system

Abstract

The system according to this embodiment aims to suggest activities and events based on the user's interests and lifestyle. [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 user log data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes activities and events based on the analysis results obtained by the analysis unit. The provision unit provides the activities and events 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, the method including: 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, activities and events based on a user's interests and lifestyle are not sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose activities and events based on a user's interests and lifestyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects user log data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes activities and events based on the analysis results obtained by the analysis unit. The provision unit provides the activities and events proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest activities and events based on the user's interests and lifestyle. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 AI ​​agent system according to an embodiment of the present invention is a system for making users' private time more efficient and fulfilling. This AI agent system collects data such as the user's message logs, search logs, purchase logs, and email logs, and the AI ​​analyzes this data. Based on the analysis results, it proposes activities and events that are best suited to the user's interests and lifestyle. This allows users to make their private time more efficient and fulfilling. The AI ​​agent system also supports the user's time management and contributes to promoting health and well-being. For example, if a user is feeling stressed, the AI ​​agent system proposes relaxing activities or health-beneficial events. This allows users to improve their work-life balance and make their private time more fulfilling. This system is particularly targeted at busy modern people, especially working people in their 20s to 40s. They face challenges such as a feeling of wasted private time, increased weekday stress, and a lack of work-life balance. To solve these problems, the AI ​​agent system provides personalized activity suggestions and time management support. The AI ​​agent system makes full use of the user's log data to generate and propose appropriate activities and events. This allows the AI ​​agent system to make users' private time more efficient and fulfilling.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects user log data. The collection unit collects data such as user message logs, search logs, purchase logs, and email logs. By collecting this data, the collection unit provides basic data for understanding the user's interests and lifestyle. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using techniques such as data mining, statistical analysis, and machine learning. By using these techniques, the analysis unit identifies the user's interests and lifestyle. The suggestion unit suggests activities and events based on the analysis results obtained by the analysis unit. The suggestion unit suggests activities and events based on the user's interests and past behavior, for example. The suggestion unit can thereby enrich the user's private time. The provision unit provides the activities and events suggested by the suggestion unit to the user. The provision unit provides activities and events by methods such as notifications, emails, and in-app displays. The provision unit makes it easy for the user to access the suggested activities and events. As a result, the AI ​​agent system according to this embodiment can collect and analyze user log data, and propose and provide activities and events, thereby making private time more efficient and fulfilling.

[0030] The data collection unit collects user log data. Specifically, it collects data from various services that users use on a daily basis. For example, this includes message logs from messaging apps used by the user, search history on search engines, purchase history on online shopping sites, and email sending and receiving history. This data reflects the user's behavior and interests and is an important source of information for understanding the user's lifestyle and preferences. The data collection unit automatically collects this data and stores it in a secure database. Data collection is carried out with appropriate security measures in place to protect user privacy and with the user's consent. The collected data is updated in real time, and the latest information is always maintained. This allows the data collection unit to provide data that reflects the user's latest behavior and interests. Furthermore, the data collection unit has functions to check the integrity and consistency of the data and eliminate inaccurate and duplicate data in order to maintain data quality. This allows the data collection unit to provide high-quality data to the analysis unit.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit utilizes techniques such as data mining, statistical analysis, and machine learning to identify user behavior patterns and interests. For example, using data mining techniques, it extracts frequently searched keywords and purchased products from users' search and purchase history to identify user interests. It also uses statistical analysis to analyze user behavior data and reveal behavioral patterns at specific times of day or on specific days of the week. Furthermore, it uses machine learning algorithms to learn from users' past behavior data and predict future behavior. For example, it predicts which events users are most likely to participate in next based on data from past events and activities. The analysis unit combines these techniques to comprehensively understand user interests and lifestyles, providing foundational data for making optimal suggestions to individual users. The analysis results are sent to the suggestion unit, which forms the basis for specific suggestions to users.

[0032] The Proposal Department suggests activities and events based on the analysis results obtained by the Analysis Department. The Proposal Department selects activities and events that are appealing to users, taking into account their interests and past behavioral data. For example, based on data from music events a user has previously attended, it might suggest new music events of the same genre. It can also suggest workshops and seminars related to hobbies and interests that users frequently search for. The Proposal Department makes suggestions at the optimal time, tailored to the user's lifestyle and schedule. For example, it might suggest weekend events to users who want to relax on weekends, or suggest activities available on weekday evenings to users who have free time on weekday evenings. The Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. It analyzes how users reacted to suggested activities and events and incorporates this into future suggestions. This allows the Proposal Department to make suggestions that better suit user needs and enrich users' private time.

[0033] The service provider delivers activities and events proposed by the suggestion provider to users. The service provider provides information in various ways to ensure users can easily access the proposed activities and events. For example, it uses smartphone notification functions to notify users of activity and event information in real time. It also provides detailed information via email, allowing users to register by simply clicking a link if they are interested. Furthermore, it uses in-app display to prominently display information about proposed activities and events when users open the app. The service provider flexibly selects the method of information delivery, taking user convenience into consideration. For example, it learns the notification methods and timing preferred by users and delivers information in the most optimal way. The service provider collects user feedback and continuously improves the methods and timing of information delivery. This allows the service provider to make it easy for users to access proposed activities and events, enabling users to make their private time more efficient and fulfilling.

[0034] The data collection unit can collect user message logs, search logs, purchase logs, and email logs. For example, the data collection unit collects user message logs. Message logs include message content, sending time, and sender information. By collecting this data, the data collection unit can understand the user's communication patterns. For example, the data collection unit collects user search logs. Search logs include search keywords, search date and time, and click information on search results. By collecting this data, the data collection unit can understand the user's interests and preferences. For example, the data collection unit collects user purchase logs. Purchase logs include purchased items, purchase date and time, and purchase amount. By collecting this data, the data collection unit can understand the user's purchasing behavior. For example, the data collection unit collects user email logs. Email logs include the date and time of sending and receiving emails, sender and recipient information, and email content. By collecting this data, the data collection unit can understand the user's communication patterns and interests. As a result, the data collection unit can perform more accurate analysis by collecting diverse user log data.

