System

Abstract

An object of a system according to an embodiment is to provide smoking cessation support customized in accordance with the needs and situation of a smoker.SOLUTION: A system according to an embodiment includes a need analysis unit, a support plan generation unit, and a support execution unit. The needs analysis unit analyzes the needs and situation of the smoker. The support plan generator generates a customized non-smoking support plan based on the result analyzed by the needs analyzer. The support executing section provides specific support to the smoker based on the non-smoking support plan generated by the support plan generating section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide smoking cessation support tailored to individual smokers' needs and circumstances, and there is room for improvement.

[0005] The system according to the embodiment aims to provide smoking cessation support that is customized to the smoker's needs and circumstances. [Means for solving the problem]

[0006] The system according to the embodiment includes a needs analysis unit, a support plan generation unit, and a support implementation unit. The needs analysis unit analyzes the needs and situation of a smoker. The support plan generation unit generates a customized smoking cessation support plan based on the results of the analysis by the needs analysis unit. The support implementation unit provides specific support to the smoker based on the smoking cessation support plan generated by the support plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized smoking cessation support according to the smoker's needs and circumstances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The smoking cessation support platform according to an embodiment of the present invention is a system that utilizes generative AI to provide a mechanism for smokers to quit smoking without having to endure it. This system analyzes the needs and circumstances of smokers and provides customized smoking cessation support based on those findings. This allows the smoking cessation support platform to provide a mechanism for smokers to quit smoking without having to endure it.

[0029] A smoking cessation support platform according to an embodiment includes a needs analysis unit, a support plan generation unit, and a support implementation unit. The needs analysis unit analyzes a smoker's needs and situation. For example, the needs analysis unit collects and analyzes the smoker's smoking habits, stress level, and history of past quit attempts. The needs analysis unit can also analyze the smoker's needs and situation based on information provided by the smoker. For example, the needs analysis unit performs analysis based on prompts, including information and answers to questions, provided by the smoker to the generation AI. The support plan generation unit generates a customized smoking cessation support plan based on the results of the analysis by the needs analysis unit. For example, the support plan generation unit may suggest alternative actions when the smoker feels stressed or send messages to maintain the smoker's motivation to continue quitting smoking. The support plan generation unit can also monitor the smoker's progress and adjust the support plan as needed. For example, the support plan generation unit generates and adjusts the support plan based on prompts including the smoker's progress and feedback. The support implementation unit provides specific support to the smoker based on the smoking cessation support plan generated by the support plan generation unit. For example, the support implementation unit may suggest relaxation methods when a smoker feels the urge to smoke, or may increase motivation by sharing successful experiences of quitting smoking. The support implementation unit may also recommend that smokers join support groups or communities to help them continue quitting smoking. For example, the support implementation unit provides support based on prompts that include the smoker's feedback and the effectiveness of the support. This allows the smoking cessation support platform according to the embodiment to provide a mechanism that allows smokers to quit smoking without having to endure it. For example, by suggesting appropriate alternative behaviors when a smoker feels stressed, the urge to smoke can be suppressed. Furthermore, by monitoring the progress of smoking cessation and adjusting the support plan as needed, it becomes easier for smokers to continue quitting smoking. Furthermore, by measuring the effectiveness of smoking cessation support and providing feedback to smokers, the success rate of quitting smoking can be increased.

[0030] The needs analysis unit collects data on a smoker's lifestyle patterns and daily behavior to perform a more detailed needs analysis. For example, to analyze a smoker's lifestyle patterns, the needs analysis unit collects daily movement history using GPS data from a smartphone. For example, it analyzes commuting routes and places visited to identify situations that trigger smoking. The needs analysis unit also uses the activity tracking function of a smartwatch to collect data on a smoker's daily behavior. For example, it analyzes exercise volume and sleep patterns to evaluate the impact of stress and fatigue on smoking. The needs analysis unit also collects data from smart home devices to analyze a smoker's lifestyle patterns in detail. For example, it analyzes home appliance usage and indoor environment data to identify factors that trigger smoking. By collecting data on a smoker's lifestyle patterns and behavior, a more detailed needs analysis becomes possible.

[0031] The needs analysis unit incorporates feedback from family and friends into the smoker's needs analysis, enabling more comprehensive support to be provided. For example, the needs analysis unit collects opinions from family and friends through a dedicated app to incorporate feedback from family and friends into the smoker's needs analysis. For example, family and friends report changes in the smoker's behavior and emotions. The needs analysis unit also conducts regular surveys to incorporate feedback from family and friends into the smoker's needs analysis. For example, it collects information from family and friends about the smoker's stress and triggers for smoking. The needs analysis unit also utilizes online forums to incorporate feedback from family and friends into the smoker's needs analysis. For example, family and friends share opinions and advice about supporting the smoker. This allows for more comprehensive support by incorporating feedback from family and friends.

