System

Abstract

An object of the system according to the embodiment is to collect actual conversation contents and to generate and provide a highly accurate lifestyle proposal.SOLUTION: A system according to an embodiment includes a conversation collection unit, an analysis unit, a proposal generation unit, and a provision unit. The conversation collection unit collects actual conversation contents. The analysis unit analyzes the conversation content collected by the conversation collection unit. The proposal generation unit generates a lifestyle proposal based on the data analyzed by the analysis unit. The providing unit provides the user with the proposal generated by the proposal generation unit.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 have not adequately collected real-life conversation content and utilized it to make lifestyle suggestions, so there is room for improvement.

[0005] The system according to the embodiment aims to collect real-life conversation content and generate and provide highly accurate lifestyle suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation collection unit, an analysis unit, a proposal generation unit, and a provision unit. The conversation collection unit collects real-world conversation content. The analysis unit analyzes the conversation content collected by the conversation collection unit. The proposal generation unit generates lifestyle proposals based on the data analyzed by the analysis unit. The provision unit provides the proposals generated by the proposal generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can collect real-life conversation content and generate and provide highly accurate lifestyle suggestions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The lifestyle suggestion system according to the embodiment of the present invention is a system that collects real-life person-to-person conversation content in addition to conversation content on generative AI and social media, and makes highly accurate lifestyle suggestions. This allows the lifestyle suggestion system to make highly accurate lifestyle suggestions based on real-life conversation content.

[0029] A lifestyle suggestion system according to an embodiment includes a conversation collection unit, an analysis unit, a proposal generation unit, and a provision unit. The conversation collection unit collects real-world conversation content. For example, it records everyday conversations through a microphone installed in a smartphone or a wearable device. The conversation collection unit can also collect conversation content through a smart device in the home. Furthermore, the conversation collection unit can also collect conversation content through an infotainment system in a vehicle. The analysis unit analyzes the conversation content collected by the conversation collection unit. For example, the generation AI analyzes the conversation content using natural language processing technology and extracts information about a user's interests and lifestyle. The analysis unit can also analyze changes in emotions during conversation in real time using an emotion estimation function. The proposal generation unit generates lifestyle suggestions based on the data analyzed by the analysis unit. For example, the generation AI suggests healthy eating and exercise based on the user's profile data. The proposal generation unit can also combine the user's geographical location information to provide region-specific suggestions. The provision unit provides the suggestions generated by the proposal generation unit to the user. For example, the suggested content may be notified via a smartphone app or a wearable device. The providing unit may also provide the suggested content via a smart device in the home. This allows the lifestyle suggestion system according to the embodiment to provide highly accurate lifestyle suggestions based on real-life conversation content. For example, the user may receive individually customized suggestions based on the hobbies and interests that the user talks about in everyday conversation. Furthermore, feedback on the suggested content improves the accuracy of the suggestions, further enriching the user's lifestyle.

[0030] The conversation collection unit can collect conversation content through a microphone installed in a smartphone or a wearable device. The conversation collection unit records everyday conversations using, for example, a microphone installed in a smartphone or a wearable device. For example, the conversation content is collected using the microphone of the smartphone. The conversation content can also be collected using the microphone of a wearable device. For example, the conversation content is recorded using the microphone of a smartwatch. In this way, the conversation content can be collected through a smartphone or a wearable device.

[0031] The analysis unit can analyze the user's hobbies, interests, and daily behavioral patterns. For example, the generation AI analyzes the content of the conversation and extracts the user's hobbies, interests, and daily behavioral patterns. For example, the generation AI uses natural language processing technology to identify the user's hobbies and interests from the content of the conversation. The generation AI can also analyze the user's daily behavioral patterns from the content of the conversation. For example, the user's behavioral patterns can be identified based on the hobbies and interests that the user frequently talks about. This makes it possible to analyze the user's hobbies, interests, and behavioral patterns.

[0032] The suggestion generation unit can make suggestions for healthy eating and exercise if the user is interested in health. In the suggestion generation unit, for example, the generation AI makes suggestions for healthy eating and exercise based on the user's profile data. For example, if the user is interested in health, the generation AI suggests a balanced meal menu. In addition, if the user is interested in health, the generation AI can also suggest a recommended exercise plan. For example, the generation AI suggests an appropriate exercise plan based on the user's health data. This makes it possible to make healthy suggestions if the user is interested in health.

[0033] The providing unit can notify the content of the proposal through a smartphone app or a wearable device. The providing unit, for example, notifies the content of the proposal through a smartphone app. For example, the smartphone app notifies the content of the proposal. The providing unit can also notify the content of the proposal through a wearable device. For example, a smartwatch notifies the content of the proposal. This makes it possible to notify the content of the proposal through a smartphone app or a wearable device.