[0035] The analysis unit can analyze collected data to identify user interests and lifestyles. For example, the analysis unit can analyze collected data using data mining techniques. Data mining techniques are used to extract useful information from large amounts of data and are used to identify user interests and lifestyles. The analysis unit can also analyze collected data using statistical analysis techniques. Statistical analysis techniques are used to analyze data distribution and trends and are used to identify user interests and lifestyles. Furthermore, the analysis unit can analyze collected data using machine learning techniques. Machine learning techniques are used to learn patterns from data and perform predictions and classifications, and are used to identify user interests and lifestyles. This allows the analysis unit to analyze collected data and identify user interests and lifestyles, enabling more personalized suggestions.

[0036] The suggestion unit can propose at least one activity and / or event based on the analysis results. For example, the suggestion unit can propose activities based on the user's interests and past behavior. For example, the suggestion unit can propose events based on the user's interests and past behavior. This allows the suggestion unit to enrich the user's private time. For example, the suggestion unit can propose activities such as sports, hobbies, and events. For example, the suggestion unit can propose events such as concerts, exhibitions, and seminars. By proposing activities and events based on the analysis results, the suggestion unit can enrich the user's private time.

[0037] The service provider can provide users with suggested activities and events. For example, the service provider can provide activities and events using notifications. For example, the service provider can provide activities and events using email. For example, the service provider can provide activities and events using in-app displays. This allows users to easily access suggested activities and events. For example, the service provider can provide detailed information about activities and events. For example, the service provider can provide instructions on how to participate in activities and events. For example, the service provider can provide schedules for activities and events. This allows the service provider to efficiently utilize users' private time by providing them with suggested activities and events.

[0038] The schedule analysis unit can analyze the user's schedule. For example, the schedule analysis unit analyzes the user's calendar events. For example, the schedule analysis unit analyzes the priority of the user's appointments. For example, the schedule analysis unit analyzes the user's time zones. By doing so, the schedule analysis unit can understand the user's schedule and suggest more appropriate activities and events. For example, the schedule analysis unit adjusts the timing of activity and event suggestions based on the user's schedule. For example, the schedule analysis unit determines the priority of activities and events based on the user's schedule. For example, the schedule analysis unit customizes the content of activity and event suggestions based on the user's schedule. In this way, the schedule analysis unit can suggest more appropriate activities and events by analyzing the user's schedule.

[0039] The health analysis unit can analyze the user's health status. For example, the health analysis unit can analyze the user's heart rate. For example, the health analysis unit can analyze the user's sleep patterns. For example, the health analysis unit can analyze the user's exercise level. By doing so, the health analysis unit can understand the user's health status and suggest health-conscious activities and events. For example, the health analysis unit can customize the suggested activities and events based on the user's health status. For example, the health analysis unit can adjust the timing of suggested activities and events based on the user's health status. For example, the health analysis unit can determine the priority of activities and events based on the user's health status. In this way, the health analysis unit can suggest health-conscious activities and events by analyzing the user's health status.

[0040] The stress analysis unit can analyze the user's stress state. For example, the stress analysis unit can analyze the user's stress level. For example, the stress analysis unit can analyze the causes of the user's stress. For example, the stress analysis unit can analyze the effects of the user's stress. By doing so, the stress analysis unit can understand the user's stress state and suggest activities and events that can help reduce stress. For example, the stress analysis unit can customize the suggested activities and events based on the user's stress state. For example, the stress analysis unit can adjust the timing of suggested activities and events based on the user's stress state. For example, the stress analysis unit can determine the priority of activities and events based on the user's stress state. In this way, by analyzing the user's stress state, the stress analysis unit can suggest activities and events that can help reduce stress.

[0041] The data collection unit can analyze the user's past log data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting log data from applications that the user has frequently used in the past. For example, if the user has generated a large amount of data during a specific time period in the past, the data collection unit will concentrate collection during that time period. For example, the data collection unit can analyze the user's past behavior patterns and select the most efficient collection method. In this way, the data collection unit can select the optimal collection method by analyzing the user's past log data collection history.

[0042] The data collection unit can filter log data based on the user's current activity and areas of interest. For example, the data collection unit can prioritize collecting log data related to the user's current activities. For example, the data collection unit can filter and collect data related to the user's current areas of interest. For example, the data collection unit can exclude unnecessary data based on the user's current activity. In this way, the data collection unit can collect more relevant data by filtering based on the user's current activity and areas of interest.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting log data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data around the user's home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location.

[0044] The data collection unit can analyze a user's social media activity and collect relevant data when collecting log data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity history and collect data related to their interests. For example, the data collection unit can collect relevant data based on information about accounts that the user follows. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the log data during the analysis. For example, the analysis unit performs a detailed analysis on high-importance data. For example, the analysis unit performs a simplified analysis on low-importance data. For example, the analysis unit performs an analysis with an appropriate level of detail on data of moderate importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the log data.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the log data during analysis. For example, the analysis unit applies a text analysis algorithm to message logs. For example, the analysis unit applies a search behavior analysis algorithm to search logs. For example, the analysis unit applies a purchase behavior analysis algorithm to purchase logs. By applying different analysis algorithms depending on the category of the log data, the analysis unit can perform more accurate analysis.

[0047] The analysis unit can determine the priority of analysis based on when the log data was collected. For example, the analysis unit may prioritize the analysis of the most recent log data. For example, the analysis unit may prioritize the most recent data while referring to past log data. For example, the analysis unit may prioritize the analysis of data collected in a concentrated period. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on when the log data was collected.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the log data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant data, postpone analyzing less relevant data, or analyze moderately relevant data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the log data.