[0032] The needs analysis unit can integrate past medical data and health checkup results into the smoker's needs analysis and provide support that takes health risks into consideration. The needs analysis unit, for example, works with an electronic medical record system to integrate past medical data into the smoker's needs analysis. For example, it analyzes past diagnosis results and prescription history and provides support that takes health risks into consideration. The needs analysis unit also collects regular health checkup data to integrate health checkup results into the smoker's needs analysis. For example, it analyzes blood pressure and cholesterol levels and provides support that takes health risks into consideration. The needs analysis unit also builds a data sharing system with medical institutions to integrate past medical data into the smoker's needs analysis. For example, it analyzes the smoker's medical history and allergy information and provides support that takes health risks into consideration. In this way, by integrating past medical data and health checkup results, support that takes health risks into consideration is possible.

[0033] The support plan generation unit can analyze data on a smoker's past attempts to quit smoking, identify factors that contributed to success and failure, and reflect this in the support plan. The support plan generation unit, for example, works with an electronic medical record system to analyze data on a smoker's past attempts to quit smoking. For example, it analyzes past cases of successful and unsuccessful quitting and identifies factors that contributed to success and failure. The support plan generation unit also collects historical data from a smoking cessation app to analyze data on a smoker's past attempts to quit smoking. For example, it analyzes progress in quitting smoking and frequency of cravings to identify factors that contributed to success and failure. The support plan generation unit also conducts a questionnaire survey to analyze data on a smoker's past attempts to quit smoking. For example, it collects information on motivations for quitting smoking and support methods, and identifies factors that contributed to success and failure. In this way, by analyzing data on past attempts to quit smoking, it is possible to identify factors that contributed to success and failure and reflect this in the support plan.

[0034] The support plan generation unit can incorporate lifestyle improvement elements such as diet and exercise into the smoker's support plan. For example, the support plan generation unit incorporates advice from a nutritionist to incorporate dietary improvement elements into the smoker's support plan. For example, it may suggest a meal menu suitable for smoking cessation. The support plan generation unit may also incorporate advice from a fitness trainer to incorporate exercise elements into the smoker's support plan. For example, it may suggest an exercise program that is effective for relieving stress. The support plan generation unit may also incorporate advice from a sleep expert to incorporate lifestyle improvement elements into the smoker's support plan. For example, it may suggest a method for promoting good quality sleep while quitting smoking. In this way, by incorporating lifestyle improvement elements such as diet and exercise, more comprehensive support is possible.

[0035] The support plan generation unit can incorporate a relaxation session using virtual reality into the support plan for the smoker. For example, the support plan generation unit uses a VR headset to incorporate a relaxation session using VR into the support plan for the smoker. For example, the support plan generation unit allows the smoker to experience a relaxing natural landscape. In addition, the support plan generation unit provides a meditation or yoga session to incorporate a relaxation session using VR into the support plan for the smoker. For example, the support plan generation unit allows the smoker to experience a guided meditation through VR. In addition, the support plan generation unit provides a stress relief game to incorporate a relaxation session using VR into the support plan for the smoker. For example, the support plan generation unit allows the smoker to experience a relaxing game through VR. In this way, more effective support can be provided by incorporating a relaxation session using virtual reality (VR).

[0036] The support implementation unit can track the smoker's progress in detail and provide individualized feedback. For example, the support implementation unit uses a smoking cessation app to record the smoker's daily smoking status in order to track the smoker's progress in detail. For example, the support implementation unit tracks the number of cigarettes smoked and the frequency of cravings and provides individualized feedback. The support implementation unit also uses the activity tracking function of a smartwatch to track the smoker's progress in detail. For example, the support implementation unit tracks the amount of exercise and sleep patterns and provides individualized feedback. The support implementation unit also conducts regular surveys to track the smoker's progress in detail. For example, the support implementation unit asks questions about the progress of smoking cessation and changes in cravings and provides individualized feedback. This makes it possible to provide more effective support by tracking the smoker's progress in detail and providing individualized feedback.

[0037] The support implementation department can incorporate the healing elements of pets and plants into the support for smokers. For example, to incorporate pets into the support for smokers, the support implementation department recommends pet care. For example, by suggesting dog walks or cat playtime to help relieve stress. Furthermore, to incorporate plants into the support for smokers, the support implementation department recommends gardening. For example, by suggesting growing indoor plants and maintaining a garden to promote relaxation. Furthermore, to incorporate healing elements into the support for smokers, the support implementation department recommends community activities related to pets and plants. For example, by suggesting joining a pet lover's group or a gardening club. In this way, by incorporating healing elements such as pets and plants, it is possible to reduce stress for smokers and support them in quitting smoking.

