system

Abstract

The system according to this embodiment aims to propose individual lifestyle plans based on the user's interests and behavioral patterns, and to provide information in real time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's interests and behavioral patterns. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a lifestyle plan based on the analysis results obtained by the analysis unit. The provision unit provides information in real time based on the plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose an individual lifestyle plan based on the user's interests and behavior patterns and provide information in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an individual lifestyle plan based on the user's interests and behavior patterns and provide information in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects the user's interests and behavioral patterns. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a lifestyle plan based on the analysis results obtained by the analysis unit. The provision unit provides information in real time based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose individual lifestyle plans based on the user's interests and behavioral patterns, and provide information in real 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 multiple 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 receiving 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 receiving 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 lifestyle optimization system according to an embodiment of the present invention is a system that proposes an individualized lifestyle plan based on the user's interests and behavioral patterns, streamlines time management, and provides a daily plan that balances elements of health, hobbies, and entertainment. The lifestyle optimization system collects the user's interests and behavioral patterns, and a generating AI analyzes them. For example, it understands what hobbies the user has and what activities they are interested in. Next, the generating AI proposes an optimal lifestyle plan to the user based on the analysis results. This may include exercise plans and dietary advice for health management, suggestions for hobby activities, and recommendations for entertainment content. Furthermore, it integrates data from map services and healthcare services to provide real-time information and support. For example, it utilizes data from map services and healthcare services to provide real-time hospital reservations and navigation support. In addition, users can receive notifications of appointments and tasks, as well as important reminders, through messages. This mechanism allows users to easily access optimal activities and content even in their busy daily lives, improving their quality of life. For example, if a user wants to go somewhere on the weekend, the system utilizes data from map services to suggest activities, tourist spots, and restaurants in their destination. Furthermore, it utilizes community features to provide a platform for connecting with people who share similar hobbies. The lifestyle optimization system aims to enrich, streamline, and enhance users' daily lives, utilizing generative AI and big data to provide optimal plans and information tailored to each user's individual needs. This aims to improve users' quality of life and enable them to achieve a highly satisfying lifestyle. In this way, the lifestyle optimization system can improve users' quality of life and support a fulfilling daily life.

[0029] The lifestyle optimization system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's interests and behavioral patterns. For example, the collection unit can collect the user's purchase history, browsing history, location information, etc. The collection unit can also collect data related to the user's hobbies and activities. For example, the collection unit can collect what sports the user likes, what books they read, and what travel destinations they are interested in. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and identify the user's interests and behavioral patterns. For example, the analysis unit can analyze the user's purchase history to identify what products the user is interested in. The analysis unit can also analyze the user's browsing history to identify what websites the user visits. The proposal unit proposes a lifestyle plan based on the analysis results obtained by the analysis unit. For example, the proposal unit uses AI to propose an optimal lifestyle plan for the user. For example, the proposal unit proposes an exercise plan and dietary advice for the user's health management. The proposal unit can also suggest the user's hobby activities. For example, the suggestion unit suggests sports, reading material, and travel destinations that the user is interested in. The provision unit provides information in real time based on the plan suggested by the suggestion unit. The provision unit can, for example, use AI to provide real-time hospital reservations and navigation support. For example, when a user makes a hospital reservation, the provision unit suggests the most suitable hospital and supports the reservation process. The provision unit can also provide navigation support to help the user reach their destination. For example, the provision unit suggests the optimal route from the user's current location to their destination and provides navigation. In this way, the lifestyle optimization system according to the embodiment can improve the user's quality of life and support a fulfilling daily life.

[0030] The data collection unit collects user interests and behavioral patterns. For example, it can collect user purchase history, browsing history, and location information. Specifically, it obtains the history of products purchased by users on online shopping sites and analyzes what product categories they are interested in. It also collects the history of websites visited by users to understand what kind of content they are interested in. Regarding location information, it can use the GPS function of smartphones to record the places users have visited and their movement patterns. Furthermore, the data collection unit can also collect data on users' hobbies and activities. For example, it obtains information on events users have attended and tickets they have purchased to collect information on what sports users like, what books they read, and what travel destinations they are interested in. It can also collect data such as the frequency and type of exercise and calories burned from fitness trackers and smartwatches used by users. As a result, the data collection unit can gain a detailed understanding of users' diverse interests and behavioral patterns and provide foundational data to propose optimal lifestyle plans for individual users. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analytics department analyzes the data collected by the data collection department. For example, the analytics department uses AI to analyze the collected data and identify user interests and behavioral patterns. Specifically, the AI ​​uses machine learning algorithms to analyze users' purchase history and identify what kinds of products they are interested in. For example, it analyzes products that users frequently purchase and their interest in specific product categories. It can also analyze users' browsing history to identify what kinds of websites they visit. This allows the analytics department to understand the topics and themes that users are interested in. Furthermore, the analytics department analyzes users' location information to identify user movement patterns and trends in places they visit. For example, by analyzing places that users frequently visit and places they visit at specific times of day, the analytics department can understand users' lifestyles and behavioral patterns. The analytics department integrates this data to comprehensively analyze user interests and behavioral patterns. This allows the analytics department to obtain detailed insights into users' lifestyles. Furthermore, the analytics department can also use historical data and statistical information to analyze long-term trends and patterns. For example, based on past purchase and browsing history, it can predict changes in user interests and behavior and identify future needs. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes lifestyle plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department uses AI to propose the optimal lifestyle plan for the user. Specifically, the AI ​​proposes exercise plans and dietary advice for the user's health management. For instance, based on the user's exercise data, it suggests appropriate exercise volume and type, and provides specific actions to improve the user's health. It can also analyze the user's dietary history and health data to propose a nutritionally balanced meal plan. Furthermore, the Proposal Department can also suggest the user's hobbies and activities. For example, it suggests sports, books, and travel destinations that the user is interested in. Specifically, based on the user's past activity data, it suggests new sports and events that the user can enjoy, providing ideas to enrich the user's lifestyle. It also suggests books in genres the user is interested in and travel destinations they want to visit, providing specific actions to pique the user's interest. The Proposal Department can customize these suggestions to match the user's lifestyle, providing the optimal plan for each individual user. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of the user implementing the suggested plan and revise the suggestions based on those results. Furthermore, the proposal department can flexibly adjust its proposals in response to changes in the user's lifestyle. This allows the proposal department to consistently provide users with the optimal lifestyle plan, thereby improving their quality of life.

