system

Abstract

The system according to this embodiment aims to understand the user's mental state and living situation in real time and to provide encouragement and advice at an appropriate time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides encouragement and advice based on the analysis results obtained by the analysis unit. The proposal unit proposes useful services and products to the user based on the results provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the mental state and living situation of a user in real time and provide encouragement and advice at an appropriate timing.

[0005] The system according to the embodiment aims to grasp the mental state and living situation of a user in real time and provide encouragement and advice at an appropriate timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a data suggestion unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides encouragement and advice based on the analysis results obtained by the analysis unit. The data suggestion unit proposes useful services and products to the user based on the results provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the user's mental state and living situation in real time and provide encouragement and advice at the appropriate time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Kokorozashi Partner System according to an embodiment of the present invention is an AI agent system that deeply understands the user's goals and aspirations and walks alongside them, providing support in solitude. This Kokorozashi Partner System integrates diverse data obtained from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps to grasp the user's mental state and living situation in real time. The Kokorozashi Partner System proactively provides encouragement and advice at the optimal time. Furthermore, the Kokorozashi Partner System carefully selects and proposes services and products that are truly useful to the user and provides long-term support. The Kokorozashi Partner System is designed as a sustainable ecosystem, and AI agent providers can earn referral rewards from service and product providers. Service and product providers can expect improved accuracy in customer acquisition and a reduction in churn rates as the AI ​​agent carefully selects and proposes products to users. As a result, users can use the Kokorozashi Partner System, which is useful in their lives, free of charge and indefinitely. For example, the Kokorozashi Partner System integrates diverse data obtained from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps to grasp the user's mental state and living situation in real time. During this process, the system detects changes in heart rate and stress levels, and offers encouraging messages and relaxation methods as needed. This helps users reduce feelings of loneliness and maintain emotional stability. Next, the Kokorozashi Partner System proactively provides encouragement and advice at the optimal time. For example, if a user is feeling stressed, the Kokorozashi Partner System sends an encouraging message such as, "It's okay, you're making progress one step at a time." If a user wants to relax, it suggests, "Let's take a break at your favorite cafe." This helps users reduce feelings of loneliness and maintain emotional stability. Furthermore, the Kokorozashi Partner System carefully selects and suggests services and products that are truly useful to the user. For example, if a user is aiming for career advancement, the Kokorozashi Partner System suggests online courses for skill learning. If a user is aiming to maintain their health, the Kokorozashi Partner System suggests an exercise plan.This allows users to efficiently progress towards their goals. The Kokorozashi Partner System is designed as a sustainable ecosystem, where AI agent providers can earn referral rewards from service and product providers. Service and product providers can expect improved customer acquisition accuracy and reduced churn rates as AI agents carefully select and recommend products to users. This allows users to use the Kokorozashi Partner System, which is useful in life, free of charge and indefinitely. In this way, the Kokorozashi Partner System greatly contributes to the enrichment of people's well-being and the improvement of healthy social productivity by supporting people in overcoming loneliness and achieving their aspirations. In this way, the Kokorozashi Partner System can support users in achieving their goals and alleviate feelings of loneliness.

[0029] The Kokorozashi Partner System according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a data proposal unit. The data collection unit collects user data. The data collection unit can collect data from, for example, wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. For example, the data collection unit can collect heart rate and step count data from a smartwatch. The data collection unit can also collect room temperature and humidity data from environmental sensors. Furthermore, the data collection unit can collect transaction history data from bank accounts. For example, the data collection unit can collect meal content data from a meal management app. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data in real time. For example, the analysis unit can analyze heart rate data to estimate the user's stress level. Furthermore, the analysis unit can analyze room temperature data to evaluate the user's comfort level. Furthermore, the analysis unit can analyze transaction history data to understand the user's consumption trends. For example, the analysis unit can analyze meal content data to evaluate the user's nutritional balance. The Service Department provides encouragement and advice based on the analysis results obtained by the Analysis Department. For example, the Service Department can proactively provide encouragement and advice at the optimal time. For example, if a user is feeling stressed, the Service Department can send an encouraging message such as, "It's okay, you're making progress one step at a time." Also, if a user wants to relax, the Service Department can suggest, "Let's take a break at your favorite cafe." Furthermore, if a user is aiming to maintain their health, the Service Department can suggest an exercise plan. For example, if a user is aiming for career advancement, the Service Department can suggest an online course for skill acquisition. The Recommendation Department proposes useful services and products to users based on the results provided by the Service Department. For example, the Recommendation Department can carefully select and propose services and products that are truly useful to the user. For example, if a user is aiming to maintain their health, the Recommendation Department can suggest health management services or fitness products.Furthermore, the suggestion department can also suggest online courses for skill acquisition if the user is aiming for career advancement. In addition, if the user wants to enrich their hobbies, the suggestion department can suggest hobby-related products and services. For example, if the user is planning a trip, the suggestion department can suggest travel-related services and products. In this way, the aspiration partner system according to the embodiment can support the user in achieving their goals and reduce feelings of loneliness.