[0049] The proposal team can adjust the level of detail in their proposals based on the importance of the activities and events. For example, they can provide detailed proposals for high-importance activities, simplified proposals for low-importance activities, and proposals with a moderate level of detail for moderately important activities. This allows the proposal team to make efficient proposals by adjusting the level of detail based on the importance of the activities and events.

[0050] The proposal function can apply different proposal algorithms depending on the category of activity or event. For example, for relaxation-related activities, it will apply a proposal algorithm that emphasizes relaxation effects. For sports-related activities, it will apply a proposal algorithm that emphasizes exercise effects. For cultural activities, it will apply a proposal algorithm that emphasizes cultural value. By applying different proposal algorithms depending on the category of activity or event, the proposal function can make more accurate proposals.

[0051] The proposal team can prioritize proposals based on the timing of activities and events. For example, they might prioritize activities happening soon. They might postpone activities happening far in the future. They might prioritize activities that are concentrated within a specific period. This allows the proposal team to make efficient proposals by prioritizing proposals based on the timing of activities and events.

[0052] The proposal team can adjust the order of proposals based on the relevance of activities and events. For example, the proposal team can prioritize highly relevant activities, postpone less relevant activities, or moderately propose activities of moderate relevance. This allows the proposal team to make efficient proposals by adjusting the order of proposals based on the relevance of activities and events.

[0053] The service provider can select the optimal delivery method by referring to the user's past activity participation history at the time of delivery. For example, the service provider can provide relevant activities based on the activities the user has participated in in the past. For example, the service provider can analyze the user's past participation history and provide the activities that are most likely to interest the user. For example, the service provider can prioritize providing activities that the user has participated in frequently based on their past participation history. In this way, the service provider can select the optimal delivery method by referring to the user's past activity participation history.

[0054] The service provider can customize the means of delivery based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can provide activities that can be participated in for a short time. For example, if the user is relaxed, the service provider can provide activities that can be enjoyed for a long time. For example, if the user is health-conscious, the service provider can provide activities that are good for health. In this way, the service provider can provide more appropriate services by customizing the means of delivery based on the user's current lifestyle.

[0055] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider will provide activities related to that region. For example, if the user is traveling, the service provider will provide activities related to the travel destination. For example, if the user is at home, the service provider will provide activities around the user's home. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information.

[0056] The service provider can analyze the user's social media activity and propose a suitable delivery method at the time of delivery. For example, the service provider can provide relevant activities based on information shared by the user on social media. For example, the service provider can analyze the user's social media activity history and provide activities related to their interests. For example, the service provider can provide relevant activities based on information about accounts the user follows. In this way, the service provider can propose the most suitable delivery method by analyzing the user's social media activity.

[0057] The schedule analysis unit can select the optimal analysis method by referring to the user's past schedule history during schedule analysis. For example, the schedule analysis unit can suggest an optimal schedule based on activities the user has frequently performed in the past. For example, the schedule analysis unit can suggest an efficient schedule by analyzing the user's past schedule history. For example, the schedule analysis unit can suggest a schedule that helps reduce stress based on the user's past schedule history. In this way, the schedule analysis unit can select the optimal analysis method by referring to the user's past schedule history.

[0058] The schedule analysis unit can select the optimal analysis method by considering the user's device information during schedule analysis. For example, if the user is using a smartphone, the schedule analysis unit will propose a schedule optimized for the smartphone. For example, if the user is using a tablet, the schedule analysis unit will propose a schedule optimized for the tablet. For example, if the user is using a smartwatch, the schedule analysis unit will propose a schedule optimized for the smartwatch. In this way, the schedule analysis unit can select the optimal analysis method by considering the user's device information.

[0059] The health analysis unit can select the optimal analysis method by referring to the user's past health data during health analysis. For example, the health analysis unit performs optimal health analysis based on health data previously recorded by the user. For example, the health analysis unit analyzes the user's past health data to perform efficient health analysis. For example, the health analysis unit performs health analysis that helps reduce stress based on the user's past health data. In this way, the health analysis unit can select the optimal analysis method by referring to the user's past health data.

[0060] The health analysis unit can select the optimal analysis method by considering the user's lifestyle information during health analysis. For example, the health analysis unit can perform optimal health analysis based on the user's dietary information. For example, the health analysis unit can perform optimal health analysis based on the user's exercise habits information. For example, the health analysis unit can perform optimal health analysis based on the user's sleep habits information. In this way, the health analysis unit can select the optimal analysis method by considering the user's lifestyle information.

[0061] The stress analysis unit can select the optimal analysis method by referring to the user's past stress data during stress analysis. For example, the stress analysis unit performs the optimal stress analysis based on the stress data previously recorded by the user. For example, the stress analysis unit performs an efficient stress analysis by analyzing the user's past stress data. For example, the stress analysis unit performs a stress analysis that helps reduce stress based on the user's past stress data. In this way, the stress analysis unit can select the optimal analysis method by referring to the user's past stress data.

[0062] The stress analysis unit can select the optimal analysis method by considering the user's living environment information during stress analysis. For example, the stress analysis unit can perform the optimal stress analysis based on the user's work environment information. For example, the stress analysis unit can perform the optimal stress analysis based on the user's home environment information. For example, the stress analysis unit can perform the optimal stress analysis based on the user's social environment information. In this way, the stress analysis unit can select the optimal analysis method by considering the user's living environment information.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of data related to that region. If the user is traveling, it can prioritize the collection of data related to their travel destination. Furthermore, if the user is at home, it can prioritize the collection of data around their home. This enables optimal data collection that takes the user's geographical location into consideration.

[0065] The analysis unit can apply different analysis algorithms depending on the category of log data. For example, a text analysis algorithm can be applied to message logs, a search behavior analysis algorithm to search logs, and a purchase behavior analysis algorithm to purchase logs. This enables optimal analysis according to the category of log data.