[0033] The service provider provides information in real time based on the plan proposed by the proposal provider. For example, the service provider uses AI to provide real-time hospital reservations and navigation support. Specifically, when a user makes a hospital reservation, it suggests the most suitable hospital and supports the reservation process. For example, it suggests the most suitable hospital and department based on the user's current location and past medical history, and automates the reservation process. The service provider can also provide navigation support to help users reach their destinations. For example, it suggests the best route from the user's current location to their destination and provides navigation. This allows users to reach their destinations efficiently. Furthermore, the service provider can provide information related to the user's lifestyle in real time. For example, it provides information on events and activities that the user is interested in and supports the registration process. It can also provide information on travel destinations that the user wants to visit and supports travel planning. The service provider can customize this information to match the user's lifestyle and provide the most suitable information for each individual user. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, it can provide feedback on the results of actions taken by users based on the provided information and revise the services based on those results. Furthermore, the service provider can flexibly adjust the content of its offerings in accordance with changes in the user's lifestyle. This allows the service provider to always provide users with the most optimal information and improve their quality of life.

[0034] The data collection unit can collect data about the user's hobbies and activities. For example, it can collect information such as what sports the user likes, what books they read, and what travel destinations they are interested in. The data collection unit can also collect, for example, the history of events and activities the user has participated in. Furthermore, the data collection unit can collect information about hobbies and activities that the user has shared on social media. By collecting data about the user's hobbies and activities, it is possible to provide a more personalized lifestyle plan. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media posts into a generating AI and extract information about hobbies and activities.

[0035] The analysis unit can analyze collected data to identify user interests and behavioral patterns. For example, the analysis unit can use AI to analyze collected data and identify user interests and behavioral patterns. For example, the analysis unit can analyze a user's purchase history to identify what kinds of products the user is interested in. The analysis unit can also analyze a user's browsing history to identify what kinds of websites the user visits. Furthermore, the analysis unit can analyze a user's location information to identify what kinds of places the user is interested in. By identifying user interests and behavioral patterns, it is possible to propose more accurate lifestyle plans. 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 user purchase history data into a generating AI to identify interests and behavioral patterns.

[0036] The suggestion unit can propose exercise plans and dietary advice for health management. For example, the suggestion unit can use AI to propose the optimal exercise plan and dietary advice to the user. For example, based on the user's health condition and lifestyle, the suggestion unit can suggest how many times a week to exercise and what kind of diet to eat. The suggestion unit can also propose calorie-restricted or nutritionally balanced meal plans according to the user's goals. In this way, by proposing exercise plans and dietary advice for health management, it can support the user in maintaining their health. 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 the user's health data into a generating AI and generate the optimal exercise plan and dietary advice.

[0037] The suggestion unit can suggest hobby activities. For example, the suggestion unit can use AI to suggest the most suitable hobby activities for the user. For example, the suggestion unit can suggest hobby activities such as sports, reading, and travel based on the user's interests and behavioral patterns. The suggestion unit can also suggest ways for the user to discover new hobbies. For example, the suggestion unit can suggest activities or events that the user has not yet tried. In this way, by suggesting hobby activities, the quality of life of the user can be improved. 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 the user's hobby data into a generating AI and generate the most suitable hobby activities.

[0038] The recommendation unit can recommend entertainment content. For example, the recommendation unit can use AI to recommend the most suitable entertainment content to the user. For example, the recommendation unit can recommend entertainment content such as movies, music, and games based on the user's interests and behavioral patterns. The recommendation unit can also make recommendations to help the user discover new entertainment content. For example, the recommendation unit can recommend movies the user has not yet watched or music the user has not yet listened to. In this way, by recommending entertainment content, the user's entertainment experience can be improved. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's entertainment data into a generation AI and generate the most suitable entertainment content.

[0039] The service provider can offer real-time hospital booking and navigation support. For example, the service provider can use AI to provide real-time hospital booking and navigation support. For example, when a user makes a hospital booking, the service provider can suggest the most suitable hospital and support the booking process. The service provider can also provide navigation support to help the user reach their destination. For example, the service provider can suggest the best route from the user's current location to their destination and provide navigation. This improves user convenience by providing real-time hospital booking and navigation support. 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 location information into a generating AI and generate the best route.

[0040] The service provider can provide notifications of appointments and tasks, as well as important reminders, through messages. For example, the service provider can use AI to provide users with notifications of appointments and tasks, as well as important reminders. For example, the service provider can analyze the user's calendar information and send meeting notifications and deadline reminders. The service provider can also integrate with the user's task management app and notify them of task progress. In this way, by providing notifications of appointments and tasks, as well as important reminders, through messages, the service provider can support the user's time management. Some or all of the above processes 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 calendar data into a generating AI to generate the optimal notification timing.