[0030] The data collection unit collects user data. For example, the data collection unit can collect data from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. Specifically, it can collect heart rate and step count data from smartwatches. Smartwatches are worn on the user's wrist and use heart rate sensors and accelerometers to measure the user's heart rate and step count in real time. This data is transmitted to the data collection unit via Bluetooth® or Wi-Fi. The data collection unit can also collect room temperature and humidity data from environmental sensors. Environmental sensors are installed in the user's living space and use room temperature and humidity sensors to measure the indoor temperature and humidity. This data is periodically transmitted to the data collection unit and used to evaluate the user's comfort level. Furthermore, the data collection unit can also collect transaction history data from bank accounts. Bank account transaction history data is important for understanding the user's consumption trends and economic situation, and the data collection unit obtains this data through the bank's API with the user's consent. For example, the data collection unit can also collect meal content data from diet management apps. A meal management app is an application that allows users to record their daily meals, and it transmits the meal details and calorie information entered by the user to a data collection unit. This allows the collection unit to gather data to evaluate the user's health status and nutritional balance. The collection unit centrally manages the data collected from these various devices and applications, making it accessible to the analysis and provision units. This allows the collection unit to efficiently collect multifaceted user data and improve the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit. For example, the analysis unit can analyze collected data in real time. Specifically, it can analyze heart rate data to estimate the user's stress level. Heart rate data shows fluctuations in the user's heart rate, and by analyzing this, it is possible to estimate whether the user is experiencing stress. For example, if the heart rate rises sharply, it is likely that the user is experiencing stress. The analysis unit can also analyze room temperature data to evaluate the user's comfort level. Room temperature data shows the temperature of the user's living space, and by analyzing this, it is possible to identify the temperature range in which the user can live comfortably. For example, if the room temperature is too high, the user may feel uncomfortable, and appropriate temperature adjustment is necessary. Furthermore, the analysis unit can analyze transaction history data to understand the user's consumption trends. Transaction history data shows the user's economic activity, and by analyzing this, it is possible to understand the user's consumption patterns and spending trends. For example, if spending on a particular category is increasing, it is possible to identify the user's interests and needs. The analysis unit can comprehensively analyze this data to gain a detailed understanding of the user's behavior and state. This allows the analysis department to provide users with the foundational data necessary to offer appropriate advice and suggestions.

[0032] The service provider offers encouragement and advice based on the analysis results obtained by the analysis provider. For example, the service provider can proactively provide encouragement and advice at the optimal time. Specifically, if a user is feeling stressed, it can send an encouraging message such as, "It's okay, you're making progress one step at a time." The service provider monitors the user's heart rate data in real time and sends an encouraging message at the appropriate time if it determines that the stress level has increased. The service provider can also suggest, for example, "Let's take a break at your favorite cafe" if the user wants to relax. The service provider analyzes the user's environmental and behavioral data and suggests places and activities where the user can relax. Furthermore, if the service provider is aiming to maintain their health, it can also suggest an exercise plan. The service provider analyzes the user's exercise data and health status and suggests an optimal exercise plan for the user. For example, if the user is not getting enough exercise, it can suggest light exercise such as walking or jogging to support the user's health maintenance. In addition, if the service provider is aiming for career advancement, it can suggest online courses for skill learning. The service provider analyzes the user's career goals and skill level and suggests an optimal online course for the user. This allows the service provider to support users in achieving their goals and increase their motivation.

[0033] The Proposal Department proposes useful services and products to users based on the results provided by the Service Department. For example, the Proposal Department can carefully select and propose services and products that are truly useful to the user. Specifically, if a user aims to maintain their health, the Proposal Department can propose health management services and fitness products. The Proposal Department analyzes the user's health status and exercise data and proposes the most suitable health management services and fitness products. For example, if a user is not getting enough exercise, the Proposal Department can propose fitness gym membership plans or home training equipment. Also, if a user aims to advance their career, the Proposal Department can propose online courses for skill learning. The Proposal Department analyzes the user's career goals and skill level and proposes the most suitable online courses. For example, if a user wants to acquire a new skill, the Proposal Department can propose relevant online courses and learning materials. Furthermore, if a user wants to enrich their hobbies, the Proposal Department can propose hobby-related products and services. The Proposal Department analyzes the user's hobbies and interests and proposes the most suitable hobby-related products and services. For example, if a user is planning a trip, the Proposal Department can propose travel-related services and products. The Proposal Department analyzes the user's travel plans and budget and proposes the most suitable travel plans and accommodations. This allows the proposal department to suggest useful services and products that meet users' needs and goals, thereby enriching users' lives.

[0034] The data collection unit can collect data from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. For example, the data collection unit can collect heart rate and step count data from a smartwatch. The data collection unit can also collect room temperature and humidity data from environmental sensors. The data collection unit can also collect transaction history data from bank accounts. The data collection unit can also collect dietary data from diet management apps. By collecting diverse data in this way, it is possible to understand the user's mental state and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data obtained from a smartwatch into a generating AI, which can analyze the data to estimate the user's stress level.

[0035] The analysis unit can analyze the collected data in real time. For example, the analysis unit can analyze heart rate data to estimate the user's stress level. For example, the analysis unit can analyze room temperature data to evaluate the user's comfort level. For example, the analysis unit can analyze transaction history data to understand the user's consumption trends. For example, the analysis unit can analyze meal content data to evaluate the user's nutritional balance. This allows for an immediate understanding of the user's situation by analyzing the data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can analyze the data to understand the user's situation.

[0036] The service provider can proactively offer encouragement and advice at the optimal time. For example, if a user is feeling stressed, the service provider can send an encouraging message such as, "It's okay, you're making progress one step at a time." If a user wants to relax, the service provider can suggest, "Let's take a break at your favorite cafe." If a user is aiming to maintain their health, the service provider can suggest an exercise plan. If a user is aiming for career advancement, the service provider can suggest an online course for skill acquisition. By providing encouragement and advice at the optimal time, the service provider can alleviate feelings of isolation in the user. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's situation into a generating AI, which can then generate encouragement and advice at the optimal time.