[0066] The suggestion function can apply different suggestion algorithms depending on the category of activity or event. For example, for relaxation activities, a suggestion algorithm that emphasizes relaxation effects can be applied. For sports activities, a suggestion algorithm that emphasizes exercise effects can be applied. Furthermore, for cultural activities, a suggestion algorithm that emphasizes cultural value can be applied. This enables the application of optimal suggestions tailored to the category of activity or event.

[0067] The service provider can select the optimal delivery method by referring to the user's past activity participation history. For example, it can provide relevant activities based on the activities the user has participated in in the past. It can analyze the user's past participation history and provide the activities that are most likely to interest them. Furthermore, it can prioritize providing activities that the user has participated in frequently based on their past participation history. This enables optimal delivery by referring to the user's past activity participation history.

[0068] The schedule analysis unit can select the optimal analysis method considering the user's device information. For example, if the user is using a smartphone, it can suggest a schedule optimized for smartphones. If the user is using a tablet, it can suggest a schedule optimized for tablets. Furthermore, if the user is using a smartwatch, it can suggest a schedule optimized for smartwatches. This enables optimal schedule analysis that takes the user's device information into account.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The collection unit collects user log data. For example, it collects data such as user message logs, search logs, purchase logs, and email logs. This provides basic data to understand the user's interests and lifestyle. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using techniques such as data mining, statistical analysis, and machine learning to identify users' interests and lifestyles. Step 3: The proposal unit proposes activities and events based on the analysis results obtained by the analysis unit. For example, it proposes activities and events based on the user's interests and past behavior to enrich the user's private time. Step 4: The providing team delivers the activities and events proposed by the suggesting team to the user. For example, they provide the activities and events through methods such as notifications, emails, and in-app displays, making it easy for the user to access the proposed activities and events.

[0071] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for making users' private time more efficient and fulfilling. This AI agent system collects data such as the user's message logs, search logs, purchase logs, and email logs, and the AI ​​analyzes this data. Based on the analysis results, it proposes activities and events that are best suited to the user's interests and lifestyle. This allows users to make their private time more efficient and fulfilling. The AI ​​agent system also supports the user's time management and contributes to promoting health and well-being. For example, if a user is feeling stressed, the AI ​​agent system proposes relaxing activities or health-beneficial events. This allows users to improve their work-life balance and make their private time more fulfilling. This system is particularly targeted at busy modern people, especially working people in their 20s to 40s. They face challenges such as a feeling of wasted private time, increased weekday stress, and a lack of work-life balance. To solve these problems, the AI ​​agent system provides personalized activity suggestions and time management support. The AI ​​agent system makes full use of the user's log data to generate and propose appropriate activities and events. This allows the AI ​​agent system to make users' private time more efficient and fulfilling.

[0072] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects user log data. The collection unit collects data such as user message logs, search logs, purchase logs, and email logs. By collecting this data, the collection unit provides basic data for understanding the user's interests and lifestyle. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using techniques such as data mining, statistical analysis, and machine learning. By using these techniques, the analysis unit identifies the user's interests and lifestyle. The suggestion unit suggests activities and events based on the analysis results obtained by the analysis unit. The suggestion unit suggests activities and events based on the user's interests and past behavior, for example. The suggestion unit can thereby enrich the user's private time. The provision unit provides the activities and events suggested by the suggestion unit to the user. The provision unit provides activities and events by methods such as notifications, emails, and in-app displays. The provision unit makes it easy for the user to access the suggested activities and events. As a result, the AI ​​agent system according to this embodiment can collect and analyze user log data, and propose and provide activities and events, thereby making private time more efficient and fulfilling.

[0073] The data collection unit collects user log data. Specifically, it collects data from various services that users use on a daily basis. For example, this includes message logs from messaging apps used by the user, search history on search engines, purchase history on online shopping sites, and email sending and receiving history. This data reflects the user's behavior and interests and is an important source of information for understanding the user's lifestyle and preferences. The data collection unit automatically collects this data and stores it in a secure database. Data collection is carried out with appropriate security measures in place to protect user privacy and with the user's consent. The collected data is updated in real time, and the latest information is always maintained. This allows the data collection unit to provide data that reflects the user's latest behavior and interests. Furthermore, the data collection unit has functions to check the integrity and consistency of the data and eliminate inaccurate and duplicate data in order to maintain data quality. This allows the data collection unit to provide high-quality data to the analysis unit.

[0074] The analysis unit analyzes the data collected by the data collection unit. The analysis unit utilizes techniques such as data mining, statistical analysis, and machine learning to identify user behavior patterns and interests. For example, using data mining techniques, it extracts frequently searched keywords and purchased products from users' search and purchase history to identify user interests. It also uses statistical analysis to analyze user behavior data and reveal behavioral patterns at specific times of day or on specific days of the week. Furthermore, it uses machine learning algorithms to learn from users' past behavior data and predict future behavior. For example, it predicts which events users are most likely to participate in next based on data from past events and activities. The analysis unit combines these techniques to comprehensively understand user interests and lifestyles, providing foundational data for making optimal suggestions to individual users. The analysis results are sent to the suggestion unit, which forms the basis for specific suggestions to users.

[0075] The Proposal Department suggests activities and events based on the analysis results obtained by the Analysis Department. The Proposal Department selects activities and events that are appealing to users, taking into account their interests and past behavioral data. For example, based on data from music events a user has previously attended, it might suggest new music events of the same genre. It can also suggest workshops and seminars related to hobbies and interests that users frequently search for. The Proposal Department makes suggestions at the optimal time, tailored to the user's lifestyle and schedule. For example, it might suggest weekend events to users who want to relax on weekends, or suggest activities available on weekday evenings to users who have free time on weekday evenings. The Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. It analyzes how users reacted to suggested activities and events and incorporates this into future suggestions. This allows the Proposal Department to make suggestions that better suit user needs and enrich users' private time.