[0034] The analysis unit can record the results of the user's execution of the proposed exercise plan and analyze its effectiveness. The analysis unit, for example, records the results of the user's execution of the proposed exercise plan. For example, the generation AI stores the results of the exercise plan executed by the user in a database. The analysis unit also analyzes the effectiveness based on those results. For example, the generation AI analyzes the results of the user's exercise plan and evaluates its effectiveness. This allows the user to record the results of the proposed exercise plan and analyze its effectiveness.

[0035] The conversation collection unit can simultaneously collect gesture and facial expression data in addition to voice data. For example, the conversation collection unit simultaneously collects gesture and facial expression data in addition to voice data using a camera. For example, a camera mounted on a smartphone or wearable device can be used to record facial expressions and hand movements during conversation. The conversation collection unit can also integrate voice data, gesture and facial expression data to analyze the context of the conversation in more detail. For example, it combines voice recognition technology and image recognition technology to understand the content and background of the conversation. This makes it possible to collect gesture and facial expression data in addition to voice data.

[0036] The conversation collection unit can also collect background and environmental sounds of the conversation. For example, when collecting the content of a conversation, the conversation collection unit simultaneously collects background and environmental sounds in addition to audio data. For example, a microphone mounted on a smartphone or a wearable device is used to record surrounding sounds. The conversation collection unit can also analyze background and environmental sounds to understand the situation of the conversation. For example, the conversation collection unit can analyze the sounds of a cafe or traffic to identify the location and situation where the conversation took place. This allows the conversation to also collect background and environmental sounds.

[0037] The conversation collection unit can collect conversation content through smart devices in the home. The conversation collection unit collects everyday conversation content using, for example, a smart speaker in the home. For example, conversations in the home are recorded through devices such as Amazon Echo or Google Home. The conversation collection unit can also collect conversation content in the home using a smart TV. For example, the TV's voice recognition function is used to record and analyze family conversations. Furthermore, the conversation collection unit can link multiple smart devices in the home to collect conversation content from the entire home. For example, a smart speaker and a smart TV are linked to centrally collect conversations in the home. This allows conversation content to be collected through smart devices in the home.

[0038] The conversation collection unit can collect conversation content through an in-vehicle infotainment system. The conversation collection unit, for example, uses the in-vehicle infotainment system to collect conversation content while driving. For example, the conversation while driving is recorded and analyzed using an in-vehicle microphone. The conversation collection unit can also work in conjunction with the in-vehicle infotainment system to collect conversation content while traveling in real time. For example, the conversation while traveling is analyzed in conjunction with a navigation system. Furthermore, the conversation collection unit can make lifestyle suggestions while traveling based on the conversation content collected through the in-vehicle infotainment system. For example, the conversation content while driving is analyzed and suggestions suitable for driving are made. In this way, conversation content can be collected through the in-vehicle infotainment system.

[0039] The analysis unit can also integrate the user's past conversation history and comments on social media to create a more comprehensive profile. The analysis unit, for example, analyzes the user's past conversation history and integrates it with real-life conversation content. For example, the past conversation data can be used to identify the user's interests and concerns. The analysis unit can also analyze comments made on social media and integrate it with real-life conversation content. For example, it can analyze posts on Twitter or Facebook to create a user profile. The analysis unit can also integrate the user's past conversation history and comments made on social media to create a more comprehensive user profile. For example, the analysis unit can identify the user's behavioral patterns and interests based on past data. This allows the analysis unit to integrate the user's past conversation history and comments made on social media to create a comprehensive profile.

[0040] The analysis unit can combine the user's biometric data and perform analysis based on the health condition. The analysis unit, for example, analyzes the user's heart rate data and combines it with the content of the conversation to understand the health condition. For example, the stress level is identified based on fluctuations in the heart rate. The analysis unit can also analyze the user's stress level data and combine it with the content of the conversation to understand the health condition. For example, the user's emotional state is identified based on fluctuations in the stress level. Furthermore, the analysis unit can analyze the user's biometric data and combine it with the content of the conversation to make lifestyle suggestions based on the health condition. For example, healthy suggestions are made based on the heart rate and stress level. This makes it possible to combine the biometric data to perform analysis based on the health condition.

[0041] The analysis unit can share the analysis results of the conversation content with social networks such as family and friends, and make lifestyle suggestions for the entire group. The analysis unit, for example, shares the analysis results of the conversation content with family members and makes lifestyle suggestions for the entire group. For example, it can suggest a healthy meal plan based on the health status of all family members. The analysis unit can also share the analysis results of the conversation content with friends and make lifestyle suggestions for the entire group. For example, it can suggest a leisure plan based on the interests and concerns of the friend group. Furthermore, the analysis unit can share the analysis results of the conversation content with social networks and make lifestyle suggestions for the entire group. For example, it can suggest joint activities based on the behavioral patterns of the entire group. In this way, the analysis results of the conversation content can be shared with social networks and make lifestyle suggestions for the entire group.