[0041] The data collection unit can analyze a user's past behavior history and select the optimal data collection method. For example, the data collection unit can use AI to analyze a user's past behavior history and select the optimal data collection method. For example, the data collection unit can collect data from apps and services that the user has frequently used in the past. The data collection unit can also analyze a user's past behavior patterns and determine the optimal timing for data collection. Furthermore, the data collection unit can collect data from content that the user has shown interest in in the past. This allows the optimal data collection method to be selected by analyzing a user's past behavior 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 user behavior history data into a generating AI and generate the optimal data collection method.

[0042] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can use AI to filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics that the user is currently interested in. The data collection unit can also filter data according to the user's current lifestyle (e.g., childcare, work). Furthermore, the data collection unit can collect relevant data based on the user's current health status. This allows for the collection of more relevant data by filtering data based on the user's current 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 user lifestyle data into a generating AI to generate an optimal data filtering method.

[0043] 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, the data collection unit can use AI to prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information related to the user's current location. The data collection unit can also collect information on nearby restaurants and cafes based on the user's geographical location. Furthermore, the data collection unit can collect information on nearby tourist attractions and activities based on the user's location information. In this way, by considering the user's geographical location information, highly relevant data can be prioritized for collection. 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 location information data into a generating AI to generate an optimal data collection method.

[0044] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can use AI to analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by the user on social media. The data collection unit can also analyze the activity of the user's followers and friends on social media and collect relevant data. Furthermore, the data collection unit can collect data based on topics the user has shown interest in on social media. 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 data into a generating AI to generate an optimal data collection method.

[0045] 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 use AI to adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, more 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 importance of the data into a generating AI and generate the optimal level of analysis detail.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can use AI to apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm specialized for health management to health data. It can also apply an analysis algorithm for entertainment content to entertainment data. Furthermore, it can apply an analysis algorithm specialized for hobbies to hobby activity data. By applying different analysis algorithms depending on the data category, more 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 generate the optimal analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can use AI to determine the priority of analysis based on the data collection period. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the data collection period, more 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 collection period into a generating AI to generate the optimal analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can use AI to adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data into a generating AI to generate the optimal analysis order.

[0049] The proposal unit can adjust the level of detail of a proposal based on the importance of the plan. For example, the proposal unit can use AI to adjust the level of detail based on the importance of the plan. For example, the proposal unit will provide a detailed proposal for plans of high importance. It can also provide a concise proposal for plans of low importance. Furthermore, it can provide a proposal with an appropriate level of detail for plans of medium importance. By adjusting the level of detail of a proposal based on the importance of the plan, it becomes possible to provide more appropriate proposals. 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 importance of the plan into a generating AI and generate the optimal level of detail for the proposal.

[0050] The proposal unit can apply different proposal algorithms depending on the plan category when making a proposal. For example, the proposal unit can use AI to apply different proposal algorithms depending on the plan category. For example, the proposal unit can apply a proposal algorithm specialized for health management to a health management plan. It can also apply an entertainment content proposal algorithm to an entertainment plan. Furthermore, it can apply a proposal algorithm specialized for hobbies to a hobby activity plan. By applying different proposal algorithms depending on the plan category, more appropriate proposals can be made. 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 plan category into a generating AI and generate the optimal proposal algorithm.

[0051] The proposal department can determine the priority of proposals based on the submission date of each plan. For example, the proposal department can use AI to determine the priority of proposals based on the submission date. For example, the proposal department will prioritize the most recent plan. The proposal department can also propose the latest plan while referring to past plans. Furthermore, the proposal department can prioritize plans submitted within a specific period. This allows for more appropriate proposals by determining the priority of proposals based on the submission date. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the plan submission date into a generating AI to generate the optimal proposal priority.

[0052] The proposal unit can adjust the order of proposals based on the relevance of the plans during the proposal process. For example, the proposal unit can use AI to adjust the order of proposals based on the relevance of the plans. For example, the proposal unit can prioritize proposing plans with high relevance. It can also postpone proposing plans with low relevance. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the plans. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the plans. 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 relevance of the plans into a generating AI to generate the optimal proposal order.

[0053] The information delivery unit can select the optimal delivery method by referring to the user's past behavior history when providing information. For example, the information delivery unit can use AI to select the optimal delivery method by referring to the user's past behavior history when providing information. For example, the information delivery unit can prioritize selecting information delivery methods that the user has used in the past. The information delivery unit can also analyze the user's past behavior history and select the optimal information delivery method. Furthermore, the information delivery unit can select information delivery methods that the user has shown interest in in the past. In this way, the optimal information delivery method can be selected by referring to the user's past behavior history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input user behavior history data into a generating AI and generate the optimal information delivery method.

[0054] The information provider can customize the means of information delivery based on the user's current living situation. For example, the information provider can use AI to customize the means of information delivery based on the user's current living situation. For example, the information provider can prioritize providing information related to topics that the user is currently interested in. The information provider can also customize the means of information delivery according to the user's current living situation (e.g., childcare, work). Furthermore, the information provider can provide relevant information based on the user's current health status. By customizing the means of information delivery based on the user's current living situation, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user living situation data into a generating AI to generate the optimal means of information delivery.

[0055] The information provider can select the optimal information delivery method by considering the user's geographical location when providing information. For example, the information provider can use AI to select the optimal information delivery method by considering the user's geographical location when providing information. For example, the information provider can provide event information related to the user's current location. The information provider can also provide information on nearby restaurants and cafes based on the user's geographical location. Furthermore, the information provider can provide information on nearby tourist attractions and activities based on the user's location. In this way, the optimal information delivery method can be selected by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's location data into a generating AI to generate the optimal information delivery method.