[0037] The suggestion department can select and propose services and products that are truly useful to the user. For example, if the user is aiming to maintain their health, the suggestion department can propose health management services and fitness products. For example, if the user is aiming for career advancement, the suggestion department can propose online courses for skill learning. For example, if the user wants to enrich their hobbies, the suggestion department can propose hobby-related products and services. For example, if the user is planning a trip, the suggestion department can propose travel-related services and products. In this way, by proposing services and products that are useful to the user, it is possible to support the user in achieving their goals. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's situation into a generating AI, and the generating AI can propose services and products that are useful to the user.

[0038] The suggestion unit can propose concrete action plans to help users efficiently progress towards their goals. For example, if a user aims to maintain their health, the suggestion unit can propose specific exercise and meal plans. If a user aims to advance their career, the suggestion unit can also propose specific skill learning plans. If a user wants to enrich their hobbies, the suggestion unit can also propose specific hobby activity plans. If a user is planning a trip, the suggestion unit can also propose specific travel plans. By proposing concrete action plans, the suggestion unit can efficiently support users in achieving their goals. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can request a generating AI to generate an action plan based on the user's goals, and the generating AI can generate a concrete action plan.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from devices and applications that the user has frequently used in the past. For example, the data collection unit can concentrate data collection during specific time periods based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the most efficient data collection method. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is interested in maintaining their health, the data collection unit will prioritize collecting data related to diet and exercise. For example, if the user is aiming for career advancement, the data collection unit may also prioritize collecting data related to learning and work. The data collection unit can also adjust the type and amount of data collected according to the user's lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can also collect data related to topics of interest from the user's social media activity. For example, the data collection unit can analyze the activity of the user's social media followers and friends and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can also perform a concise analysis on less important data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health data. For example, the analysis unit can also apply financial-related analysis algorithms to financial data. For example, the analysis unit can also apply environmental-related analysis algorithms to environmental data. By applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also lower the priority of analysis of older data. The analysis unit can also adjust the priority of analysis according to the data collection timing. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, which can then determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then adjust the order of analysis.

[0047] The service provider can analyze the user's past responses at the time of delivery to select the most appropriate encouragement or advice. For example, the service provider may prioritize providing encouragement or advice that the user has previously received favorably. The service provider can also select the most appropriate encouragement or advice based on the user's past responses. The service provider can also analyze the user's past responses to provide effective encouragement or advice. This allows the service provider to provide the most appropriate encouragement or advice by analyzing the user's past responses. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past response data into a generating AI, which can then select the most appropriate encouragement or advice.

[0048] The service provider can customize the content of encouragement and advice based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider can provide concise encouragement and advice. For example, if the user is relaxed, the service provider can also provide detailed encouragement and advice. The service provider can also customize the content of encouragement and advice according to the user's living situation. This allows for more appropriate support to be provided by customizing the content of encouragement and advice according to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation data into a generating AI, which can then customize the content of encouragement and advice.

[0049] The service provider can select the most appropriate encouragement and advice by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide encouragement and advice related to that region. For example, if the user is traveling, the service provider can also provide encouragement and advice related to the travel destination. For example, if the user is at home, the service provider can provide encouragement and advice based on information about the area around their home. In this way, by considering the user's geographical location information, it is possible to provide highly relevant encouragement and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then select the most appropriate encouragement and advice.

[0050] The service provider can analyze the user's social media activity at the time of delivery and suggest content for encouragement and advice. For example, the service provider can provide encouragement and advice based on information shared by the user on social media. For example, the service provider can also provide encouragement and advice related to topics of interest based on the user's social media activity. For example, the service provider can analyze the activities of the user's social media followers and friends and provide encouragement and advice. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant encouragement and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into a generating AI, and the generating AI can suggest content for encouragement and advice.

[0051] The proposal unit can adjust the level of detail of its proposals based on the user's goals. For example, if the user has short-term goals, the proposal unit will propose a specific action plan. If the user has long-term goals, the proposal unit can also propose a step-by-step approach. The proposal unit can also adjust the level of detail of its proposals according to the user's goals. This allows the proposal unit to provide appropriate suggestions by adjusting the level of detail based on the user's goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's goal data into a generating AI, which can then adjust the level of detail of its suggestions.

[0052] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, the suggestion unit can apply a health-related suggestion algorithm to a user aiming to maintain their health. For example, the suggestion unit can apply a career-related suggestion algorithm to a user aiming for career advancement. For example, the suggestion unit can apply a hobby-related suggestion algorithm to a user who wants to enrich their hobbies. By applying different suggestion algorithms depending on the user's category, appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user category data into a generating AI, and the generating AI can apply an appropriate suggestion algorithm.

[0053] The proposal unit can prioritize proposals based on the user's goal achievement timeline. For example, if the user has short-term goals, the proposal unit will prioritize proposals related to those goals. If the user has long-term goals, the proposal unit can also provide proposals related to those goals in stages. The proposal unit can also prioritize proposals according to the user's goal achievement timeline. By prioritizing proposals based on the user's goal achievement timeline, proposals can be provided at the appropriate time. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input user goal achievement timeline data into a generating AI, which can then determine the proposal priority.

[0054] The suggestion unit can adjust the order of suggestions based on the user's relevance when making suggestions. For example, the suggestion unit may prioritize highly relevant suggestions. The suggestion unit may also postpone less relevant suggestions. The suggestion unit can also adjust the order of suggestions according to the user's relevance. This allows suggestions to be provided in an appropriate order by adjusting the order of suggestions based on the user's relevance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user relevance data into a generating AI, which can then adjust the order of suggestions.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit learns the user's past behavioral patterns when collecting user data, enabling it to collect data at the optimal time. For example, if a user has a habit of jogging every morning, the unit can prioritize collecting heart rate and step count data during that time. Similarly, if a user has time to relax at night, it can collect stress level and sleep data during that time. Furthermore, if a user engages in specific activities on certain days of the week, it can collect data related to those days. This optimizes the timing of data collection based on the user's behavioral patterns, resulting in more accurate data.