[0038] The support implementation unit can incorporate music therapy and art therapy into support for smokers. For example, to incorporate music therapy into support for smokers, the support implementation unit provides a relaxing music playlist. For example, classical music or natural sounds that are effective for relieving stress may be suggested. In addition, to incorporate art therapy into support for smokers, the support implementation unit recommends painting or craft activities. For example, painting or creating handmade crafts can promote relaxation. In addition, to incorporate music therapy into support for smokers, the support implementation unit provides online music sessions. For example, it suggests meditation sessions while listening to relaxing music. In this way, by incorporating music therapy or art therapy, smokers can reduce stress and be supported in quitting smoking.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The smoking cessation support platform can further include a reward provider. The reward provider can provide a reward when a smoker achieves their goal of quitting smoking. For example, the reward provider can award points when a smoker continues to quit smoking for a certain period of time and allow the points to be exchanged for goods or services. The reward provider can also provide encouraging messages or badges each time a smoker reports their progress in quitting smoking. Furthermore, the reward provider can periodically offer luxurious prizes in a lottery to increase the motivation of smokers to continue quitting smoking. Thus, the provision of the reward provider can increase smokers' motivation to quit smoking and improve the success rate of quitting smoking.

[0041] The smoking cessation support platform may further include a health monitoring unit. The health monitoring unit may periodically check the smoker's health status and detect health risks early. For example, the health monitoring unit may use a smartwatch or fitness tracker to monitor vital signs such as heart rate, blood pressure, and oxygen saturation. The health monitoring unit may also send reminders to the smoker to undergo regular health checkups and analyze the health checkup results to assess health risks. Furthermore, the health monitoring unit may provide advice to the smoker to maintain a healthy lifestyle. Thus, the health monitoring unit may continuously manage the smoker's health status and maximize the effectiveness of smoking cessation.

[0042] A smoking cessation support platform can also have a communications department. This department can provide a forum where smokers can interact with other quitters and supporters. For example, the communications department can encourage smokers to share their quitting experiences and advice through online forums and chat rooms. The communications department can also hold regular online meetings and webinars to provide advice and support from experts. Furthermore, the communications department can encourage smokers to join local smoking cessation support groups and promote actual interactions. Thus, having a communications department makes it easier for smokers to receive support to continue quitting smoking without feeling isolated.

[0043] A smoking cessation support platform can also have an education department. The education department can provide smokers with knowledge and skills related to quitting smoking. For example, the education department can offer online courses that provide information about the benefits of quitting smoking and the health risks of smoking. The education department can also hold workshops where smokers can learn techniques and strategies to successfully quit smoking. Furthermore, the education department can run newsletters and blogs to keep smokers updated with the latest research and information on smoking cessation. Thus, having an education department can help smokers deepen their knowledge about quitting smoking and make their smoking cessation more effective.

[0044] The smoking cessation support platform may further include a feedback unit. The feedback unit may collect feedback from smokers and use it to improve the smoking cessation support platform. For example, the feedback unit may periodically conduct a survey in which smokers can provide their opinions on the usability and effectiveness of the smoking cessation support platform. The feedback unit may also provide an online form in which smokers can suggest specific improvements and new features. Furthermore, the feedback unit may continuously improve the functions and services of the smoking cessation support platform based on feedback from smokers and provide more effective support. Thus, the provision of a feedback unit enables flexible responses to meet the needs of smokers.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The needs analysis section analyzes the smoker's needs and situation. For example, it collects and analyzes the smoker's smoking habits, stress level, and history of past quit attempts. It also analyzes the information provided by the smoker and their answers to questions. Step 2: The support plan generator generates a customized smoking cessation support plan based on the results of the needs analysis. For example, it may suggest alternative actions when the smoker feels stressed or send messages to maintain motivation to continue quitting smoking. It also monitors the smoker's progress and adjusts the support plan as needed. Step 3: The support implementation unit provides specific support to smokers based on the smoking cessation support plan generated by the support plan generation unit. For example, the support implementation unit suggests ways to relax when smokers feel the urge to smoke, or motivates smokers by sharing their successful experiences of quitting smoking. The support implementation unit can also recommend participation in support groups or communities.

[0047] (Example 2) The smoking cessation support platform according to an embodiment of the present invention is a system that utilizes generative AI to provide a mechanism for smokers to quit smoking without having to endure it. This system analyzes the needs and circumstances of smokers and provides customized smoking cessation support based on those findings. This allows the smoking cessation support platform to provide a mechanism for smokers to quit smoking without having to endure it.