[0056] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can use AI to analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can provide relevant information based on the content the user has shared on social media. The information provider can also analyze the activity of the user's followers and friends on social media and provide relevant information. Furthermore, the information provider can provide information based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the optimal method of providing information can be proposed. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media data into a generating AI and generate the optimal method of providing information.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] A lifestyle optimization system can analyze a user's past behavioral history and select the optimal data collection method. For example, it can collect data from apps and services that the user has frequently used in the past. It can also analyze the user's past behavioral patterns to determine the optimal timing for data collection. Furthermore, it can collect data from content that the user has shown interest in in the past. In this way, by analyzing the user's past behavioral history, the system can select the most suitable data collection method.

[0059] The lifestyle optimization system can prioritize the collection of highly relevant data by considering the user's geographical location. For example, it can collect event information related to the user's current location. It can also collect information on nearby restaurants and cafes based on the user's geographical location. Furthermore, it can collect information on nearby tourist attractions and activities based on the user's location. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0060] The lifestyle optimization system can analyze a user's social media activity and collect relevant data during data collection. For example, it can collect relevant data based on the content a user shares on social media. It can also analyze the activity of a user's followers and friends on social media and collect relevant data. Furthermore, it can collect data based on topics a user has shown interest in on social media. In this way, relevant data can be collected by analyzing a user's social media activity.

[0061] The lifestyle optimization system can apply different analysis algorithms depending on the data category during analysis. For example, health data can be analyzed using an analysis algorithm specifically designed for health management. Similarly, entertainment data can be analyzed using an analysis algorithm for entertainment content. Furthermore, hobby activity data can be analyzed using an analysis algorithm specifically designed for hobbies. By applying different analysis algorithms depending on the data category, the system can provide more appropriate analysis results.

[0062] The lifestyle optimization system can prioritize analysis based on when the data was collected. For example, it can prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, it can prioritize the analysis of data collected during a specific period. By prioritizing analysis based on the data collection period, it can provide more appropriate analysis results.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit gathers user interests and behavioral patterns. Specifically, it collects data on users' purchase history, browsing history, location information, and hobbies and activities. For example, it collects information on what sports users like, what books they read, and what travel destinations they are interested in. Step 2: The analysis unit analyzes the data collected by the collection unit. Specifically, it uses AI to analyze the collected data and identify user interests and behavioral patterns. For example, it analyzes the user's purchase history to identify what kinds of products they are interested in. It also analyzes the user's browsing history to identify what kinds of websites they visit. Step 3: The proposal unit proposes a lifestyle plan based on the analysis results obtained by the analysis unit. Specifically, it uses AI to propose the optimal lifestyle plan for the user. For example, it may suggest exercise plans and dietary advice for health management, as well as hobby activities. It may also suggest sports, reading, and travel destinations that the user is interested in. Step 4: The service provider will provide information in real time based on the plan proposed by the proposal provider. Specifically, they will use AI to provide real-time hospital booking and navigation support. For example, when a user is booking a hospital, the service provider will suggest the best hospital and support the booking process. They will also suggest the best route from the user's current location to their destination and provide navigation.

[0065] (Example of form 2) The lifestyle optimization system according to an embodiment of the present invention is a system that proposes an individualized lifestyle plan based on the user's interests and behavioral patterns, streamlines time management, and provides a daily plan that balances elements of health, hobbies, and entertainment. The lifestyle optimization system collects the user's interests and behavioral patterns, and a generating AI analyzes them. For example, it understands what hobbies the user has and what activities they are interested in. Next, the generating AI proposes an optimal lifestyle plan to the user based on the analysis results. This may include exercise plans and dietary advice for health management, suggestions for hobby activities, and recommendations for entertainment content. Furthermore, it integrates data from map services and healthcare services to provide real-time information and support. For example, it utilizes data from map services and healthcare services to provide real-time hospital reservations and navigation support. In addition, users can receive notifications of appointments and tasks, as well as important reminders, through messages. This mechanism allows users to easily access optimal activities and content even in their busy daily lives, improving their quality of life. For example, if a user wants to go somewhere on the weekend, the system utilizes data from map services to suggest activities, tourist spots, and restaurants in their destination. Furthermore, it utilizes community features to provide a platform for connecting with people who share similar hobbies. The lifestyle optimization system aims to enrich, streamline, and enhance users' daily lives, utilizing generative AI and big data to provide optimal plans and information tailored to each user's individual needs. This aims to improve users' quality of life and enable them to achieve a highly satisfying lifestyle. In this way, the lifestyle optimization system can improve users' quality of life and support a fulfilling daily life.

[0066] The lifestyle optimization system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's interests and behavioral patterns. For example, the collection unit can collect the user's purchase history, browsing history, location information, etc. The collection unit can also collect data related to the user's hobbies and activities. For example, the collection unit can collect what sports the user likes, what books they read, and what travel destinations they are interested in. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data and identify the user's interests and behavioral patterns. For example, the analysis unit can analyze the user's purchase history to identify what products the user is interested in. The analysis unit can also analyze the user's browsing history to identify what websites the user visits. The proposal unit proposes a lifestyle plan based on the analysis results obtained by the analysis unit. For example, the proposal unit uses AI to propose an optimal lifestyle plan for the user. For example, the proposal unit proposes an exercise plan and dietary advice for the user's health management. The proposal unit can also suggest the user's hobby activities. For example, the suggestion unit suggests sports, reading material, and travel destinations that the user is interested in. The provision unit provides information in real time based on the plan suggested by the suggestion unit. The provision unit can, for example, use AI to provide real-time hospital reservations and navigation support. For example, when a user makes a hospital reservation, the provision unit suggests the most suitable hospital and supports the reservation process. The provision unit can also provide navigation support to help the user reach their destination. For example, the provision unit suggests the optimal route from the user's current location to their destination and provides navigation. In this way, the lifestyle optimization system according to the embodiment can improve the user's quality of life and support a fulfilling daily life.