[0057] The analysis unit can detect anomalies by comparing collected data with the user's past data. For example, if a user's heart rate is higher than normal, the analysis unit can detect this anomaly and alert the user. It can also detect sudden changes in a user's consumption patterns and provide feedback to the user. Furthermore, if a user's nutritional balance is unbalanced, it can detect this imbalance and provide advice for improvement. This allows for the early detection of abnormalities in the user's health and lifestyle, prompting appropriate action.

[0058] The suggestion department can customize the content of its suggestions based on the user's goals. For example, if a user aims to maintain their health, the suggestion department can propose specific exercise and meal plans. If a user aims for career advancement, the suggestion department can propose online courses for skill learning or career coaching services. Furthermore, if a user wants to enrich their hobbies, the suggestion department can propose hobby-related events and communities. This allows the department to provide concrete action plans tailored to the user's goals.

[0059] The analytics department can prioritize analysis based on the user's lifestyle and areas of interest when analyzing collected data. For example, if a user is interested in maintaining their health, the analytics department can prioritize analyzing health-related data. Similarly, if a user is aiming for career advancement, the analytics department can prioritize analyzing career-related data. Furthermore, if a user wants to enrich their hobbies, the analytics department can prioritize analyzing hobby-related data. This allows the department to provide appropriate analysis results tailored to the user's areas of interest.

[0060] The proposal department can analyze users' past data and provide optimal suggestions. For example, it can prioritize suggestions that users have previously accepted favorably. It can also provide suggestions at the optimal timing based on users' past behavior patterns. Furthermore, it can select the most effective suggestions for each user based on their past data. This allows the department to provide optimal suggestions based on users' past data.

[0061] The proposal team can prioritize proposals based on the user's goal achievement timeline. For example, if a user has short-term goals, proposals related to those goals can be prioritized. If a user has long-term goals, proposals related to those goals can be presented in stages. Furthermore, the priority of proposals can be adjusted according to the user's goal achievement timeline. This allows for the provision of appropriate proposals based on the user's goal achievement timeline.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user data. The data collection unit can collect data from, for example, wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. Specifically, it can collect heart rate and step count data from smartwatches, room temperature and humidity data from environmental sensors, transaction history data from bank accounts, and diet content data from diet management apps. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data in real time to estimate the user's stress level by analyzing heart rate data, evaluate the user's comfort level by analyzing room temperature data, understand the user's consumption trends by analyzing transaction history data, and evaluate the user's nutritional balance by analyzing meal content data. Step 3: The service provider provides encouragement and advice based on the analysis results obtained by the analysis provider. For example, the service provider can proactively provide encouragement and advice at the optimal time. Specifically, they can send encouraging messages when a user is feeling stressed, suggest taking a break at a cafe when a user wants to relax, suggest an exercise plan when a user is aiming to maintain their health, or suggest an online course for skill learning when a user is aiming for career advancement. Step 4: The Proposal Department proposes useful services and products to the user based on the results provided by the Supply Department. For example, the Proposal Department can carefully select and propose services and products that are truly useful to the user. Specifically, it can propose health management services and fitness products if the user aims to maintain their health, online courses for skill learning if the user aims to advance their career, hobby-related products and services if the user wants to enrich their hobbies, or travel-related services and products if the user is planning a trip.

[0064] (Example of form 2) The Kokorozashi Partner System according to an embodiment of the present invention is an AI agent system that deeply understands the user's goals and aspirations and walks alongside them, providing support in solitude. This Kokorozashi Partner System integrates diverse data obtained from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps to grasp the user's mental state and living situation in real time. The Kokorozashi Partner System proactively provides encouragement and advice at the optimal time. Furthermore, the Kokorozashi Partner System carefully selects and proposes services and products that are truly useful to the user and provides long-term support. The Kokorozashi Partner System is designed as a sustainable ecosystem, and AI agent providers can earn referral rewards from service and product providers. Service and product providers can expect improved accuracy in customer acquisition and a reduction in churn rates as the AI ​​agent carefully selects and proposes products to users. As a result, users can use the Kokorozashi Partner System, which is useful in their lives, free of charge and indefinitely. For example, the Kokorozashi Partner System integrates diverse data obtained from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps to grasp the user's mental state and living situation in real time. During this process, the system detects changes in heart rate and stress levels, and offers encouraging messages and relaxation methods as needed. This helps users reduce feelings of loneliness and maintain emotional stability. Next, the Kokorozashi Partner System proactively provides encouragement and advice at the optimal time. For example, if a user is feeling stressed, the Kokorozashi Partner System sends an encouraging message such as, "It's okay, you're making progress one step at a time." If a user wants to relax, it suggests, "Let's take a break at your favorite cafe." This helps users reduce feelings of loneliness and maintain emotional stability. Furthermore, the Kokorozashi Partner System carefully selects and suggests services and products that are truly useful to the user. For example, if a user is aiming for career advancement, the Kokorozashi Partner System suggests online courses for skill learning. If a user is aiming to maintain their health, the Kokorozashi Partner System suggests an exercise plan.This allows users to efficiently progress towards their goals. The Kokorozashi Partner System is designed as a sustainable ecosystem, where AI agent providers can earn referral rewards from service and product providers. Service and product providers can expect improved customer acquisition accuracy and reduced churn rates as AI agents carefully select and recommend products to users. This allows users to use the Kokorozashi Partner System, which is useful in life, free of charge and indefinitely. In this way, the Kokorozashi Partner System greatly contributes to the enrichment of people's well-being and the improvement of healthy social productivity by supporting people in overcoming loneliness and achieving their aspirations. In this way, the Kokorozashi Partner System can support users in achieving their goals and alleviate feelings of loneliness.