[0042] The analysis unit can share the analysis results of the conversation content with colleagues and team members at work and make lifestyle suggestions suitable for the work environment. The analysis unit, for example, can share the analysis results of the conversation content with colleagues and make lifestyle suggestions suitable for the work environment. For example, the analysis unit can suggest a relaxation plan based on the stress level at work. The analysis unit can also share the analysis results of the conversation content with team members and make lifestyle suggestions suitable for the work environment. For example, the analysis unit can suggest a healthy office environment based on the health status of the entire team. The analysis unit can also share the analysis results of the conversation content with the workplace social network and make lifestyle suggestions suitable for the work environment. For example, the analysis unit can suggest team building activities based on workplace communication patterns. In this way, the analysis results of the conversation content can be shared with colleagues and team members at work and make lifestyle suggestions suitable for the work environment.

[0043] The proposal generation unit can improve the accuracy of proposals by reflecting the user's past behavioral history and feedback. The proposal generation unit, for example, analyzes the user's past behavioral history and reflects it in lifestyle proposals. For example, the proposal generation unit can propose a next exercise plan based on past exercise history. The proposal generation unit can also collect feedback from the user and reflect it in lifestyle proposals. For example, the proposal generation unit can adjust the next meal plan based on feedback on a proposed meal plan. Furthermore, the proposal generation unit can integrate past behavioral history and feedback to improve the accuracy of lifestyle proposals. For example, the proposal generation unit makes proposals based on the user's preferences and habits based on past data. This makes it possible to improve the accuracy of proposals by reflecting past behavioral history and feedback.

[0044] The proposal generation unit can combine the user's geographical location information to make proposals specialized for the region. The proposal generation unit, for example, analyzes the user's geographical location information to make lifestyle proposals specialized for the region. For example, it can suggest nearby restaurants and events based on the user's current location. The proposal generation unit can also make lifestyle proposals tailored to the characteristics of the region based on the geographical location information. For example, it can make proposals tailored to the climate and culture of the region. Furthermore, the proposal generation unit can analyze the user's location information in real time to make proposals specialized for the region. For example, if the user is traveling, it can suggest tourist spots and activities in the region. In this way, it is possible to combine the geographical location information to make proposals specialized for the region.

[0045] The suggestion generation unit can share lifestyle suggestions with the user's family and friends, and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can share lifestyle suggestions with family and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can suggest a fitness plan that the whole family can participate in. The suggestion generation unit can also share lifestyle suggestions with friends and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can suggest a leisure plan that can be enjoyed by a group of friends. Furthermore, the suggestion generation unit can share lifestyle suggestions with social networks and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can suggest events or activities that the whole group can participate in. This allows lifestyle suggestions to be shared with family and friends, and make suggestions that can be implemented by the entire group.

[0046] The proposal generation unit can customize lifestyle proposals to suit the user's work environment and work content, thereby improving performance at work. The proposal generation unit, for example, customizes lifestyle proposals to suit the work environment and improves performance at work. For example, it proposes a workplace stress management plan. The proposal generation unit can also customize lifestyle proposals to suit the work content and improve performance at work. For example, it proposes a time management plan to improve work efficiency. Furthermore, the proposal generation unit can customize lifestyle proposals based on the work environment and work content, thereby improving performance at work. For example, it proposes a plan to improve communication in the workplace. In this way, it is possible to customize lifestyle proposals to suit the work environment and work content, thereby improving performance at work.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The lifestyle suggestion system can further collect and analyze the user's sleep data. For example, a wearable device can be used to record the user's sleep patterns and identify periods of deep and light sleep. The analysis unit can also evaluate the user's sleep quality based on the collected sleep data and make suggestions for improvement. For example, if the sleep quality is low, the analysis unit can suggest relaxation techniques and appropriate bedtimes. Furthermore, the suggestion generation unit can combine the user's sleep data with daytime activity data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions based on the user's sleep data.

[0049] The lifestyle suggestion system can further collect and analyze the user's dietary data. For example, it can take photos of meals using a smartphone camera and analyze the meal contents using image recognition technology. The analysis unit can also evaluate the user's nutritional balance based on the collected dietary data and make suggestions for improvement. For example, if the user's nutritional balance is unbalanced, it can suggest a balanced meal menu. Furthermore, the suggestion generation unit can combine the user's dietary data and exercise data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions to be made based on the user's dietary data.

[0050] The lifestyle suggestion system can further monitor and analyze the user's stress level. For example, a wearable device can be used to measure heart rate and electrodermal activity to identify the stress level. The analysis unit can also make suggestions for the user's stress management based on the collected stress data. For example, if the stress level is high, the analysis unit can suggest relaxation methods and activities to reduce stress. Furthermore, the suggestion generation unit can combine the user's stress data with daily behavior data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions based on the user's stress data.

[0051] The lifestyle suggestion system can also collect and analyze the user's exercise data. For example, a wearable device can be used to record and analyze the user's exercise amount and type. The analysis unit can also evaluate the user's exercise habits based on the collected exercise data and make suggestions for improvement. For example, if the user is not getting enough exercise, an appropriate exercise plan can be suggested. Furthermore, the suggestion generation unit can combine the user's exercise data with dietary data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions to be made based on the user's exercise data.