[0048] A smoking cessation support platform according to an embodiment includes a needs analysis unit, a support plan generation unit, and a support implementation unit. The needs analysis unit analyzes a smoker's needs and situation. For example, the needs analysis unit collects and analyzes the smoker's smoking habits, stress level, and history of past quit attempts. The needs analysis unit can also analyze the smoker's needs and situation based on information provided by the smoker. For example, the needs analysis unit performs analysis based on prompts, including information and answers to questions, provided by the smoker to the generation AI. The support plan generation unit generates a customized smoking cessation support plan based on the results of the analysis by the needs analysis unit. For example, the support plan generation unit may suggest alternative actions when the smoker feels stressed or send messages to maintain the smoker's motivation to continue quitting smoking. The support plan generation unit can also monitor the smoker's progress and adjust the support plan as needed. For example, the support plan generation unit generates and adjusts the support plan based on prompts including the smoker's progress and feedback. The support implementation unit provides specific support to the smoker based on the smoking cessation support plan generated by the support plan generation unit. For example, the support implementation unit may suggest relaxation methods when a smoker feels the urge to smoke, or may increase motivation by sharing successful experiences of quitting smoking. The support implementation unit may also recommend that smokers join support groups or communities to help them continue quitting smoking. For example, the support implementation unit provides support based on prompts that include the smoker's feedback and the effectiveness of the support. This allows the smoking cessation support platform according to the embodiment to provide a mechanism that allows smokers to quit smoking without having to endure it. For example, by suggesting appropriate alternative behaviors when a smoker feels stressed, the urge to smoke can be suppressed. Furthermore, by monitoring the progress of smoking cessation and adjusting the support plan as needed, it becomes easier for smokers to continue quitting smoking. Furthermore, by measuring the effectiveness of smoking cessation support and providing feedback to smokers, the success rate of quitting smoking can be increased.

[0049] The needs analysis unit can analyze the smoker's emotional state in real time and dynamically update the needs and situation in response to emotional fluctuations. For example, the needs analysis unit monitors the smoker's heart rate and electrodermal activity using sensors in a smartwatch or smartphone to analyze the smoker's emotional state in real time. This detects stress and anxiety levels and dynamically updates the needs and situation. To analyze the smoker's emotional state, the needs analysis unit also uses facial recognition technology to analyze facial expressions in real time and detect emotional fluctuations. For example, the smoker's facial expressions are captured using a camera, and the needs and situation are updated in response to emotional fluctuations. To analyze the smoker's emotional state in real time, the needs analysis unit also uses voice analysis technology to analyze the smoker's tone of voice and speaking style. For example, voice data is collected using a smartphone microphone, and the needs and situation are dynamically updated in response to emotional fluctuations. This allows the needs and situation to be dynamically updated in response to the smoker's emotional state, thereby providing more appropriate support.

[0050] The needs analysis unit collects data on a smoker's lifestyle patterns and daily behavior to perform a more detailed needs analysis. For example, to analyze a smoker's lifestyle patterns, the needs analysis unit collects daily movement history using GPS data from a smartphone. For example, it analyzes commuting routes and places visited to identify situations that trigger smoking. The needs analysis unit also uses the activity tracking function of a smartwatch to collect data on a smoker's daily behavior. For example, it analyzes exercise volume and sleep patterns to evaluate the impact of stress and fatigue on smoking. The needs analysis unit also collects data from smart home devices to analyze a smoker's lifestyle patterns in detail. For example, it analyzes home appliance usage and indoor environment data to identify factors that trigger smoking. By collecting data on a smoker's lifestyle patterns and behavior, a more detailed needs analysis becomes possible.

[0051] The needs analysis unit can use the emotion estimation function to analyze the smoker's stress level and emotional state and identify needs based on the analysis. For example, the needs analysis unit uses the emotion estimation function to monitor the smoker's heart rate and electrodermal activity to analyze the smoker's stress level. For example, it can identify that the need for smoking increases when stress increases. The needs analysis unit can also use the emotion estimation function to use facial recognition technology to analyze the smoker's emotional state. For example, it can analyze changes in facial expressions in real time to identify the need for smoking based on the emotional state. The needs analysis unit can also use the emotion estimation function to use voice analysis technology to analyze the smoker's emotional state. For example, it can analyze the tone of voice and speaking style to identify the need for smoking based on the emotional state. In this way, the emotion estimation function can be used to identify needs based on the smoker's stress level and emotional state.

[0052] The needs analysis unit incorporates feedback from family and friends into the smoker's needs analysis, enabling more comprehensive support to be provided. For example, the needs analysis unit collects opinions from family and friends through a dedicated app to incorporate feedback from family and friends into the smoker's needs analysis. For example, family and friends report changes in the smoker's behavior and emotions. The needs analysis unit also conducts regular surveys to incorporate feedback from family and friends into the smoker's needs analysis. For example, it collects information from family and friends about the smoker's stress and triggers for smoking. The needs analysis unit also utilizes online forums to incorporate feedback from family and friends into the smoker's needs analysis. For example, family and friends share opinions and advice about supporting the smoker. This allows for more comprehensive support by incorporating feedback from family and friends.