[0067] The data collection unit collects user interests and behavioral patterns. For example, it can collect user purchase history, browsing history, and location information. Specifically, it obtains the history of products purchased by users on online shopping sites and analyzes what product categories they are interested in. It also collects the history of websites visited by users to understand what kind of content they are interested in. Regarding location information, it can use the GPS function of smartphones to record the places users have visited and their movement patterns. Furthermore, the data collection unit can also collect data on users' hobbies and activities. For example, it obtains information on events users have attended and tickets they have purchased to collect information on what sports users like, what books they read, and what travel destinations they are interested in. It can also collect data such as the frequency and type of exercise and calories burned from fitness trackers and smartwatches used by users. As a result, the data collection unit can gain a detailed understanding of users' diverse interests and behavioral patterns and provide foundational data to propose optimal lifestyle plans for individual users. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0068] The analytics department analyzes the data collected by the data collection department. For example, the analytics department uses AI to analyze the collected data and identify user interests and behavioral patterns. Specifically, the AI ​​uses machine learning algorithms to analyze users' purchase history and identify what kinds of products they are interested in. For example, it analyzes products that users frequently purchase and their interest in specific product categories. It can also analyze users' browsing history to identify what kinds of websites they visit. This allows the analytics department to understand the topics and themes that users are interested in. Furthermore, the analytics department analyzes users' location information to identify user movement patterns and trends in places they visit. For example, by analyzing places that users frequently visit and places they visit at specific times of day, the analytics department can understand users' lifestyles and behavioral patterns. The analytics department integrates this data to comprehensively analyze user interests and behavioral patterns. This allows the analytics department to obtain detailed insights into users' lifestyles. Furthermore, the analytics department can also use historical data and statistical information to analyze long-term trends and patterns. For example, based on past purchase and browsing history, it can predict changes in user interests and behavior and identify future needs. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0069] The Proposal Department proposes lifestyle plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department uses AI to propose the optimal lifestyle plan for the user. Specifically, the AI ​​proposes exercise plans and dietary advice for the user's health management. For instance, based on the user's exercise data, it suggests appropriate exercise volume and type, and provides specific actions to improve the user's health. It can also analyze the user's dietary history and health data to propose a nutritionally balanced meal plan. Furthermore, the Proposal Department can also suggest the user's hobbies and activities. For example, it suggests sports, books, and travel destinations that the user is interested in. Specifically, based on the user's past activity data, it suggests new sports and events that the user can enjoy, providing ideas to enrich the user's lifestyle. It also suggests books in genres the user is interested in and travel destinations they want to visit, providing specific actions to pique the user's interest. The Proposal Department can customize these suggestions to match the user's lifestyle, providing the optimal plan for each individual user. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of the user implementing the suggested plan and revise the suggestions based on those results. Furthermore, the proposal department can flexibly adjust its proposals in response to changes in the user's lifestyle. This allows the proposal department to consistently provide users with the optimal lifestyle plan, thereby improving their quality of life.

[0070] The service provider provides information in real time based on the plan proposed by the proposal provider. For example, the service provider uses AI to provide real-time hospital reservations and navigation support. Specifically, when a user makes a hospital reservation, it suggests the most suitable hospital and supports the reservation process. For example, it suggests the most suitable hospital and department based on the user's current location and past medical history, and automates the reservation process. The service provider can also provide navigation support to help users reach their destinations. For example, it suggests the best route from the user's current location to their destination and provides navigation. This allows users to reach their destinations efficiently. Furthermore, the service provider can provide information related to the user's lifestyle in real time. For example, it provides information on events and activities that the user is interested in and supports the registration process. It can also provide information on travel destinations that the user wants to visit and supports travel planning. The service provider can customize this information to match the user's lifestyle and provide the most suitable information for each individual user. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, it can provide feedback on the results of actions taken by users based on the provided information and revise the services based on those results. Furthermore, the service provider can flexibly adjust the content of its offerings in accordance with changes in the user's lifestyle. This allows the service provider to always provide users with the most optimal information and improve their quality of life.

[0071] The data collection unit can collect data about the user's hobbies and activities. For example, it can collect information such as what sports the user likes, what books they read, and what travel destinations they are interested in. The data collection unit can also collect, for example, the history of events and activities the user has participated in. Furthermore, the data collection unit can collect information about hobbies and activities that the user has shared on social media. By collecting data about the user's hobbies and activities, it is possible to provide a more personalized lifestyle plan. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media posts into a generating AI and extract information about hobbies and activities.

[0072] The analysis unit can analyze collected data to identify user interests and behavioral patterns. For example, the analysis unit can use AI to analyze collected data and identify user interests and behavioral patterns. For example, the analysis unit can analyze a user's purchase history to identify what kinds of products the user is interested in. The analysis unit can also analyze a user's browsing history to identify what kinds of websites the user visits. Furthermore, the analysis unit can analyze a user's location information to identify what kinds of places the user is interested in. By identifying user interests and behavioral patterns, it is possible to propose more accurate lifestyle plans. 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 user purchase history data into a generating AI to identify interests and behavioral patterns.