[0065] The Kokorozashi Partner System according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a data proposal unit. The data collection unit collects user data. The data collection unit can collect data from, for example, wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. For example, the data collection unit can collect heart rate and step count data from a smartwatch. The data collection unit can also collect room temperature and humidity data from environmental sensors. Furthermore, the data collection unit can collect transaction history data from bank accounts. For example, the data collection unit can collect meal content data from a meal management app. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data in real time. For example, the analysis unit can analyze heart rate data to estimate the user's stress level. Furthermore, the analysis unit can analyze room temperature data to evaluate the user's comfort level. Furthermore, the analysis unit can analyze transaction history data to understand the user's consumption trends. For example, the analysis unit can analyze meal content data to evaluate the user's nutritional balance. The Service Department provides encouragement and advice based on the analysis results obtained by the Analysis Department. For example, the Service Department can proactively provide encouragement and advice at the optimal time. For example, if a user is feeling stressed, the Service Department can send an encouraging message such as, "It's okay, you're making progress one step at a time." Also, if a user wants to relax, the Service Department can suggest, "Let's take a break at your favorite cafe." Furthermore, if a user is aiming to maintain their health, the Service Department can suggest an exercise plan. For example, if a user is aiming for career advancement, the Service Department can suggest an online course for skill acquisition. The Recommendation Department proposes useful services and products to users based on the results provided by the Service Department. For example, the Recommendation Department can carefully select and propose services and products that are truly useful to the user. For example, if a user is aiming to maintain their health, the Recommendation Department can suggest health management services or fitness products.Furthermore, the suggestion department can also suggest online courses for skill acquisition if the user is aiming for career advancement. In addition, if the user wants to enrich their hobbies, the suggestion department can suggest hobby-related products and services. For example, if the user is planning a trip, the suggestion department can suggest travel-related services and products. In this way, the aspiration partner system according to the embodiment can support the user in achieving their goals and reduce feelings of loneliness.

[0066] The data collection unit collects user data. For example, the data collection unit can collect data from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. Specifically, it can collect heart rate and step count data from smartwatches. Smartwatches are worn on the user's wrist and use heart rate sensors and accelerometers to measure the user's heart rate and step count in real time. This data is transmitted to the data collection unit via Bluetooth or Wi-Fi. The data collection unit can also collect room temperature and humidity data from environmental sensors. Environmental sensors are installed in the user's living space and use room temperature and humidity sensors to measure the indoor temperature and humidity. This data is periodically transmitted to the data collection unit and used to evaluate the user's comfort level. Furthermore, the data collection unit can also collect transaction history data from bank accounts. Bank account transaction history data is important for understanding the user's consumption trends and economic situation, and the data collection unit obtains this data through the bank's API with the user's consent. For example, the data collection unit can also collect meal content data from diet management apps. A meal management app is an application that allows users to record their daily meals, and it transmits the meal details and calorie information entered by the user to a data collection unit. This allows the collection unit to gather data to evaluate the user's health status and nutritional balance. The collection unit centrally manages the data collected from these various devices and applications, making it accessible to the analysis and provision units. This allows the collection unit to efficiently collect multifaceted user data and improve the overall system performance.

[0067] The analysis unit analyzes data collected by the data collection unit. For example, the analysis unit can analyze collected data in real time. Specifically, it can analyze heart rate data to estimate the user's stress level. Heart rate data shows fluctuations in the user's heart rate, and by analyzing this, it is possible to estimate whether the user is experiencing stress. For example, if the heart rate rises sharply, it is likely that the user is experiencing stress. The analysis unit can also analyze room temperature data to evaluate the user's comfort level. Room temperature data shows the temperature of the user's living space, and by analyzing this, it is possible to identify the temperature range in which the user can live comfortably. For example, if the room temperature is too high, the user may feel uncomfortable, and appropriate temperature adjustment is necessary. Furthermore, the analysis unit can analyze transaction history data to understand the user's consumption trends. Transaction history data shows the user's economic activity, and by analyzing this, it is possible to understand the user's consumption patterns and spending trends. For example, if spending on a particular category is increasing, it is possible to identify the user's interests and needs. The analysis unit can comprehensively analyze this data to gain a detailed understanding of the user's behavior and state. This allows the analysis department to provide users with the foundational data necessary to offer appropriate advice and suggestions.

[0068] The service provider offers encouragement and advice based on the analysis results obtained by the analysis provider. For example, the service provider can proactively provide encouragement and advice at the optimal time. Specifically, if a user is feeling stressed, it can send an encouraging message such as, "It's okay, you're making progress one step at a time." The service provider monitors the user's heart rate data in real time and sends an encouraging message at the appropriate time if it determines that the stress level has increased. The service provider can also suggest, for example, "Let's take a break at your favorite cafe" if the user wants to relax. The service provider analyzes the user's environmental and behavioral data and suggests places and activities where the user can relax. Furthermore, if the service provider is aiming to maintain their health, it can also suggest an exercise plan. The service provider analyzes the user's exercise data and health status and suggests an optimal exercise plan for the user. For example, if the user is not getting enough exercise, it can suggest light exercise such as walking or jogging to support the user's health maintenance. In addition, if the service provider is aiming for career advancement, it can suggest online courses for skill learning. The service provider analyzes the user's career goals and skill level and suggests an optimal online course for the user. This allows the service provider to support users in achieving their goals and increase their motivation.