[0053] The needs analysis unit can integrate past medical data and health checkup results into the smoker's needs analysis and provide support that takes health risks into consideration. The needs analysis unit, for example, works with an electronic medical record system to integrate past medical data into the smoker's needs analysis. For example, it analyzes past diagnosis results and prescription history and provides support that takes health risks into consideration. The needs analysis unit also collects regular health checkup data to integrate health checkup results into the smoker's needs analysis. For example, it analyzes blood pressure and cholesterol levels and provides support that takes health risks into consideration. The needs analysis unit also builds a data sharing system with medical institutions to integrate past medical data into the smoker's needs analysis. For example, it analyzes the smoker's medical history and allergy information and provides support that takes health risks into consideration. In this way, by integrating past medical data and health checkup results, support that takes health risks into consideration is possible.

[0054] The needs analysis unit can use the emotion estimation function to analyze the emotional state of the smoker and perform emotion-based needs analysis. The needs analysis unit, for example, uses face recognition technology to analyze the emotional state of the smoker using the emotion estimation function. For example, it analyzes changes in facial expressions in real time to perform emotion-based needs analysis. The needs analysis unit also uses voice analysis technology to analyze the emotional state of the smoker using the emotion estimation function. For example, it analyzes tone of voice and speaking style to perform emotion-based needs analysis. The needs analysis unit also uses the smartwatch sensor to analyze the emotional state of the smoker using the emotion estimation function. For example, it monitors heart rate and electrodermal activity to perform emotion-based needs analysis. As a result, the emotion estimation function enables needs analysis based on the emotional state of the smoker.

[0055] The support plan generation unit can adjust the support plan in real time according to the smoker's emotional state and provide optimal support. For example, the support plan generation unit monitors the heart rate and electrodermal activity using a sensor in the smartwatch to adjust the support plan according to the smoker's emotional state. For example, it suggests relaxation methods when stress increases. The support plan generation unit also analyzes facial expressions in real time using facial recognition technology to adjust the support plan according to the smoker's emotional state. For example, it sends motivational messages according to emotional fluctuations. The support plan generation unit also analyzes the tone of voice and speaking style using voice analysis technology to adjust the support plan according to the smoker's emotional state. For example, it suggests alternative actions according to emotional fluctuations. In this way, optimal support can be provided by adjusting the support plan in real time according to the smoker's emotional state.

[0056] The support plan generation unit can analyze data on a smoker's past attempts to quit smoking, identify factors that contributed to success and failure, and reflect this in the support plan. The support plan generation unit, for example, works with an electronic medical record system to analyze data on a smoker's past attempts to quit smoking. For example, it analyzes past cases of successful and unsuccessful quitting and identifies factors that contributed to success and failure. The support plan generation unit also collects historical data from a smoking cessation app to analyze data on a smoker's past attempts to quit smoking. For example, it analyzes progress in quitting smoking and frequency of cravings to identify factors that contributed to success and failure. The support plan generation unit also conducts a questionnaire survey to analyze data on a smoker's past attempts to quit smoking. For example, it collects information on motivations for quitting smoking and support methods, and identifies factors that contributed to success and failure. In this way, by analyzing data on past attempts to quit smoking, it is possible to identify factors that contributed to success and failure and reflect this in the support plan.

[0057] The support plan generation unit can use the emotion estimation function to suggest motivational messages and alternative actions based on the smoker's emotional state. The support plan generation unit, for example, uses the emotion estimation function to send motivational messages based on the smoker's emotional state. For example, facial recognition technology is used to analyze facial expressions and send positive messages. The support plan generation unit also uses the emotion estimation function to suggest alternative actions based on the smoker's emotional state. For example, voice analysis technology is used to analyze the tone of voice and suggest relaxation methods. The support plan generation unit also uses the emotion estimation function to send motivational messages based on the smoker's emotional state. For example, a smartwatch sensor is used to monitor the smoker's heart rate and send encouraging messages. In this way, the emotion estimation function can be used to suggest motivational messages and alternative actions based on the smoker's emotional state.

[0058] The support plan generation unit can incorporate lifestyle improvement elements such as diet and exercise into the smoker's support plan. For example, the support plan generation unit incorporates advice from a nutritionist to incorporate dietary improvement elements into the smoker's support plan. For example, it may suggest a meal menu suitable for smoking cessation. The support plan generation unit may also incorporate advice from a fitness trainer to incorporate exercise elements into the smoker's support plan. For example, it may suggest an exercise program that is effective for relieving stress. The support plan generation unit may also incorporate advice from a sleep expert to incorporate lifestyle improvement elements into the smoker's support plan. For example, it may suggest a method for promoting good quality sleep while quitting smoking. In this way, by incorporating lifestyle improvement elements such as diet and exercise, more comprehensive support is possible.