[0052] The lifestyle suggestion system can also collect and analyze the user's geographical location information. For example, the system can record and analyze the user's movement history using the GPS function of a smartphone. The analysis unit can also evaluate the user's movement patterns based on the collected location information and make suggestions specific to the area. For example, the system can suggest nearby restaurants and events based on the user's frequently visited places. Furthermore, the suggestion generation unit can combine the user's location information with daily behavior data to make suggestions to improve the user's overall lifestyle. This allows for highly accurate lifestyle suggestions based on the user's geographical location information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The conversation collection unit collects real-world conversation content. For example, it can record everyday conversations using microphones installed in smartphones or wearable devices. Conversation content can also be collected through smart devices in the home or infotainment systems in cars. Step 2: The analysis unit analyzes the conversation content collected by the conversation collection unit. For example, the generation AI uses natural language processing technology to analyze the conversation content and extract information about the user's interests, concerns, and lifestyle. It can also use emotion estimation functionality to analyze changes in emotions during conversation in real time. Step 3: The proposal generator generates lifestyle suggestions based on the data analyzed by the analyzer. For example, the generator AI can suggest healthy eating and exercise based on the user's profile data. It can also combine the user's geographic location information to provide region-specific suggestions. Step 4: The providing unit provides the proposals generated by the proposal generating unit to the user. For example, the proposal contents may be notified to the user through a smartphone app or a wearable device. The proposal contents may also be provided through a smart device in the home.

[0055] (Example 2) The lifestyle suggestion system according to the embodiment of the present invention is a system that collects real-life person-to-person conversation content in addition to conversation content on generative AI and social media, and makes highly accurate lifestyle suggestions. This allows the lifestyle suggestion system to make highly accurate lifestyle suggestions based on real-life conversation content.

[0056] A lifestyle suggestion system according to an embodiment includes a conversation collection unit, an analysis unit, a proposal generation unit, and a provision unit. The conversation collection unit collects real-world conversation content. For example, it records everyday conversations through a microphone installed in a smartphone or a wearable device. The conversation collection unit can also collect conversation content through a smart device in the home. Furthermore, the conversation collection unit can also collect conversation content through an infotainment system in a vehicle. The analysis unit analyzes the conversation content collected by the conversation collection unit. For example, the generation AI analyzes the conversation content using natural language processing technology and extracts information about a user's interests and lifestyle. The analysis unit can also analyze changes in emotions during conversation in real time using an emotion estimation function. The proposal generation unit generates lifestyle suggestions based on the data analyzed by the analysis unit. For example, the generation AI suggests healthy eating and exercise based on the user's profile data. The proposal generation unit can also combine the user's geographical location information to provide region-specific suggestions. The provision unit provides the suggestions generated by the proposal generation unit to the user. For example, the suggested content may be notified via a smartphone app or a wearable device. The providing unit may also provide the suggested content via a smart device in the home. This allows the lifestyle suggestion system according to the embodiment to provide highly accurate lifestyle suggestions based on real-life conversation content. For example, the user may receive individually customized suggestions based on the hobbies and interests that the user talks about in everyday conversation. Furthermore, feedback on the suggested content improves the accuracy of the suggestions, further enriching the user's lifestyle.

[0057] The conversation collection unit can collect conversation content through a microphone installed in a smartphone or a wearable device. The conversation collection unit records everyday conversations using, for example, a microphone installed in a smartphone or a wearable device. For example, the conversation content is collected using the microphone of the smartphone. The conversation content can also be collected using the microphone of a wearable device. For example, the conversation content is recorded using the microphone of a smartwatch. In this way, the conversation content can be collected through a smartphone or a wearable device.

[0058] The analysis unit can analyze the user's hobbies, interests, and daily behavioral patterns. For example, the generation AI analyzes the content of the conversation and extracts the user's hobbies, interests, and daily behavioral patterns. For example, the generation AI uses natural language processing technology to identify the user's hobbies and interests from the content of the conversation. The generation AI can also analyze the user's daily behavioral patterns from the content of the conversation. For example, the user's behavioral patterns can be identified based on the hobbies and interests that the user frequently talks about. This makes it possible to analyze the user's hobbies, interests, and behavioral patterns.

[0059] The suggestion generation unit can make suggestions for healthy eating and exercise if the user is interested in health. In the suggestion generation unit, for example, the generation AI makes suggestions for healthy eating and exercise based on the user's profile data. For example, if the user is interested in health, the generation AI suggests a balanced meal menu. In addition, if the user is interested in health, the generation AI can also suggest a recommended exercise plan. For example, the generation AI suggests an appropriate exercise plan based on the user's health data. This makes it possible to make healthy suggestions if the user is interested in health.