[0069] The Proposal Department proposes useful services and products to users based on the results provided by the Service Department. For example, the Proposal Department can carefully select and propose services and products that are truly useful to the user. Specifically, if a user aims to maintain their health, the Proposal Department can propose health management services and fitness products. The Proposal Department analyzes the user's health status and exercise data and proposes the most suitable health management services and fitness products. For example, if a user is not getting enough exercise, the Proposal Department can propose fitness gym membership plans or home training equipment. Also, if a user aims to advance their career, the Proposal Department can propose online courses for skill learning. The Proposal Department analyzes the user's career goals and skill level and proposes the most suitable online courses. For example, if a user wants to acquire a new skill, the Proposal Department can propose relevant online courses and learning materials. Furthermore, if a user wants to enrich their hobbies, the Proposal Department can propose hobby-related products and services. The Proposal Department analyzes the user's hobbies and interests and proposes the most suitable hobby-related products and services. For example, if a user is planning a trip, the Proposal Department can propose travel-related services and products. The Proposal Department analyzes the user's travel plans and budget and proposes the most suitable travel plans and accommodations. This allows the proposal department to suggest useful services and products that meet users' needs and goals, thereby enriching users' lives.

[0070] The data collection unit can collect data from wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. For example, the data collection unit can collect heart rate and step count data from a smartwatch. The data collection unit can also collect room temperature and humidity data from environmental sensors. The data collection unit can also collect transaction history data from bank accounts. The data collection unit can also collect dietary data from diet management apps. By collecting diverse data in this way, it is possible to understand the user's mental state and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data obtained from a smartwatch into a generating AI, which can analyze the data to estimate the user's stress level.

[0071] The analysis unit can analyze the collected data in real time. For example, the analysis unit can analyze heart rate data to estimate the user's stress level. For example, the analysis unit can analyze room temperature data to evaluate the user's comfort level. For example, the analysis unit can analyze transaction history data to understand the user's consumption trends. For example, the analysis unit can analyze meal content data to evaluate the user's nutritional balance. This allows for an immediate understanding of the user's situation by analyzing the data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can analyze the data to understand the user's situation.

[0072] The service provider can proactively offer encouragement and advice at the optimal time. For example, if a user is feeling stressed, the service provider can send an encouraging message such as, "It's okay, you're making progress one step at a time." If a user wants to relax, the service provider can suggest, "Let's take a break at your favorite cafe." If a user is aiming to maintain their health, the service provider can suggest an exercise plan. If a user is aiming for career advancement, the service provider can suggest an online course for skill acquisition. By providing encouragement and advice at the optimal time, the service provider can alleviate feelings of isolation in the user. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's situation into a generating AI, which can then generate encouragement and advice at the optimal time.

[0073] The suggestion department can select and propose services and products that are truly useful to the user. For example, if the user is aiming to maintain their health, the suggestion department can propose health management services and fitness products. For example, if the user is aiming for career advancement, the suggestion department can propose online courses for skill learning. For example, if the user wants to enrich their hobbies, the suggestion department can propose hobby-related products and services. For example, if the user is planning a trip, the suggestion department can propose travel-related services and products. In this way, by proposing services and products that are useful to the user, it is possible to support the user in achieving their goals. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's situation into a generating AI, and the generating AI can propose services and products that are useful to the user.

[0074] The suggestion unit can propose concrete action plans to help users efficiently progress towards their goals. For example, if a user aims to maintain their health, the suggestion unit can propose specific exercise and meal plans. If a user aims to advance their career, the suggestion unit can also propose specific skill learning plans. If a user wants to enrich their hobbies, the suggestion unit can also propose specific hobby activity plans. If a user is planning a trip, the suggestion unit can also propose specific travel plans. By proposing concrete action plans, the suggestion unit can efficiently support users in achieving their goals. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can request a generating AI to generate an action plan based on the user's goals, and the generating AI can generate a concrete action plan.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to obtain more detailed data. For example, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it according to the user's situation. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the timing of data collection.

[0076] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from devices and applications that the user has frequently used in the past. For example, the data collection unit can concentrate data collection during specific time periods based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the most efficient data collection method. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0077] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is interested in maintaining their health, the data collection unit will prioritize collecting data related to diet and exercise. For example, if the user is aiming for career advancement, the data collection unit may also prioritize collecting data related to learning and work. The data collection unit can also adjust the type and amount of data collected according to the user's lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI, which can then filter the data.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate and stress level data. For example, if the user is relaxed, the data collection unit may prioritize collecting data related to lifestyle and hobbies. For example, if the user is in a hurry, the data collection unit may prioritize collecting only important data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and determine the priority of data to be collected by the generative AI.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can also collect data related to topics of interest from the user's social media activity. For example, the data collection unit can analyze the activity of the user's social media followers and friends and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can also perform a concise analysis on less important data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health data. For example, the analysis unit can also apply financial-related analysis algorithms to financial data. For example, the analysis unit can also apply environmental-related analysis algorithms to environmental data. By applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can also provide a brief analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also lower the priority of analysis of older data. The analysis unit can also adjust the priority of analysis according to the data collection timing. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, which can then determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then adjust the order of analysis.

[0087] The service provider can estimate the user's emotions and adjust the way encouragement and advice are expressed based on the estimated emotions. For example, if the user is stressed, the service provider can offer encouragement and advice in gentle words. For example, if the user is relaxed, the service provider can offer detailed advice. For example, if the user is in a hurry, the service provider can offer concise advice. By adjusting the way encouragement and advice are expressed according to the user's emotions, they can be delivered in a way that is easily accepted by the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the way encouragement and advice are expressed.