[0059] The support plan generation unit can incorporate a relaxation session using virtual reality into the support plan for the smoker. For example, the support plan generation unit uses a VR headset to incorporate a relaxation session using VR into the support plan for the smoker. For example, the support plan generation unit allows the smoker to experience a relaxing natural landscape. In addition, the support plan generation unit provides a meditation or yoga session to incorporate a relaxation session using VR into the support plan for the smoker. For example, the support plan generation unit allows the smoker to experience a guided meditation through VR. In addition, the support plan generation unit provides a stress relief game to incorporate a relaxation session using VR into the support plan for the smoker. For example, the support plan generation unit allows the smoker to experience a relaxing game through VR. In this way, more effective support can be provided by incorporating a relaxation session using virtual reality (VR).

[0060] The support plan generation unit can use the emotion estimation function to generate a support plan that is easy to empathize with emotionally based on the emotional state of the smoker. The support plan generation unit, for example, uses the emotion estimation function to generate a support plan that is easy to empathize with emotionally based on the emotional state of the smoker. For example, facial recognition technology is used to analyze facial expressions and send messages that elicit empathy. The support plan generation unit also uses the emotion estimation function to generate a support plan that is easy to empathize with emotionally based on the emotional state of the smoker. For example, voice analysis technology is used to analyze the tone of voice and send messages that empathize with the emotions. The support plan generation unit also uses the emotion estimation function to generate a support plan that is easy to empathize with emotionally based on the emotional state of the smoker. For example, a smartwatch sensor is used to monitor the heart rate and send messages that empathize with the emotions. In this way, by using the emotion estimation function, a support plan that is easy to empathize with can be generated based on the emotional state of the smoker.

[0061] The support implementation unit can monitor the smoker's emotional state in real time and dynamically adjust the support content according to emotional fluctuations. For example, the support implementation unit measures heart rate and electrodermal activity using a smartwatch sensor to monitor the smoker's emotional state in real time. For example, it suggests relaxation methods when stress increases. The support implementation unit also analyzes facial expressions using facial recognition technology to monitor the smoker's emotional state in real time. For example, it sends motivational messages according to emotional fluctuations. The support implementation unit also analyzes voice tone and speaking style using voice analysis technology to monitor the smoker's emotional state in real time. For example, it suggests alternative actions according to emotional fluctuations. This enables more effective support by monitoring the smoker's emotional state in real time and dynamically adjusting the support content.

[0062] The support implementation unit can track the smoker's progress in detail and provide individualized feedback. For example, the support implementation unit uses a smoking cessation app to record the smoker's daily smoking status in order to track the smoker's progress in detail. For example, the support implementation unit tracks the number of cigarettes smoked and the frequency of cravings and provides individualized feedback. The support implementation unit also uses the activity tracking function of a smartwatch to track the smoker's progress in detail. For example, the support implementation unit tracks the amount of exercise and sleep patterns and provides individualized feedback. The support implementation unit also conducts regular surveys to track the smoker's progress in detail. For example, the support implementation unit asks questions about the progress of smoking cessation and changes in cravings and provides individualized feedback. This makes it possible to provide more effective support by tracking the smoker's progress in detail and providing individualized feedback.

[0063] The support implementation unit can use the emotion estimation function to suggest relaxation methods and methods to increase motivation based on the smoker's emotional state. The support implementation unit, for example, uses the emotion estimation function to suggest relaxation methods based on the smoker's emotional state. For example, facial recognition technology can be used to analyze facial expressions and suggest relaxing music or meditation. The support implementation unit can also use the emotion estimation function to suggest methods to increase motivation based on the smoker's emotional state. For example, voice analysis technology can be used to analyze the tone of voice and send an encouraging message. The support implementation unit can also use the emotion estimation function to suggest relaxation methods based on the smoker's emotional state. For example, a smartwatch sensor can be used to monitor the heart rate and suggest relaxing breathing techniques. In this way, the emotion estimation function can be used to suggest relaxation methods and methods to increase motivation based on the smoker's emotional state.

[0064] The support implementation department can incorporate the healing elements of pets and plants into the support for smokers. For example, to incorporate pets into the support for smokers, the support implementation department recommends pet care. For example, by suggesting dog walks or cat playtime to help relieve stress. Furthermore, to incorporate plants into the support for smokers, the support implementation department recommends gardening. For example, by suggesting growing indoor plants and maintaining a garden to promote relaxation. Furthermore, to incorporate healing elements into the support for smokers, the support implementation department recommends community activities related to pets and plants. For example, by suggesting joining a pet lover's group or a gardening club. In this way, by incorporating healing elements such as pets and plants, it is possible to reduce stress for smokers and support them in quitting smoking.