[0073] The suggestion unit can propose exercise plans and dietary advice for health management. For example, the suggestion unit can use AI to propose the optimal exercise plan and dietary advice to the user. For example, based on the user's health condition and lifestyle, the suggestion unit can suggest how many times a week to exercise and what kind of diet to eat. The suggestion unit can also propose calorie-restricted or nutritionally balanced meal plans according to the user's goals. In this way, by proposing exercise plans and dietary advice for health management, it can support the user in maintaining their health. 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 the user's health data into a generating AI and generate the optimal exercise plan and dietary advice.

[0074] The suggestion unit can suggest hobby activities. For example, the suggestion unit can use AI to suggest the most suitable hobby activities for the user. For example, the suggestion unit can suggest hobby activities such as sports, reading, and travel based on the user's interests and behavioral patterns. The suggestion unit can also suggest ways for the user to discover new hobbies. For example, the suggestion unit can suggest activities or events that the user has not yet tried. In this way, by suggesting hobby activities, the quality of life of the user can be improved. 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 the user's hobby data into a generating AI and generate the most suitable hobby activities.

[0075] The recommendation unit can recommend entertainment content. For example, the recommendation unit can use AI to recommend the most suitable entertainment content to the user. For example, the recommendation unit can recommend entertainment content such as movies, music, and games based on the user's interests and behavioral patterns. The recommendation unit can also make recommendations to help the user discover new entertainment content. For example, the recommendation unit can recommend movies the user has not yet watched or music the user has not yet listened to. In this way, by recommending entertainment content, the user's entertainment experience can be improved. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's entertainment data into a generation AI and generate the most suitable entertainment content.

[0076] The service provider can offer real-time hospital booking and navigation support. For example, the service provider can use AI to provide real-time hospital booking and navigation support. For example, when a user makes a hospital booking, the service provider can suggest the most suitable hospital and support the booking process. The service provider can also provide navigation support to help the user reach their destination. For example, the service provider can suggest the best route from the user's current location to their destination and provide navigation. This improves user convenience by providing real-time hospital booking and navigation support. 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 location information into a generating AI and generate the best route.

[0077] The service provider can provide notifications of appointments and tasks, as well as important reminders, through messages. For example, the service provider can use AI to provide users with notifications of appointments and tasks, as well as important reminders. For example, the service provider can analyze the user's calendar information and send meeting notifications and deadline reminders. The service provider can also integrate with the user's task management app and notify them of task progress. In this way, by providing notifications of appointments and tasks, as well as important reminders, through messages, the service provider can support the user's time management. Some or all of the above processes 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 calendar data into a generating AI to generate the optimal notification timing.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit estimates the user's emotions using an emotion engine or generative AI. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. 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 a generative AI to generate the optimal data collection timing.

[0079] The data collection unit can analyze a user's past behavior history and select the optimal data collection method. For example, the data collection unit can use AI to analyze a user's past behavior history and select the optimal data collection method. For example, the data collection unit can collect data from apps and services that the user has frequently used in the past. The data collection unit can also analyze a user's past behavior patterns and determine the optimal timing for data collection. Furthermore, the data collection unit can collect data from content that the user has shown interest in in the past. This allows the optimal data collection method to be selected by analyzing a user's past behavior 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 user behavior history data into a generating AI and generate the optimal data collection method.

[0080] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can use AI to filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics that the user is currently interested in. The data collection unit can also filter data according to the user's current lifestyle (e.g., childcare, work). Furthermore, the data collection unit can collect relevant data based on the user's current health status. This allows for the collection of more relevant data by filtering data based on the user's current 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 user lifestyle data into a generating AI to generate an optimal data filtering method.

[0081] 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, the data collection unit estimates the user's emotions using an emotion engine or generative AI. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate data collection by determining the priority of data to collect based on the user's emotions. 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 a generative AI to generate the optimal data collection priority.

[0082] 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, the data collection unit can use AI to prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information related to the user's current location. The data collection unit can also collect information on nearby restaurants and cafes based on the user's geographical location. Furthermore, the data collection unit can collect information on nearby tourist attractions and activities based on the user's location information. In this way, by considering the user's geographical location information, highly relevant data can be prioritized for collection. 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 location information data into a generating AI to generate an optimal data collection method.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can use AI to analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by the user on social media. The data collection unit can also analyze the activity of the user's followers and friends on social media and collect relevant data. Furthermore, the data collection unit can collect data based on topics the user has shown interest in on social media. 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 data into a generating AI to generate an optimal data collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for the provision of more appropriate analysis results by adjusting the method of expressing the analysis based on the user's emotions. 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 user's emotion data into a generative AI and generate the optimal method of expressing the analysis.

[0085] 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 use AI to adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, more 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 importance of the data into a generating AI and generate the optimal level of analysis detail.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can use AI to apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm specialized for health management to health data. It can also apply an analysis algorithm for entertainment content to entertainment data. Furthermore, it can apply an analysis algorithm specialized for hobbies to hobby activity data. By applying different analysis algorithms depending on the data category, more 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 generate the optimal analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and generate an optimal analysis length.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can use AI to determine the priority of analysis based on the data collection period. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the data collection period, more 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 collection period into a generating AI to generate the optimal analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can use AI to adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data into a generating AI to generate the optimal analysis order.