[0088] The service provider can analyze the user's past responses at the time of delivery to select the most appropriate encouragement or advice. For example, the service provider may prioritize providing encouragement or advice that the user has previously received favorably. The service provider can also select the most appropriate encouragement or advice based on the user's past responses. The service provider can also analyze the user's past responses to provide effective encouragement or advice. This allows the service provider to provide the most appropriate encouragement or advice by analyzing the user's past responses. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past response data into a generating AI, which can then select the most appropriate encouragement or advice.

[0089] The service provider can customize the content of encouragement and advice based on the user's current living situation at the time of delivery. For example, if the user is busy, the service provider can provide concise encouragement and advice. For example, if the user is relaxed, the service provider can also provide detailed encouragement and advice. The service provider can also customize the content of encouragement and advice according to the user's living situation. This allows for more appropriate support to be provided by customizing the content of encouragement and advice according to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation data into a generating AI, which can then customize the content of encouragement and advice.

[0090] The service provider can estimate the user's emotions and prioritize encouragement and advice based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize providing encouraging messages. For example, if the user is relaxed, the service provider may also prioritize providing advice messages. The service provider can also prioritize encouragement and advice according to the user's emotions. This allows for timely support by prioritizing encouragement and advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then determine the priority of encouragement and advice.

[0091] The service provider can select the most appropriate encouragement and advice by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide encouragement and advice related to that region. For example, if the user is traveling, the service provider can also provide encouragement and advice related to the travel destination. For example, if the user is at home, the service provider can provide encouragement and advice based on information about the area around their home. In this way, by considering the user's geographical location information, it is possible to provide highly relevant encouragement and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then select the most appropriate encouragement and advice.

[0092] The service provider can analyze the user's social media activity at the time of delivery and suggest content for encouragement and advice. For example, the service provider can provide encouragement and advice based on information shared by the user on social media. For example, the service provider can also provide encouragement and advice related to topics of interest based on the user's social media activity. For example, the service provider can analyze the activities of the user's social media followers and friends and provide encouragement and advice. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant encouragement and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into a generating AI, and the generating AI can suggest content for encouragement and advice.

[0093] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present suggestions in gentle language. If the user is relaxed, the suggestion unit may present detailed suggestions. If the user is in a hurry, the suggestion unit may present concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are more acceptable to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way it presents suggestions.

[0094] The proposal unit can adjust the level of detail of its proposals based on the user's goals. For example, if the user has short-term goals, the proposal unit will propose a specific action plan. If the user has long-term goals, the proposal unit can also propose a step-by-step approach. The proposal unit can also adjust the level of detail of its proposals according to the user's goals. This allows the proposal unit to provide appropriate suggestions by adjusting the level of detail based on the user's goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's goal data into a generating AI, which can then adjust the level of detail of its suggestions.

[0095] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, the suggestion unit can apply a health-related suggestion algorithm to a user aiming to maintain their health. For example, the suggestion unit can apply a career-related suggestion algorithm to a user aiming for career advancement. For example, the suggestion unit can apply a hobby-related suggestion algorithm to a user who wants to enrich their hobbies. By applying different suggestion algorithms depending on the user's category, appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user category data into a generating AI, and the generating AI can apply an appropriate suggestion algorithm.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. If the user is in a hurry, for example, the suggestion unit can provide brief suggestions. By adjusting the length of suggestions according to the user's emotions, the system can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions.

[0097] The proposal unit can prioritize proposals based on the user's goal achievement timeline. For example, if the user has short-term goals, the proposal unit will prioritize proposals related to those goals. If the user has long-term goals, the proposal unit can also provide proposals related to those goals in stages. The proposal unit can also prioritize proposals according to the user's goal achievement timeline. By prioritizing proposals based on the user's goal achievement timeline, proposals can be provided at the appropriate time. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input user goal achievement timeline data into a generating AI, which can then determine the proposal priority.

[0098] The suggestion unit can adjust the order of suggestions based on the user's relevance when making suggestions. For example, the suggestion unit may prioritize highly relevant suggestions. The suggestion unit may also postpone less relevant suggestions. The suggestion unit can also adjust the order of suggestions according to the user's relevance. This allows suggestions to be provided in an appropriate order by adjusting the order of suggestions based on the user's relevance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user relevance data into a generating AI, which can then adjust the order of suggestions.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit learns the user's past behavioral patterns when collecting user data, enabling it to collect data at the optimal time. For example, if a user has a habit of jogging every morning, the unit can prioritize collecting heart rate and step count data during that time. Similarly, if a user has time to relax at night, it can collect stress level and sleep data during that time. Furthermore, if a user engages in specific activities on certain days of the week, it can collect data related to those days. This optimizes the timing of data collection based on the user's behavioral patterns, resulting in more accurate data.

[0101] The analysis unit can detect anomalies by comparing collected data with the user's past data. For example, if a user's heart rate is higher than normal, the analysis unit can detect this anomaly and alert the user. It can also detect sudden changes in a user's consumption patterns and provide feedback to the user. Furthermore, if a user's nutritional balance is unbalanced, it can detect this imbalance and provide advice for improvement. This allows for the early detection of abnormalities in the user's health and lifestyle, prompting appropriate action.