[0060] The providing unit can notify the content of the proposal through a smartphone app or a wearable device. The providing unit, for example, notifies the content of the proposal through a smartphone app. For example, the smartphone app notifies the content of the proposal. The providing unit can also notify the content of the proposal through a wearable device. For example, a smartwatch notifies the content of the proposal. This makes it possible to notify the content of the proposal through a smartphone app or a wearable device.

[0061] The analysis unit can record the results of the user's execution of the proposed exercise plan and analyze its effectiveness. The analysis unit, for example, records the results of the user's execution of the proposed exercise plan. For example, the generation AI stores the results of the exercise plan executed by the user in a database. The analysis unit also analyzes the effectiveness based on those results. For example, the generation AI analyzes the results of the user's exercise plan and evaluates its effectiveness. This allows the user to record the results of the proposed exercise plan and analyze its effectiveness.

[0062] The conversation collection unit can simultaneously collect gesture and facial expression data in addition to voice data. For example, the conversation collection unit simultaneously collects gesture and facial expression data in addition to voice data using a camera. For example, a camera mounted on a smartphone or wearable device can be used to record facial expressions and hand movements during conversation. The conversation collection unit can also integrate voice data, gesture and facial expression data to analyze the context of the conversation in more detail. For example, it combines voice recognition technology and image recognition technology to understand the content and background of the conversation. This makes it possible to collect gesture and facial expression data in addition to voice data.

[0063] The conversation collection unit can also collect background and environmental sounds of the conversation. For example, when collecting the content of a conversation, the conversation collection unit simultaneously collects background and environmental sounds in addition to audio data. For example, a microphone mounted on a smartphone or a wearable device is used to record surrounding sounds. The conversation collection unit can also analyze background and environmental sounds to understand the situation of the conversation. For example, the conversation collection unit can analyze the sounds of a cafe or traffic to identify the location and situation where the conversation took place. This allows the conversation to also collect background and environmental sounds.

[0064] The conversation collection unit can analyze changes in emotions during a conversation in real time using the emotion estimation function. The conversation collection unit, for example, analyzes audio data during a conversation and analyzes changes in emotions in real time using the emotion estimation function. For example, it analyzes the tone and pitch of the voice to identify changes in emotions. The conversation collection unit can also use the emotion estimation function to monitor changes in emotions during a conversation in real time and collect data based on emotions. For example, it identifies positive and negative emotions during a conversation and collects data based on that. This allows changes in emotions during a conversation to be analyzed in real time.

[0065] The conversation collection unit can collect conversation content through smart devices in the home. The conversation collection unit collects everyday conversation content using, for example, a smart speaker in the home. For example, conversations in the home are recorded through devices such as Amazon Echo or Google Home. The conversation collection unit can also collect conversation content in the home using a smart TV. For example, the TV's voice recognition function is used to record and analyze family conversations. Furthermore, the conversation collection unit can link multiple smart devices in the home to collect conversation content from the entire home. For example, a smart speaker and a smart TV are linked to centrally collect conversations in the home. This allows conversation content to be collected through smart devices in the home.

[0066] The conversation collection unit can collect conversation content through an in-vehicle infotainment system. The conversation collection unit, for example, uses the in-vehicle infotainment system to collect conversation content while driving. For example, the conversation while driving is recorded and analyzed using an in-vehicle microphone. The conversation collection unit can also work in conjunction with the in-vehicle infotainment system to collect conversation content while traveling in real time. For example, the conversation while traveling is analyzed in conjunction with a navigation system. Furthermore, the conversation collection unit can make lifestyle suggestions while traveling based on the conversation content collected through the in-vehicle infotainment system. For example, the conversation content while driving is analyzed and suggestions suitable for driving are made. In this way, conversation content can be collected through the in-vehicle infotainment system.

[0067] The conversation collection unit can monitor the user's emotional state in real time using a wearable device equipped with an emotion estimation function and collect conversation content based on the emotion. The conversation collection unit can monitor the user's emotional state in real time using, for example, a smartwatch equipped with an emotion estimation function. For example, the conversation collection unit can identify the emotional state by analyzing the heart rate or electrodermal activity. The conversation collection unit can also monitor the user's emotional state in real time using a wearable device equipped with an emotion estimation function and collect conversation content based on the emotion. For example, the conversation content can be filtered according to the emotional state. Furthermore, the conversation collection unit can monitor the user's emotional state in real time using a wearable device equipped with an emotion estimation function and make lifestyle suggestions based on the emotion. For example, the suggestions can be adjusted according to the emotional state. In this way, conversation content based on the emotion can be collected using a wearable device equipped with an emotion estimation function.

[0068] The analysis unit can also integrate the user's past conversation history and comments on social media to create a more comprehensive profile. The analysis unit, for example, analyzes the user's past conversation history and integrates it with real-life conversation content. For example, the past conversation data can be used to identify the user's interests and concerns. The analysis unit can also analyze comments made on social media and integrate it with real-life conversation content. For example, it can analyze posts on Twitter or Facebook to create a user profile. The analysis unit can also integrate the user's past conversation history and comments made on social media to create a more comprehensive user profile. For example, the analysis unit can identify the user's behavioral patterns and interests based on past data. This allows the analysis unit to integrate the user's past conversation history and comments made on social media to create a comprehensive profile.