[0065] The support implementation unit can incorporate music therapy and art therapy into support for smokers. For example, to incorporate music therapy into support for smokers, the support implementation unit provides a relaxing music playlist. For example, classical music or natural sounds that are effective for relieving stress may be suggested. In addition, to incorporate art therapy into support for smokers, the support implementation unit recommends painting or craft activities. For example, painting or creating handmade crafts can promote relaxation. In addition, to incorporate music therapy into support for smokers, the support implementation unit provides online music sessions. For example, it suggests meditation sessions while listening to relaxing music. In this way, by incorporating music therapy or art therapy, smokers can reduce stress and be supported in quitting smoking.

[0066] The support implementation unit can use the emotion estimation function to provide support that is easy to empathize with emotionally based on the smoker's emotional state. The support implementation unit, for example, uses the emotion estimation function to provide support that is easy to empathize with emotionally based on the smoker's emotional state. For example, facial recognition technology is used to analyze facial expressions and send a message that elicits empathy. The support implementation unit also uses the emotion estimation function to provide support that is easy to empathize with emotionally based on the smoker's emotional state. For example, voice analysis technology is used to analyze tone of voice and send a message that is empathetic to the emotion. The support implementation unit also uses the emotion estimation function to provide support that is easy to empathize with emotionally based on the smoker's emotional state. For example, a smartwatch sensor is used to monitor heart rate and send a message that is empathetic to the emotion. In this way, by using the emotion estimation function, it is possible to provide support that is easy to empathize with based on the smoker's emotional state.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The smoking cessation support platform can further include a reward provider. The reward provider can provide a reward when a smoker achieves their goal of quitting smoking. For example, the reward provider can award points when a smoker continues to quit smoking for a certain period of time and allow the points to be exchanged for goods or services. The reward provider can also provide encouraging messages or badges each time a smoker reports their progress in quitting smoking. Furthermore, the reward provider can periodically offer luxurious prizes in a lottery to increase the motivation of smokers to continue quitting smoking. Thus, the provision of the reward provider can increase smokers' motivation to quit smoking and improve the success rate of quitting smoking.

[0069] The smoking cessation support platform may further include a health monitoring unit. The health monitoring unit may periodically check the smoker's health status and detect health risks early. For example, the health monitoring unit may use a smartwatch or fitness tracker to monitor vital signs such as heart rate, blood pressure, and oxygen saturation. The health monitoring unit may also send reminders to the smoker to undergo regular health checkups and analyze the health checkup results to assess health risks. Furthermore, the health monitoring unit may provide advice to the smoker to maintain a healthy lifestyle. Thus, the health monitoring unit may continuously manage the smoker's health status and maximize the effectiveness of smoking cessation.

[0070] A smoking cessation support platform can also have a communications department. This department can provide a forum where smokers can interact with other quitters and supporters. For example, the communications department can encourage smokers to share their quitting experiences and advice through online forums and chat rooms. The communications department can also hold regular online meetings and webinars to provide advice and support from experts. Furthermore, the communications department can encourage smokers to join local smoking cessation support groups and promote actual interactions. Thus, having a communications department makes it easier for smokers to receive support to continue quitting smoking without feeling isolated.

[0071] A smoking cessation support platform can also have an education department. The education department can provide smokers with knowledge and skills related to quitting smoking. For example, the education department can offer online courses that provide information about the benefits of quitting smoking and the health risks of smoking. The education department can also hold workshops where smokers can learn techniques and strategies to successfully quit smoking. Furthermore, the education department can run newsletters and blogs to keep smokers updated with the latest research and information on smoking cessation. Thus, having an education department can help smokers deepen their knowledge about quitting smoking and make their smoking cessation more effective.

[0072] The smoking cessation support platform may further include a feedback unit. The feedback unit may collect feedback from smokers and use it to improve the smoking cessation support platform. For example, the feedback unit may periodically conduct a survey in which smokers can provide their opinions on the usability and effectiveness of the smoking cessation support platform. The feedback unit may also provide an online form in which smokers can suggest specific improvements and new features. Furthermore, the feedback unit may continuously improve the functions and services of the smoking cessation support platform based on feedback from smokers and provide more effective support. Thus, the provision of a feedback unit enables flexible responses to meet the needs of smokers.