[0102] The service provider can estimate the user's emotions and customize the content of encouragement and advice based on those estimates. For example, if a user is feeling stressed, the service provider can offer advice on relaxation methods and stress relief. If a user wants to boost their motivation, the service provider can offer encouraging messages to help them achieve their goals. Furthermore, if a user is feeling down, the service provider can improve their mood by sending positive messages. This allows the service provider to offer appropriate support tailored to the user's emotions.

[0103] The suggestion department can customize the content of its suggestions based on the user's goals. For example, if a user aims to maintain their health, the suggestion department can propose specific exercise and meal plans. If a user aims for career advancement, the suggestion department can propose online courses for skill learning or career coaching services. Furthermore, if a user wants to enrich their hobbies, the suggestion department can propose hobby-related events and communities. This allows the department to provide concrete action plans tailored to the user's goals.

[0104] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to obtain more detailed data. Furthermore, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it according to the user's situation. In this way, the user's burden can be reduced by adjusting the frequency of data collection according to the user's emotions.

[0105] The analytics department can prioritize analysis based on the user's lifestyle and areas of interest when analyzing collected data. For example, if a user is interested in maintaining their health, the analytics department can prioritize analyzing health-related data. Similarly, if a user is aiming for career advancement, the analytics department can prioritize analyzing career-related data. Furthermore, if a user wants to enrich their hobbies, the analytics department can prioritize analyzing hobby-related data. This allows the department to provide appropriate analysis results tailored to the user's areas of interest.

[0106] The service provider can estimate the user's emotions and adjust the way encouragement and advice are expressed based on those estimates. For example, if the user is stressed, the service provider can offer encouragement and advice in gentle words. If the user is relaxed, the service provider can offer detailed advice. Furthermore, if the user is in a hurry, the service provider can offer concise advice. This allows the service provider to offer support in an appropriate manner according to the user's emotions.

[0107] The proposal department can analyze users' past data and provide optimal suggestions. For example, it can prioritize suggestions that users have previously accepted favorably. It can also provide suggestions at the optimal timing based on users' past behavior patterns. Furthermore, it can select the most effective suggestions for each user based on their past data. This allows the department to provide optimal suggestions based on users' past data.

[0108] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting heart rate and stress level data. If the user is relaxed, the unit can prioritize collecting data related to lifestyle and hobbies. Furthermore, if the user is in a hurry, the unit can prioritize collecting only the most important data. In this way, by prioritizing data according to the user's emotions, it is possible to prioritize the collection of important data.

[0109] The proposal team can prioritize proposals based on the user's goal achievement timeline. For example, if a user has short-term goals, proposals related to those goals can be prioritized. If a user has long-term goals, proposals related to those goals can be presented in stages. Furthermore, the priority of proposals can be adjusted according to the user's goal achievement timeline. This allows for the provision of appropriate proposals based on the user's goal achievement timeline.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user data. The data collection unit can collect data from, for example, wearable devices, environmental sensors, bank accounts, and diet / exercise management apps. Specifically, it can collect heart rate and step count data from smartwatches, room temperature and humidity data from environmental sensors, transaction history data from bank accounts, and diet content data from diet management apps. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data in real time to estimate the user's stress level by analyzing heart rate data, evaluate the user's comfort level by analyzing room temperature data, understand the user's consumption trends by analyzing transaction history data, and evaluate the user's nutritional balance by analyzing meal content data. Step 3: The service provider provides encouragement and advice based on the analysis results obtained by the analysis provider. For example, the service provider can proactively provide encouragement and advice at the optimal time. Specifically, they can send encouraging messages when a user is feeling stressed, suggest taking a break at a cafe when a user wants to relax, suggest an exercise plan when a user is aiming to maintain their health, or suggest an online course for skill learning when a user is aiming for career advancement. Step 4: The Proposal Department proposes useful services and products to the user based on the results provided by the Supply Department. For example, the Proposal Department can carefully select and propose services and products that are truly useful to the user. Specifically, it can propose health management services and fitness products if the user aims to maintain their health, online courses for skill learning if the user aims to advance their career, hobby-related products and services if the user wants to enrich their hobbies, or travel-related services and products if the user is planning a trip.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects data from wearable devices and environmental sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the smart device 14 and provides encouragement and advice at the optimal timing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes useful services and products to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects data from wearable devices and environmental sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides encouragement and advice at the optimal timing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes useful services and products to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects data from wearable devices and environmental sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides encouragement and advice at the optimal timing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes useful services and products to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects data from wearable devices and environmental sensors. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The provision unit is implemented by the control unit 46A of the robot 414 and provides encouragement and advice at the optimal timing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes useful services and products to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides encouragement and advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that proposes useful services and products to the user based on the results provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from wearable devices, environmental sensors, bank accounts, and diet and exercise management apps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the collected data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Proactively provide encouragement and advice at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We carefully select and propose services and products that are truly useful to our users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose concrete action plans to help users efficiently progress towards their goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way encouragement and advice are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the system analyzes the user's past responses to select the most appropriate encouragement and advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the content of encouragement and advice will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes encouragement and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the system selects the most appropriate encouragement and advice, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest encouraging and advice content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, prioritize them based on the user's target achievement date. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on user relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides encouragement and advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that proposes useful services and products to the user based on the results provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from wearable devices, environmental sensors, bank accounts, and diet and exercise management apps. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the collected data in real time. The system according to feature 1.

4. The aforementioned supply unit is, Proactively provide encouragement and advice at the optimal time. The system according to feature 1.

5. The aforementioned proposal section is, We carefully select and propose services and products that are truly useful to our users. The system according to feature 1.

6. The aforementioned proposal section is, We propose concrete action plans to help users efficiently progress towards their goals. The system according to feature 1.

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

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

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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