[0076] The service provider delivers activities and events proposed by the suggestion provider to users. The service provider provides information in various ways to ensure users can easily access the proposed activities and events. For example, it uses smartphone notification functions to notify users of activity and event information in real time. It also provides detailed information via email, allowing users to register by simply clicking a link if they are interested. Furthermore, it uses in-app display to prominently display information about proposed activities and events when users open the app. The service provider flexibly selects the method of information delivery, taking user convenience into consideration. For example, it learns the notification methods and timing preferred by users and delivers information in the most optimal way. The service provider collects user feedback and continuously improves the methods and timing of information delivery. This allows the service provider to make it easy for users to access proposed activities and events, enabling users to make their private time more efficient and fulfilling.

[0077] The data collection unit can collect user message logs, search logs, purchase logs, and email logs. For example, the data collection unit collects user message logs. Message logs include message content, sending time, and sender information. By collecting this data, the data collection unit can understand the user's communication patterns. For example, the data collection unit collects user search logs. Search logs include search keywords, search date and time, and click information on search results. By collecting this data, the data collection unit can understand the user's interests and preferences. For example, the data collection unit collects user purchase logs. Purchase logs include purchased items, purchase date and time, and purchase amount. By collecting this data, the data collection unit can understand the user's purchasing behavior. For example, the data collection unit collects user email logs. Email logs include the date and time of sending and receiving emails, sender and recipient information, and email content. By collecting this data, the data collection unit can understand the user's communication patterns and interests. As a result, the data collection unit can perform more accurate analysis by collecting diverse user log data.

[0078] The analysis unit can analyze collected data to identify user interests and lifestyles. For example, the analysis unit can analyze collected data using data mining techniques. Data mining techniques are used to extract useful information from large amounts of data and are used to identify user interests and lifestyles. The analysis unit can also analyze collected data using statistical analysis techniques. Statistical analysis techniques are used to analyze data distribution and trends and are used to identify user interests and lifestyles. Furthermore, the analysis unit can analyze collected data using machine learning techniques. Machine learning techniques are used to learn patterns from data and perform predictions and classifications, and are used to identify user interests and lifestyles. This allows the analysis unit to analyze collected data and identify user interests and lifestyles, enabling more personalized suggestions.

[0079] The suggestion unit can propose at least one activity and / or event based on the analysis results. For example, the suggestion unit can propose activities based on the user's interests and past behavior. For example, the suggestion unit can propose events based on the user's interests and past behavior. This allows the suggestion unit to enrich the user's private time. For example, the suggestion unit can propose activities such as sports, hobbies, and events. For example, the suggestion unit can propose events such as concerts, exhibitions, and seminars. By proposing activities and events based on the analysis results, the suggestion unit can enrich the user's private time.

[0080] The service provider can provide users with suggested activities and events. For example, the service provider can provide activities and events using notifications. For example, the service provider can provide activities and events using email. For example, the service provider can provide activities and events using in-app displays. This allows users to easily access suggested activities and events. For example, the service provider can provide detailed information about activities and events. For example, the service provider can provide instructions on how to participate in activities and events. For example, the service provider can provide schedules for activities and events. This allows the service provider to efficiently utilize users' private time by providing them with suggested activities and events.

[0081] The schedule analysis unit can analyze the user's schedule. For example, the schedule analysis unit analyzes the user's calendar events. For example, the schedule analysis unit analyzes the priority of the user's appointments. For example, the schedule analysis unit analyzes the user's time zones. By doing so, the schedule analysis unit can understand the user's schedule and suggest more appropriate activities and events. For example, the schedule analysis unit adjusts the timing of activity and event suggestions based on the user's schedule. For example, the schedule analysis unit determines the priority of activities and events based on the user's schedule. For example, the schedule analysis unit customizes the content of activity and event suggestions based on the user's schedule. In this way, the schedule analysis unit can suggest more appropriate activities and events by analyzing the user's schedule.

[0082] The health analysis unit can analyze the user's health status. For example, the health analysis unit can analyze the user's heart rate. For example, the health analysis unit can analyze the user's sleep patterns. For example, the health analysis unit can analyze the user's exercise level. By doing so, the health analysis unit can understand the user's health status and suggest health-conscious activities and events. For example, the health analysis unit can customize the suggested activities and events based on the user's health status. For example, the health analysis unit can adjust the timing of suggested activities and events based on the user's health status. For example, the health analysis unit can determine the priority of activities and events based on the user's health status. In this way, the health analysis unit can suggest health-conscious activities and events by analyzing the user's health status.

[0083] The stress analysis unit can analyze the user's stress state. For example, the stress analysis unit can analyze the user's stress level. For example, the stress analysis unit can analyze the causes of the user's stress. For example, the stress analysis unit can analyze the effects of the user's stress. By doing so, the stress analysis unit can understand the user's stress state and suggest activities and events that can help reduce stress. For example, the stress analysis unit can customize the suggested activities and events based on the user's stress state. For example, the stress analysis unit can adjust the timing of suggested activities and events based on the user's stress state. For example, the stress analysis unit can determine the priority of activities and events based on the user's stress state. In this way, by analyzing the user's stress state, the stress analysis unit can suggest activities and events that can help reduce stress.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of log data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect log data at a time when the stress is reduced. For example, if the user is relaxed, the data collection unit will collect log data while the user is relaxed. For example, if the user is busy, the data collection unit will collect log data when the user's busyness subsides. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of log data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The data collection unit can analyze the user's past log data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting log data from applications that the user has frequently used in the past. For example, if the user has generated a large amount of data during a specific time period in the past, the data collection unit will concentrate collection during that time period. For example, the data collection unit can analyze the user's past behavior patterns and select the most efficient collection method. In this way, the data collection unit can select the optimal collection method by analyzing the user's past log data collection history.

[0086] The data collection unit can filter log data based on the user's current activity and areas of interest. For example, the data collection unit can prioritize collecting log data related to the user's current activities. For example, the data collection unit can filter and collect data related to the user's current areas of interest. For example, the data collection unit can exclude unnecessary data based on the user's current activity. In this way, the data collection unit can collect more relevant data by filtering based on the user's current activity and areas of interest.