[0090] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using an emotion engine or generative AI. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate suggestions by adjusting the way the suggestion is presented based on the user's emotions. 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 the user's emotion data into a generative AI and generate the optimal way to present the suggestion.

[0091] The proposal unit can adjust the level of detail of a proposal based on the importance of the plan. For example, the proposal unit can use AI to adjust the level of detail based on the importance of the plan. For example, the proposal unit will provide a detailed proposal for plans of high importance. It can also provide a concise proposal for plans of low importance. Furthermore, it can provide a proposal with an appropriate level of detail for plans of medium importance. By adjusting the level of detail of a proposal based on the importance of the plan, it becomes possible to provide more appropriate proposals. 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 importance of the plan into a generating AI and generate the optimal level of detail for the proposal.

[0092] The proposal unit can apply different proposal algorithms depending on the plan category when making a proposal. For example, the proposal unit can use AI to apply different proposal algorithms depending on the plan category. For example, the proposal unit can apply a proposal algorithm specialized for health management to a health management plan. It can also apply an entertainment content proposal algorithm to an entertainment plan. Furthermore, it can apply a proposal algorithm specialized for hobbies to a hobby activity plan. By applying different proposal algorithms depending on the plan category, more appropriate proposals can be made. 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 plan category into a generating AI and generate the optimal proposal algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's emotion data into a generative AI and generate the optimal suggestion length.

[0094] The proposal department can determine the priority of proposals based on the submission date of each plan. For example, the proposal department can use AI to determine the priority of proposals based on the submission date. For example, the proposal department will prioritize the most recent plan. The proposal department can also propose the latest plan while referring to past plans. Furthermore, the proposal department can prioritize plans submitted within a specific period. This allows for more appropriate proposals by determining the priority of proposals based on the submission date. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the plan submission date into a generating AI to generate the optimal proposal priority.

[0095] The proposal unit can adjust the order of proposals based on the relevance of the plans during the proposal process. For example, the proposal unit can use AI to adjust the order of proposals based on the relevance of the plans. For example, the proposal unit can prioritize proposing plans with high relevance. It can also postpone proposing plans with low relevance. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the plans. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the plans. 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 relevance of the plans into a generating AI to generate the optimal proposal order.

[0096] The information provider can estimate the user's emotions and adjust the method of information provision based on the estimated emotions. For example, the information provider can estimate the user's emotions using an emotion engine or generative AI. For example, the information provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The information provider can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the information provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate information provision by adjusting the method of information provision based on the user's emotions. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's emotion data into a generative AI and generate the optimal method of information provision.

[0097] The information delivery unit can select the optimal delivery method by referring to the user's past behavior history when providing information. For example, the information delivery unit can use AI to select the optimal delivery method by referring to the user's past behavior history when providing information. For example, the information delivery unit can prioritize selecting information delivery methods that the user has used in the past. The information delivery unit can also analyze the user's past behavior history and select the optimal information delivery method. Furthermore, the information delivery unit can select information delivery methods that the user has shown interest in in the past. In this way, the optimal information delivery method can be selected by referring to the user's past behavior history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input user behavior history data into a generating AI and generate the optimal information delivery method.

[0098] The information provider can customize the means of information delivery based on the user's current living situation. For example, the information provider can use AI to customize the means of information delivery based on the user's current living situation. For example, the information provider can prioritize providing information related to topics that the user is currently interested in. The information provider can also customize the means of information delivery according to the user's current living situation (e.g., childcare, work). Furthermore, the information provider can provide relevant information based on the user's current health status. By customizing the means of information delivery based on the user's current living situation, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user living situation data into a generating AI to generate the optimal means of information delivery.

[0099] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, the information provider can estimate the user's emotions using an emotion engine or generative AI. For example, the information provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The information provider can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the information provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This enables more appropriate information provision by determining the priority of information provision based on the user's emotions. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's emotion data into a generative AI and generate the optimal priority of information provision.

[0100] The information provider can select the optimal information delivery method by considering the user's geographical location when providing information. For example, the information provider can use AI to select the optimal information delivery method by considering the user's geographical location when providing information. For example, the information provider can provide event information related to the user's current location. The information provider can also provide information on nearby restaurants and cafes based on the user's geographical location. Furthermore, the information provider can provide information on nearby tourist attractions and activities based on the user's location. In this way, the optimal information delivery method can be selected by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's location data into a generating AI to generate the optimal information delivery method.

[0101] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can use AI to analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can provide relevant information based on the content the user has shared on social media. The information provider can also analyze the activity of the user's followers and friends on social media and provide relevant information. Furthermore, the information provider can provide information based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the optimal method of providing information can be proposed. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media data into a generating AI and generate the optimal method of providing information.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] A lifestyle optimization system can estimate a user's emotions and adjust its suggestions based on those emotions. For example, if a user is feeling stressed, it can suggest relaxing activities and methods. If a user is excited, it can suggest sports or activities to release energy. Furthermore, if a user is sad, it can suggest entertainment content or hobbies to lift their spirits. This allows the system to provide an optimal lifestyle plan tailored to the user's emotions.

[0104] A lifestyle optimization system can analyze a user's past behavioral history and select the optimal data collection method. For example, it can collect data from apps and services that the user has frequently used in the past. It can also analyze the user's past behavioral patterns to determine the optimal timing for data collection. Furthermore, it can collect data from content that the user has shown interest in in the past. In this way, by analyzing the user's past behavioral history, the system can select the most suitable data collection method.