[0069] The analysis unit can combine the user's biometric data and perform analysis based on the health condition. The analysis unit, for example, analyzes the user's heart rate data and combines it with the content of the conversation to understand the health condition. For example, the stress level is identified based on fluctuations in the heart rate. The analysis unit can also analyze the user's stress level data and combine it with the content of the conversation to understand the health condition. For example, the user's emotional state is identified based on fluctuations in the stress level. Furthermore, the analysis unit can analyze the user's biometric data and combine it with the content of the conversation to make lifestyle suggestions based on the health condition. For example, healthy suggestions are made based on the heart rate and stress level. This makes it possible to combine the biometric data to perform analysis based on the health condition.

[0070] The analysis unit can use the emotion estimation function to analyze the emotional nuances of the conversation content and create a database based on emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotional nuances of the conversation content. For example, the analysis unit analyzes the tone and pitch of the voice to identify changes in emotions. The analysis unit can also analyze the emotional nuances of the conversation content and create a database based on emotions. For example, the analysis unit classifies data based on positive emotions and negative emotions. Furthermore, the analysis unit can use the emotion estimation function to analyze the emotional nuances of the conversation content and make lifestyle suggestions based on emotions. For example, the suggestion content can be adjusted based on changes in emotions. In this way, the emotion estimation function can be used to analyze the emotional nuances of the conversation content and create a database based on emotions.

[0071] The analysis unit can share the analysis results of the conversation content with social networks such as family and friends, and make lifestyle suggestions for the entire group. The analysis unit, for example, shares the analysis results of the conversation content with family members and makes lifestyle suggestions for the entire group. For example, it can suggest a healthy meal plan based on the health status of all family members. The analysis unit can also share the analysis results of the conversation content with friends and make lifestyle suggestions for the entire group. For example, it can suggest a leisure plan based on the interests and concerns of the friend group. Furthermore, the analysis unit can share the analysis results of the conversation content with social networks and make lifestyle suggestions for the entire group. For example, it can suggest joint activities based on the behavioral patterns of the entire group. In this way, the analysis results of the conversation content can be shared with social networks and make lifestyle suggestions for the entire group.

[0072] The analysis unit can share the analysis results of the conversation content with colleagues and team members at work and make lifestyle suggestions suitable for the work environment. The analysis unit, for example, can share the analysis results of the conversation content with colleagues and make lifestyle suggestions suitable for the work environment. For example, the analysis unit can suggest a relaxation plan based on the stress level at work. The analysis unit can also share the analysis results of the conversation content with team members and make lifestyle suggestions suitable for the work environment. For example, the analysis unit can suggest a healthy office environment based on the health status of the entire team. The analysis unit can also share the analysis results of the conversation content with the workplace social network and make lifestyle suggestions suitable for the work environment. For example, the analysis unit can suggest team building activities based on workplace communication patterns. In this way, the analysis results of the conversation content can be shared with colleagues and team members at work and make lifestyle suggestions suitable for the work environment.

[0073] The analysis unit uses the emotion estimation function to analyze the user's emotional state and create a database based on the emotions, thereby providing emotional support. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state and create a database based on the emotions. For example, the analysis unit classifies data based on positive emotions and negative emotions. The analysis unit can also provide emotional support by analyzing the user's emotional state and creating a database based on the emotions. For example, if the user is feeling strongly negative emotions, the analysis unit can suggest a relaxation plan. Furthermore, the analysis unit can also use the emotion estimation function to analyze the user's emotional state and make lifestyle suggestions based on the emotions. For example, emotional support can be provided based on changes in emotions. In this way, emotional support can be provided by analyzing the user's emotional state using the emotion estimation function and creating a database based on the emotions.

[0074] The proposal generation unit can improve the accuracy of proposals by reflecting the user's past behavioral history and feedback. The proposal generation unit, for example, analyzes the user's past behavioral history and reflects it in lifestyle proposals. For example, the proposal generation unit can propose a next exercise plan based on past exercise history. The proposal generation unit can also collect feedback from the user and reflect it in lifestyle proposals. For example, the proposal generation unit can adjust the next meal plan based on feedback on a proposed meal plan. Furthermore, the proposal generation unit can integrate past behavioral history and feedback to improve the accuracy of lifestyle proposals. For example, the proposal generation unit makes proposals based on the user's preferences and habits based on past data. This makes it possible to improve the accuracy of proposals by reflecting past behavioral history and feedback.

[0075] The proposal generation unit can combine the user's geographical location information to make proposals specialized for the region. The proposal generation unit, for example, analyzes the user's geographical location information to make lifestyle proposals specialized for the region. For example, it can suggest nearby restaurants and events based on the user's current location. The proposal generation unit can also make lifestyle proposals tailored to the characteristics of the region based on the geographical location information. For example, it can make proposals tailored to the climate and culture of the region. Furthermore, the proposal generation unit can analyze the user's location information in real time to make proposals specialized for the region. For example, if the user is traveling, it can suggest tourist spots and activities in the region. In this way, it is possible to combine the geographical location information to make proposals specialized for the region.