[0073] The smoking cessation support platform can further use the emotion estimation function to suggest stress relief methods based on the smoker's emotional state. For example, the emotion estimation function can be used to analyze the smoker's emotional state and suggest relaxing music or meditation when stress levels rise. The emotion estimation function can also be used to analyze the smoker's emotional state and suggest relaxing activities according to emotional fluctuations. The emotion estimation function can also be used to analyze the smoker's emotional state and send messages that are sympathetic to the smoker's emotions. In this way, the emotion estimation function can suggest stress relief methods based on the smoker's emotional state and support the smoker in quitting smoking.

[0074] The smoking cessation support platform can further use the emotion estimation function to suggest ways to increase a smoker's motivation to quit smoking based on the smoker's emotional state. For example, the emotion estimation function can be used to analyze a smoker's emotional state and send an encouraging message when the smoker's emotions drop. The emotion estimation function can also be used to analyze a smoker's emotional state and suggest positive activities according to emotional fluctuations. The emotion estimation function can also be used to analyze a smoker's emotional state and send a message that is sympathetic to the smoker's emotions. In this way, the emotion estimation function can suggest ways to increase a smoker's motivation to quit smoking based on the smoker's emotional state and support the smoker in quitting smoking.

[0075] The smoking cessation support platform can further use an emotion estimation function to monitor the progress of smoking cessation based on the smoker's emotional state and adjust the support plan as needed. For example, the emotion estimation function can be used to analyze the smoker's emotional state and adjust the support plan when the smoker is emotionally unstable. The emotion estimation function can also be used to analyze the smoker's emotional state and update the support plan in real time according to emotional fluctuations. The emotion estimation function can also be used to analyze the smoker's emotional state and provide a support plan that is sensitive to the smoker's emotions. As a result, the emotion estimation function makes it possible to adjust the support plan based on the smoker's emotional state, thereby providing more effective smoking cessation support.

[0076] The smoking cessation support platform can further use the emotion estimation function to share successful quitting experiences based on the smoker's emotional state. For example, the emotion estimation function can be used to analyze the smoker's emotional state and introduce other smoker's successful quitting experiences when the smoker is feeling emotionally charged. The emotion estimation function can also be used to analyze the smoker's emotional state and share successful quitting experiences according to emotional fluctuations. The emotion estimation function can also be used to analyze the smoker's emotional state and provide successful quitting experiences that are in tune with the smoker's emotions. In this way, the emotion estimation function makes it possible to share successful quitting experiences based on the smoker's emotional state, thereby increasing motivation to quit smoking.

[0077] The smoking cessation support platform can further use an emotion estimation function to provide feedback on the progress of smoking cessation based on the smoker's emotional state. For example, the emotion estimation function can be used to analyze the smoker's emotional state and send positive feedback when the emotion decreases. The emotion estimation function can also be used to analyze the smoker's emotional state and adjust the content of the feedback according to emotional fluctuations. The emotion estimation function can also be used to analyze the smoker's emotional state and provide feedback that is sensitive to the emotion. In this way, the emotion estimation function can provide feedback based on the smoker's emotional state, thereby supporting the progress of smoking cessation.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The needs analysis section analyzes the smoker's needs and situation. For example, it collects and analyzes the smoker's smoking habits, stress level, and history of past quit attempts. It also analyzes the information provided by the smoker and their answers to questions. Step 2: The support plan generator generates a customized smoking cessation support plan based on the results of the needs analysis. For example, it may suggest alternative actions when the smoker feels stressed or send messages to maintain motivation to continue quitting smoking. It also monitors the smoker's progress and adjusts the support plan as needed. Step 3: The support implementation unit provides specific support to smokers based on the smoking cessation support plan generated by the support plan generation unit. For example, the support implementation unit suggests ways to relax when smokers feel the urge to smoke, or motivates smokers by sharing their successful experiences of quitting smoking. The support implementation unit can also recommend participation in support groups or communities.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] 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.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. It is a system equipped with generative AI, a needs analysis unit that analyzes the needs and situations of smokers; a support plan generation unit that generates a customized smoking cessation support plan based on the results of the analysis by the needs analysis unit; a support implementation unit that provides specific support to the smoker based on the smoking cessation support plan generated by the support plan generation unit. A system characterized by:

2. The needs analysis unit Analyzing the emotional state of the smoker in real time and dynamically updating the needs and the situation according to emotional fluctuations.

2. The system of claim 1.

3. The needs analysis unit Incorporating feedback from family and friends into the smoker's needs analysis to provide more comprehensive support 2. The system of claim 1.

4. The support plan generation unit The support plan is adjusted in real time according to the emotional state of the smoker, and the optimal support is provided.

2. The system of claim 1.

5. The support implementation department The emotional state of the smoker is monitored in real time, and the support content is dynamically adjusted according to the emotional fluctuations.

2. The system of claim 1.

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