling. This allows for smoking cessation support tailored to the user's emotions.

[0093] The smoking cessation support system can estimate the user's emotions and adjust the timing of smoking cessation support based on those emotions. For example, if the user is feeling stressed, the frequency of smoking cessation support can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of smoking cessation support can be increased, and more detailed support can be provided. Furthermore, if the user is in a hurry, smoking cessation support can be temporarily suspended and resumed later. In this way, the system can reduce the user's burden by adjusting the timing of smoking cessation support based on the user's emotions.

[0094] The smoking cessation support system can estimate the user's emotions and customize the support based on those emotions. For example, if the user is feeling stressed, it can provide advice on stress relief. If the user is relaxed, it can provide support to help them maintain that relaxation. Furthermore, if the user is in a hurry, it can provide support methods that allow for a quick response. In this way, by customizing the smoking cessation support based on the user's emotions, more appropriate support can be provided.

[0095] The smoking cessation support system can estimate the user's emotions and prioritize smoking cessation support based on those emotions. For example, if the user is feeling stressed, stress relief support can be prioritized. If the user is relaxed, support to maintain that relaxation can be prioritized. Furthermore, if the user is in a hurry, support that can be provided quickly can be prioritized. In this way, by prioritizing smoking cessation support based on the user's emotions, important support can be provided preferentially.

[0096] A smoking cessation support system can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is feeling stressed, it can provide advice on stress relief. If the user is relaxed, it can provide support to help them maintain that relaxation. Furthermore, if the user is in a hurry, it can provide support methods that allow for a quick response. By adjusting the smoking cessation support methods based on the user's emotions, it can provide more appropriate support.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects the user's physiological and behavioral data. The data collection unit can collect physiological data such as heart rate, blood pressure, and body temperature, as well as behavioral data such as steps taken, sleep patterns, and exercise levels. The data collection unit collects data using wearable devices and smartphones. For example, it can monitor heart rate and activity levels using a smartwatch, collect location information using the GPS function of a smartphone, and record exercise levels using a fitness tracker. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. Based on the collected data, the analysis unit analyzes the user's stress level and smoking craving patterns. For example, it can assess stress levels by analyzing heart rate variability and using self-report questionnaires. It can also identify patterns of smoking cravings by analyzing smoking frequency by time of day. Step 3: The support department provides support through medical and psychological approaches based on the analysis results obtained by the analysis department. The support department can provide medical approaches such as pharmacotherapy and exercise therapy. It can also provide psychological approaches such as counseling and cognitive behavioral therapy. The support department provides advice on how to relieve or avoid physical stress and provides support on how to relieve or avoid mental stress. For example, it can suggest ways to reduce stress, such as deep breathing or light exercise, send encouraging messages, or suggest relaxing music or videos. Furthermore, to curb smoking urges, it can provide coupons usable at nearby stores or news of interest.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as heart rate, activity level, and location information using the sensors and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the user's stress level and smoking craving patterns based on the collected data. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides support through medical and psychological approaches. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as heart rate, activity level, and location information using the sensors and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the user's stress level and smoking craving patterns based on the collected data. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides support through medical and psychological approaches. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data such as heart rate, activity level, and location information using the sensors and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the user's stress level and smoking craving patterns based on the collected data. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides support through medical and psychological approaches. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as heart rate, activity level, and location information using the sensors and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the user's stress level and smoking craving patterns based on the collected data. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides support through medical and psychological approaches. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A data collection unit that collects user physiological and behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the support unit provides support through medical and psychological approaches. Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect physiological and behavioral data using wearable devices and smartphones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Based on the collected data, the system analyzes users' stress levels and patterns of smoking cravings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, We provide advice on how to relieve or avoid physical stress through a medical approach. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, We provide support to alleviate or avoid mental stress through psychological approaches. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, To curb the urge to smoke, we offer coupons usable at nearby stores and news of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During data analysis, patterns of stress levels and smoking cravings are analyzed at specific times of day and locations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing data, we optimize the analysis algorithm by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When performing data analysis, consider the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing data, referencing relevant literature and research data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit, It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit, During support, the system selects the optimal support method by referring to the user's past stress levels and patterns of smoking cravings. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit, During support, the means of assistance are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit, When providing support, the optimal support method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, When providing support, we analyze the user's social media activity and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user physiological and behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the support unit provides support through medical and psychological approaches. Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect physiological and behavioral data using wearable devices and smartphones. The system according to feature 1.

3. The aforementioned analysis unit is Based on the collected data, the system analyzes users' stress levels and patterns of smoking cravings. The system according to feature 1.

4. The aforementioned support unit, We provide advice on how to relieve or avoid physical stress through a medical approach. The system according to feature 1.

5. The aforementioned support unit, We provide support to alleviate or avoid mental stress through psychological approaches. The system according to feature 1.

6. The aforementioned support unit, To curb the urge to smoke, we offer coupons usable at nearby stores and news of interest. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

Images