[0076] The suggestion generation unit can use the emotion estimation function to generate suggestions according to the user's emotional state and increase emotional satisfaction. The suggestion generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and make lifestyle suggestions according to the emotions. For example, a relaxation plan can be suggested when stress is high. The suggestion generation unit can also monitor the user's emotional state in real time and generate suggestions according to the emotions. For example, active activities can be suggested when positive emotions are strong. Furthermore, the suggestion generation unit can use the emotion estimation function to make suggestions based on the user's emotional state and increase emotional satisfaction. For example, suggestions tailored to the user's preferences can be made based on changes in emotions. In this way, the suggestion generation unit can use the emotion estimation function to generate suggestions according to the user's emotional state and increase emotional satisfaction.

[0077] The suggestion generation unit can share lifestyle suggestions with the user's family and friends, and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can share lifestyle suggestions with family and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can suggest a fitness plan that the whole family can participate in. The suggestion generation unit can also share lifestyle suggestions with friends and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can suggest a leisure plan that can be enjoyed by a group of friends. Furthermore, the suggestion generation unit can share lifestyle suggestions with social networks and make suggestions that can be implemented by the entire group. For example, the suggestion generation unit can suggest events or activities that the whole group can participate in. This allows lifestyle suggestions to be shared with family and friends, and make suggestions that can be implemented by the entire group.

[0078] The proposal generation unit can customize lifestyle proposals to suit the user's work environment and work content, thereby improving performance at work. The proposal generation unit, for example, customizes lifestyle proposals to suit the work environment and improves performance at work. For example, it proposes a workplace stress management plan. The proposal generation unit can also customize lifestyle proposals to suit the work content and improve performance at work. For example, it proposes a time management plan to improve work efficiency. Furthermore, the proposal generation unit can customize lifestyle proposals based on the work environment and work content, thereby improving performance at work. For example, it proposes a plan to improve communication in the workplace. In this way, it is possible to customize lifestyle proposals to suit the work environment and work content, thereby improving performance at work.

[0079] The suggestion generation unit can use the emotion estimation function to generate suggestions based on the user's emotional state and provide emotional support. The suggestion generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and make lifestyle suggestions based on the emotions. For example, a relaxation plan can be suggested when stress is high. The suggestion generation unit can also monitor the user's emotional state in real time and generate suggestions based on the emotions. For example, active activities can be suggested when positive emotions are strong. Furthermore, the suggestion generation unit can use the emotion estimation function to make suggestions based on the user's emotional state and provide emotional support. For example, suggestions tailored to the user's preferences can be made based on changes in emotions. In this way, the suggestion generation unit can use the emotion estimation function to generate suggestions based on the user's emotional state and provide emotional support.

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

[0081] The lifestyle suggestion system can further collect and analyze the user's sleep data. For example, a wearable device can be used to record the user's sleep patterns and identify periods of deep and light sleep. The analysis unit can also evaluate the user's sleep quality based on the collected sleep data and make suggestions for improvement. For example, if the sleep quality is low, the analysis unit can suggest relaxation techniques and appropriate bedtimes. Furthermore, the suggestion generation unit can combine the user's sleep data with daytime activity data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions based on the user's sleep data.

[0082] The lifestyle suggestion system can further collect and analyze the user's dietary data. For example, it can take photos of meals using a smartphone camera and analyze the meal contents using image recognition technology. The analysis unit can also evaluate the user's nutritional balance based on the collected dietary data and make suggestions for improvement. For example, if the user's nutritional balance is unbalanced, it can suggest a balanced meal menu. Furthermore, the suggestion generation unit can combine the user's dietary data and exercise data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions to be made based on the user's dietary data.

[0083] The lifestyle suggestion system can further monitor and analyze the user's stress level. For example, a wearable device can be used to measure heart rate and electrodermal activity to identify the stress level. The analysis unit can also make suggestions for the user's stress management based on the collected stress data. For example, if the stress level is high, the analysis unit can suggest relaxation methods and activities to reduce stress. Furthermore, the suggestion generation unit can combine the user's stress data with daily behavior data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions based on the user's stress data.

[0084] The lifestyle suggestion system can also collect and analyze the user's exercise data. For example, a wearable device can be used to record and analyze the user's exercise amount and type. The analysis unit can also evaluate the user's exercise habits based on the collected exercise data and make suggestions for improvement. For example, if the user is not getting enough exercise, an appropriate exercise plan can be suggested. Furthermore, the suggestion generation unit can combine the user's exercise data with dietary data to make lifestyle suggestions to improve overall health. This allows for highly accurate lifestyle suggestions to be made based on the user's exercise data.