[0087] The data collection unit can estimate the user's emotions and determine the priority of log data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. For example, if the user is relaxed, the data collection unit will prioritize collecting data related to relaxation. For example, if the user is excited, the data collection unit will prioritize collecting data that helps maintain excitement. In this way, the data collection unit can prioritize collecting more important data by determining the priority of log data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting log data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data around the user's home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location.

[0089] The data collection unit can analyze a user's social media activity and collect relevant data when collecting log data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity history and collect data related to their interests. For example, the data collection unit can collect relevant data based on information about accounts that the user follows. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the log data during the analysis. For example, the analysis unit performs a detailed analysis on high-importance data. For example, the analysis unit performs a simplified analysis on low-importance data. For example, the analysis unit performs an analysis with an appropriate level of detail on data of moderate importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the log data.

[0092] The analysis unit can apply different analysis algorithms depending on the category of the log data during analysis. For example, the analysis unit applies a text analysis algorithm to message logs. For example, the analysis unit applies a search behavior analysis algorithm to search logs. For example, the analysis unit applies a purchase behavior analysis algorithm to purchase logs. By applying different analysis algorithms depending on the category of the log data, the analysis unit can perform more accurate analysis.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. For example, if the user is relaxed, the analysis unit will provide a detailed analysis. For example, if the user is excited, the analysis unit will provide a visually stimulating analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analysis unit can determine the priority of analysis based on when the log data was collected. For example, the analysis unit may prioritize the analysis of the most recent log data. For example, the analysis unit may prioritize the most recent data while referring to past log data. For example, the analysis unit may prioritize the analysis of data collected in a concentrated period. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on when the log data was collected.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the log data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant data, postpone analyzing less relevant data, or analyze moderately relevant data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the log data.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit might simply suggest relaxing activities. If the user is relaxed, for example, the suggestion unit might provide detailed activity information. If the user is excited, for example, the suggestion unit might suggest visually stimulating activities. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The proposal team can adjust the level of detail in their proposals based on the importance of the activities and events. For example, they can provide detailed proposals for high-importance activities, simplified proposals for low-importance activities, and proposals with a moderate level of detail for moderately important activities. This allows the proposal team to make efficient proposals by adjusting the level of detail based on the importance of the activities and events.

[0098] The proposal function can apply different proposal algorithms depending on the category of activity or event. For example, for relaxation-related activities, it will apply a proposal algorithm that emphasizes relaxation effects. For sports-related activities, it will apply a proposal algorithm that emphasizes exercise effects. For cultural activities, it will apply a proposal algorithm that emphasizes cultural value. By applying different proposal algorithms depending on the category of activity or event, the proposal function can make more accurate proposals.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is excited, the suggestion unit will provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The proposal team can prioritize proposals based on the timing of activities and events. For example, they might prioritize activities happening soon. They might postpone activities happening far in the future. They might prioritize activities that are concentrated within a specific period. This allows the proposal team to make efficient proposals by prioritizing proposals based on the timing of activities and events.

[0101] The proposal team can adjust the order of proposals based on the relevance of activities and events. For example, the proposal team can prioritize highly relevant activities, postpone less relevant activities, or moderately propose activities of moderate relevance. This allows the proposal team to make efficient proposals by adjusting the order of proposals based on the relevance of activities and events.

[0102] The service provider can estimate the user's emotions and adjust how activities and events are displayed based on those estimated emotions. For example, if the user is stressed, the service provider might provide a simple and highly visible display. If the user is relaxed, the service provider might provide a display that includes detailed information. If the user is excited, the service provider might provide a visually stimulating display. This allows the service provider to provide more appropriate content by adjusting how activities and events are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The service provider can select the optimal delivery method by referring to the user's past activity participation history at the time of delivery. For example, the service provider can provide relevant activities based on the activities the user has participated in in the past. For example, the service provider can analyze the user's past participation history and provide the activities that are most likely to interest the user. For example, the service provider can prioritize providing activities that the user has participated in frequently based on their past participation history. In this way, the service provider can select the optimal delivery method by referring to the user's past activity participation history.

[0104] The service provider can customize the means of delivery based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can provide activities that can be participated in for a short time. For example, if the user is relaxed, the service provider can provide activities that can be enjoyed for a long time. For example, if the user is health-conscious, the service provider can provide activities that are good for health. In this way, the service provider can provide more appropriate services by customizing the means of delivery based on the user's current lifestyle.

[0105] The service provider can estimate the user's emotions and prioritize the activities and events offered based on those emotions. For example, if the user is stressed, the service provider will prioritize relaxing activities. If the user is relaxed, the service provider will prioritize enjoyable activities. If the user is excited, the service provider will prioritize stimulating activities. This allows the service provider to provide more appropriate content by prioritizing activities and events based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider will provide activities related to that region. For example, if the user is traveling, the service provider will provide activities related to the travel destination. For example, if the user is at home, the service provider will provide activities around the user's home. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information.

[0107] The service provider can analyze the user's social media activity and propose a suitable delivery method at the time of delivery. For example, the service provider can provide relevant activities based on information shared by the user on social media. For example, the service provider can analyze the user's social media activity history and provide activities related to their interests. For example, the service provider can provide relevant activities based on information about accounts the user follows. In this way, the service provider can propose the most suitable delivery method by analyzing the user's social media activity.

[0108] The schedule analysis unit can estimate the user's emotions and adjust the schedule analysis method based on the estimated emotions. For example, if the user is feeling stressed, the schedule analysis unit will suggest a schedule that helps reduce stress. For example, if the user is relaxed, the schedule analysis unit will suggest a schedule that helps maintain relaxation. For example, if the user is busy, the schedule analysis unit will suggest an efficient schedule. In this way, the schedule analysis unit can suggest a more appropriate schedule by adjusting the schedule analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The schedule analysis unit can select the optimal analysis method by referring to the user's past schedule history during schedule analysis. For example, the schedule analysis unit can suggest an optimal schedule based on activities the user has frequently performed in the past. For example, the schedule analysis unit can suggest an efficient schedule by analyzing the user's past schedule history. For example, the schedule analysis unit can suggest a schedule that helps reduce stress based on the user's past schedule history. In this way, the schedule analysis unit can select the optimal analysis method by referring to the user's past schedule history.