[0105] A lifestyle optimization system can estimate a user's emotions and adjust the timing of data collection based on those estimates. For example, collecting data when the user is relaxed can yield more accurate results. Conversely, refraining from data collection when the user is stressed can reduce the user's burden. Furthermore, data collection can be conducted when the user is excited to capture changes in their emotions. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions.

[0106] The lifestyle optimization system can prioritize the collection of highly relevant data by considering the user's geographical location. For example, it can collect event information related to the user's current location. It can also collect information on nearby restaurants and cafes based on the user's geographical location. Furthermore, it can collect information on nearby tourist attractions and activities based on the user's location. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0107] A lifestyle optimization system can estimate a user's emotions and prioritize the data to collect based on those emotions. For example, if a user is stressed, it can prioritize collecting data related to stress reduction. Similarly, if a user is relaxed, it can prioritize collecting data related to relaxation. Furthermore, if a user is excited, it can prioritize collecting data related to energy release. This allows for more appropriate data collection by prioritizing data collection based on the user's emotions.

[0108] The lifestyle optimization system can analyze a user's social media activity and collect relevant data during data collection. For example, it can collect relevant data based on the content a user shares on social media. It can also analyze the activity of a user's followers and friends on social media and collect relevant data. Furthermore, it can collect data based on topics a user has shown interest in on social media. In this way, relevant data can be collected by analyzing a user's social media activity.

[0109] The lifestyle optimization system can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, the analysis results will be presented in a calm tone. If the user is stressed, the results can be presented concisely and clearly. Furthermore, if the user is excited, the results can be presented in an energetic tone. By adjusting the presentation of the analysis based on the user's emotions, the system can provide more appropriate analysis results.

[0110] The lifestyle optimization system can apply different analysis algorithms depending on the data category during analysis. For example, health data can be analyzed using an analysis algorithm specifically designed for health management. Similarly, entertainment data can be analyzed using an analysis algorithm for entertainment content. Furthermore, hobby activity data can be analyzed using an analysis algorithm specifically designed for hobbies. By applying different analysis algorithms depending on the data category, the system can provide more appropriate analysis results.

[0111] The lifestyle optimization system can prioritize analysis based on when the data was collected. For example, it can prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, it can prioritize the analysis of data collected during a specific period. By prioritizing analysis based on the data collection period, it can provide more appropriate analysis results.

[0112] The lifestyle optimization system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, suggestions will be presented in a calm tone. If the user is stressed, suggestions can be presented concisely and clearly. Furthermore, if the user is excited, suggestions can be presented in an energetic tone. By adjusting the presentation of suggestions based on the user's emotions, more appropriate suggestions can be made.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit gathers user interests and behavioral patterns. Specifically, it collects data on users' purchase history, browsing history, location information, and hobbies and activities. For example, it collects information on what sports users like, what books they read, and what travel destinations they are interested in. Step 2: The analysis unit analyzes the data collected by the collection unit. Specifically, it uses AI to analyze the collected data and identify user interests and behavioral patterns. For example, it analyzes the user's purchase history to identify what kinds of products they are interested in. It also analyzes the user's browsing history to identify what kinds of websites they visit. Step 3: The proposal unit proposes a lifestyle plan based on the analysis results obtained by the analysis unit. Specifically, it uses AI to propose the optimal lifestyle plan for the user. For example, it may suggest exercise plans and dietary advice for health management, as well as hobby activities. It may also suggest sports, reading, and travel destinations that the user is interested in. Step 4: The service provider will provide information in real time based on the plan proposed by the proposal provider. Specifically, they will use AI to provide real-time hospital booking and navigation support. For example, when a user is booking a hospital, the service provider will suggest the best hospital and support the booking process. They will also suggest the best route from the user's current location to their destination and provide navigation.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's behavior patterns using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the user's interests and behavior patterns. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes an optimal lifestyle plan based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides real-time information and support. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

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

[0122] The 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.

[0123] 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.

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

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

[0126] Figure 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.

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

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

[0129] In the 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.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's behavior patterns using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's interests and behavior patterns. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal lifestyle plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides real-time information and support. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

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

[0138] The 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.

[0139] 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.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's behavior patterns using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's interests and behavior patterns. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal lifestyle plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides real-time information and support. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's behavior patterns using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's interests and behavior patterns. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal lifestyle plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides real-time information and support. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects user interests and behavioral patterns, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a lifestyle plan, The system comprises a provisioning unit that provides information in real time based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data about users' hobbies and activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, we identify users' interests and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We offer exercise plans and dietary advice for health management. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Propose hobby activities The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Recommend entertainment content The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing real-time hospital booking and navigation support. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, It provides notifications for appointments and tasks, as well as important reminders, through messaging. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) 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 13) The aforementioned collection unit is During data collection, 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 14) 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 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, 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 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, 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 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, 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 21) 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 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the plan category. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the plans were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the system selects the optimal method of delivery by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the method of providing information will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, we analyze users' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 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 interests and behavioral patterns, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a lifestyle plan, The system comprises a provisioning unit that provides information in real time based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data about users' hobbies and activities. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data, we identify users' interests and behavioral patterns. The system according to feature 1.

4. The aforementioned proposal section is, We offer exercise plans and dietary advice for health management. The system according to feature 1.

5. The aforementioned proposal section is, Propose hobby activities The system according to feature 1.

6. The aforementioned proposal section is, Recommend entertainment content The system according to feature 1.

7. The aforementioned supply unit is, Providing real-time hospital booking and navigation support. The system according to feature 1.

8. The aforementioned supply unit is, It provides notifications for appointments and tasks, as well as important reminders, through messaging. The system according to feature 1.

9. 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.

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

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