[0085] The lifestyle suggestion system can also collect and analyze the user's geographical location information. For example, the system can record and analyze the user's movement history using the GPS function of a smartphone. The analysis unit can also evaluate the user's movement patterns based on the collected location information and make suggestions specific to the area. For example, the system can suggest nearby restaurants and events based on the user's frequently visited places. Furthermore, the suggestion generation unit can combine the user's location information with daily behavior data to make suggestions to improve the user's overall lifestyle. This allows for highly accurate lifestyle suggestions based on the user's geographical location information.

[0086] The lifestyle suggestion system can further analyze the user's emotional state and make lifestyle suggestions based on the emotions. For example, the system can analyze voice data and facial expression data to identify the user's emotional state. The analysis unit can also make suggestions based on the collected emotional data according to the user's emotional state. For example, if the user is feeling stressed, the analysis unit can suggest relaxation methods and activities to reduce stress. Furthermore, the suggestion generation unit can combine the user's emotional data with daily behavior data to make suggestions for improving the user's overall lifestyle. This allows for highly accurate lifestyle suggestions based on the user's emotional state.

[0087] The lifestyle suggestion system can further monitor the user's emotional state in real time and provide feedback based on the emotion. For example, a wearable device can be used to measure heart rate and electrodermal activity to identify the user's emotional state. The analysis unit can also provide real-time feedback based on the collected emotional data according to the user's emotional state. For example, if the user is feeling stressed, the analysis unit can suggest relaxation methods. Furthermore, the suggestion generation unit can combine the user's emotional data with daily behavior data to make suggestions for improving the user's overall lifestyle. This allows the system to monitor the user's emotional state in real time and provide feedback based on the emotion.

[0088] The lifestyle suggestion system can further analyze the user's emotional state and suggest music and entertainment based on the emotion. For example, the system can analyze voice data and facial expression data to identify the user's emotional state. The analysis unit can also suggest music and entertainment according to the user's emotional state based on the collected emotional data. For example, if the user wants to relax, the system can suggest relaxation music. Furthermore, the suggestion generation unit can combine the user's emotional data with daily behavior data to make suggestions for improving the user's overall lifestyle. This allows the system to suggest music and entertainment based on the user's emotional state.

[0089] The lifestyle suggestion system can further analyze the user's emotional state and make communication suggestions based on the emotion. For example, the system can analyze voice data and facial expression data to identify the user's emotional state. The analysis unit can also suggest communication methods based on the collected emotional data, depending on the user's emotional state. For example, if the user is feeling stressed, the analysis unit can suggest relaxation methods and activities to reduce stress. Furthermore, the suggestion generation unit can combine the user's emotional data with daily behavior data to make suggestions for improving the user's overall lifestyle. This makes it possible to make communication suggestions based on the user's emotional state.

[0090] The lifestyle suggestion system can further analyze the user's emotional state and provide feedback based on the emotion. For example, the system can analyze voice data and facial expression data to identify the user's emotional state. The analysis unit can also provide feedback according to the user's emotional state based on the collected emotional data. For example, if the user is feeling stressed, the analysis unit can suggest relaxation methods and activities to reduce stress. Furthermore, the suggestion generation unit can combine the user's emotional data with daily behavior data to make suggestions for improving the user's overall lifestyle. This makes it possible to provide feedback based on the user's emotional state.

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

[0092] Step 1: The conversation collection unit collects real-world conversation content. For example, it can record everyday conversations using microphones installed in smartphones or wearable devices. Conversation content can also be collected through smart devices in the home or infotainment systems in cars. Step 2: The analysis unit analyzes the conversation content collected by the conversation collection unit. For example, the generation AI uses natural language processing technology to analyze the conversation content and extract information about the user's interests, concerns, and lifestyle. It can also use emotion estimation functionality to analyze changes in emotions during conversation in real time. Step 3: The proposal generator generates lifestyle suggestions based on the data analyzed by the analyzer. For example, the generator AI can suggest healthy eating and exercise based on the user's profile data. It can also combine the user's geographic location information to provide region-specific suggestions. Step 4: The providing unit provides the proposals generated by the proposal generating unit to the user. For example, the proposal contents may be notified to the user through a smartphone app or a wearable device. The proposal contents may also be provided through a smart device in the home.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a conversation collection unit that collects real conversation content; an analysis unit that analyzes the conversation content collected by the conversation collection unit; a proposal generation unit that generates lifestyle proposals based on the data analyzed by the analysis unit; a providing unit that provides the proposal generated by the proposal generating unit to the user. A system characterized by:

2. The conversation collection unit Collect conversations through a microphone on a smartphone or wearable device 2. The system of claim 1.

3. The analysis unit Analyzing the user's hobbies, interests, and daily behavior patterns 2. The system of claim 1.

4. The proposal generation unit If the user is interested in health, suggest healthy eating and exercise.

2. The system of claim 1.

5. The providing unit Proposals are communicated via a smartphone app or wearable device 2. The system of claim 1.

Images