[0110] The schedule analysis unit can estimate the user's emotions and determine the priority of schedule analysis based on the estimated emotions. For example, if the user is feeling stressed, the schedule analysis unit will prioritize analyzing schedules that help reduce stress. For example, if the user is relaxed, the schedule analysis unit will prioritize analyzing schedules that help maintain relaxation. For example, if the user is busy, the schedule analysis unit will prioritize analyzing efficient schedules. In this way, the schedule analysis unit can propose a more appropriate schedule by determining the priority of schedule analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The schedule analysis unit can select the optimal analysis method by considering the user's device information during schedule analysis. For example, if the user is using a smartphone, the schedule analysis unit will propose a schedule optimized for the smartphone. For example, if the user is using a tablet, the schedule analysis unit will propose a schedule optimized for the tablet. For example, if the user is using a smartwatch, the schedule analysis unit will propose a schedule optimized for the smartwatch. In this way, the schedule analysis unit can select the optimal analysis method by considering the user's device information.

[0112] The health analysis unit can estimate the user's emotions and adjust the health analysis method based on the estimated emotions. For example, if the user is stressed, the health analysis unit will perform health analysis to help reduce stress. For example, if the user is relaxed, the health analysis unit will perform health analysis to maintain relaxation. For example, if the user is excited, the health analysis unit will perform health analysis to maintain excitement. In this way, the health analysis unit can perform more appropriate health analysis by adjusting the health analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The health analysis unit can select the optimal analysis method by referring to the user's past health data during health analysis. For example, the health analysis unit performs optimal health analysis based on health data previously recorded by the user. For example, the health analysis unit analyzes the user's past health data to perform efficient health analysis. For example, the health analysis unit performs health analysis that helps reduce stress based on the user's past health data. In this way, the health analysis unit can select the optimal analysis method by referring to the user's past health data.

[0114] The health analysis unit can estimate the user's emotions and determine the priority of health analysis based on the estimated emotions. For example, if the user is stressed, the health analysis unit will prioritize health analysis that helps reduce stress. For example, if the user is relaxed, the health analysis unit will prioritize health analysis that helps maintain relaxation. For example, if the user is excited, the health analysis unit will prioritize health analysis that helps maintain excitement. In this way, the health analysis unit can perform more appropriate health analysis by determining the priority of health analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The health analysis unit can select the optimal analysis method by considering the user's lifestyle information during health analysis. For example, the health analysis unit can perform optimal health analysis based on the user's dietary information. For example, the health analysis unit can perform optimal health analysis based on the user's exercise habits information. For example, the health analysis unit can perform optimal health analysis based on the user's sleep habits information. In this way, the health analysis unit can select the optimal analysis method by considering the user's lifestyle information.

[0116] The stress analysis unit can estimate the user's emotions and adjust the stress analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the stress analysis unit will perform stress analysis that helps reduce stress. For example, if the user is relaxed, the stress analysis unit will perform stress analysis to maintain relaxation. For example, if the user is excited, the stress analysis unit will perform stress analysis to maintain excitement. In this way, the stress analysis unit can perform more appropriate stress analysis by adjusting the stress analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The stress analysis unit can select the optimal analysis method by referring to the user's past stress data during stress analysis. For example, the stress analysis unit performs the optimal stress analysis based on the stress data previously recorded by the user. For example, the stress analysis unit performs an efficient stress analysis by analyzing the user's past stress data. For example, the stress analysis unit performs a stress analysis that helps reduce stress based on the user's past stress data. In this way, the stress analysis unit can select the optimal analysis method by referring to the user's past stress data.

[0118] The stress analysis unit can estimate the user's emotions and determine the priority of stress analysis based on the estimated emotions. For example, if the user is feeling stressed, the stress analysis unit will prioritize stress analysis that helps reduce stress. For example, if the user is relaxed, the stress analysis unit will prioritize stress analysis that helps maintain relaxation. For example, if the user is excited, the stress analysis unit will prioritize stress analysis that helps maintain excitement. In this way, the stress analysis unit can perform more appropriate stress analysis by determining the priority of stress analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The stress analysis unit can select the optimal analysis method by considering the user's living environment information during stress analysis. For example, the stress analysis unit can perform the optimal stress analysis based on the user's work environment information. For example, the stress analysis unit can perform the optimal stress analysis based on the user's home environment information. For example, the stress analysis unit can perform the optimal stress analysis based on the user's social environment information. In this way, the stress analysis unit can select the optimal analysis method by considering the user's living environment information.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The AI ​​agent system can estimate the user's emotions and customize its suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing activities and events. If the user is relaxed, it can suggest even more enjoyable activities and events. If the user is excited, it can suggest stimulating activities and events. This allows for optimal suggestions tailored to the user's emotions.

[0122] The data collection unit can estimate the user's emotions and adjust the method of collecting log data based on the estimated emotions. For example, if the user is stressed, it can prioritize collecting data that helps reduce stress. If the user is relaxed, it can prioritize collecting data related to relaxation. Also, if the user is excited, it can prioritize collecting data that helps maintain that excitement. This enables optimal data collection based on the user's emotions.

[0123] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on the estimated emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. This makes it possible to provide optimal analysis results tailored to the user's emotions.

[0124] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those estimates. For example, if the user is stressed, it can simply suggest relaxing activities. If the user is relaxed, it can provide detailed activity information. If the user is excited, it can suggest visually stimulating activities. This enables optimal suggestions tailored to the user's emotions.

[0125] The service provider can estimate the user's emotions and adjust how activities and events are displayed based on those estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. And if the user is excited, a visually stimulating display method can be provided. This enables optimal display tailored to the user's emotions.

[0126] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of data related to that region. If the user is traveling, it can prioritize the collection of data related to their travel destination. Furthermore, if the user is at home, it can prioritize the collection of data around their home. This enables optimal data collection that takes the user's geographical location into consideration.

[0127] The analysis unit can apply different analysis algorithms depending on the category of log data. For example, a text analysis algorithm can be applied to message logs, a search behavior analysis algorithm to search logs, and a purchase behavior analysis algorithm to purchase logs. This enables optimal analysis according to the category of log data.

[0128] The suggestion function can apply different suggestion algorithms depending on the category of activity or event. For example, for relaxation activities, a suggestion algorithm that emphasizes relaxation effects can be applied. For sports activities, a suggestion algorithm that emphasizes exercise effects can be applied. Furthermore, for cultural activities, a suggestion algorithm that emphasizes cultural value can be applied. This enables the application of optimal suggestions tailored to the category of activity or event.

[0129] The service provider can select the optimal delivery method by referring to the user's past activity participation history. For example, it can provide relevant activities based on the activities the user has participated in in the past. It can analyze the user's past participation history and provide the activities that are most likely to interest them. Furthermore, it can prioritize providing activities that the user has participated in frequently based on their past participation history. This enables optimal delivery by referring to the user's past activity participation history.

[0130] The schedule analysis unit can select the optimal analysis method considering the user's device information. For example, if the user is using a smartphone, it can suggest a schedule optimized for smartphones. If the user is using a tablet, it can suggest a schedule optimized for tablets. Furthermore, if the user is using a smartwatch, it can suggest a schedule optimized for smartwatches. This enables optimal schedule analysis that takes the user's device information into account.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit collects user log data. For example, it collects data such as user message logs, search logs, purchase logs, and email logs. This provides basic data to understand the user's interests and lifestyle. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using techniques such as data mining, statistical analysis, and machine learning to identify users' interests and lifestyles. Step 3: The proposal unit proposes activities and events based on the analysis results obtained by the analysis unit. For example, it proposes activities and events based on the user's interests and past behavior to enrich the user's private time. Step 4: The providing team delivers the activities and events proposed by the suggesting team to the user. For example, they provide the activities and events through methods such as notifications, emails, and in-app displays, making it easy for the user to access the proposed activities and events.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, schedule analysis unit, health analysis unit, and stress analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data such as the user's message logs, search logs, purchase logs, and email logs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes activities and events based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the proposed activities and events to the user. The schedule analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule. The health analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's health status. The stress analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's stress state. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, schedule analysis unit, health analysis unit, and stress analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data such as the user's message logs, search logs, purchase logs, and email logs. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes activities and events based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the proposed activities and events to the user. The schedule analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule. The health analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's health status. The stress analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's stress state. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, schedule analysis unit, health analysis unit, and stress analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data such as the user's message log, search log, purchase log, and email log. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes activities and events based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the proposed activities and events to the user. The schedule analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule. The health analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's health status. The stress analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's stress state. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, schedule analysis unit, health analysis unit, and stress analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data such as the user's message logs, search logs, purchase logs, and email logs. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes activities and events based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the proposed activities and events to the user. The schedule analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule. The health analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's health status. The stress analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's stress state. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A collection unit that collects user log data, 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 activities and events, The system comprises a provisioning unit that provides the activities and events proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user message logs, search logs, purchase logs, and email logs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data to identify users' interests and lifestyles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose at least one activity and event. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide users with suggested activities and events. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a schedule analysis unit that analyzes the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a health analysis unit that analyzes the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes a stress analysis unit that analyzes the user's stress level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of log data collection based on the 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 log data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting log data, filter it based on the user's current activity 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 determines the priority of log data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting log data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting log data, analyze users' social media activity and collect 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 log 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 category of the log data. 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 analysis priority is determined based on when the log data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the log 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 based on the importance of the activity or event. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the activity or event. 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, prioritize them based on the timing of the activities and events. 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 suggestions based on the relevance of the activities and events. 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 how activities and events are displayed 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 the service, the optimal delivery method is selected by referring to the user's past activity participation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the means of delivery 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, It estimates the user's emotions and determines the priority of activities and events to provide 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 the service, the optimal delivery method 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 the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned schedule analysis unit, The system estimates the user's emotions and adjusts the scheduling analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned schedule analysis unit, During schedule analysis, the system selects the optimal analysis method by referring to the user's past schedule history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned schedule analysis unit, It estimates the user's emotions and determines the priority of schedule analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned schedule analysis unit, During schedule analysis, the optimal analysis method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned health analysis unit, The system estimates the user's emotions and adjusts the health analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned health analysis unit, During health analysis, the system selects the optimal analysis method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned health analysis unit, It estimates the user's emotions and prioritizes health analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned health analysis unit, During health analysis, the optimal analysis method is selected by considering the user's lifestyle information. The system described in Appendix 1, characterized by the features described herein. (Note 41) The stress analysis unit described above is The system estimates the user's emotions and adjusts the stress analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The stress analysis unit described above is During stress analysis, the system selects the optimal analysis method by referring to the user's past stress data. The system described in Appendix 1, characterized by the features described herein. (Note 43) The stress analysis unit described above is The system estimates the user's emotions and prioritizes stress analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The stress analysis unit described above is During stress analysis, the optimal analysis method is selected by considering the user's living environment information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 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 collection unit that collects user log data, 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 activities and events, The system comprises a provisioning unit that provides the activities and events proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user message logs, search logs, purchase logs, and email logs. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data to identify users' interests and lifestyles. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose at least one activity and event. The system according to feature 1.

5. The aforementioned supply unit is, Provide users with suggested activities and events. The system according to feature 1.

6. It includes a schedule analysis unit that analyzes the user's schedule. The system according to feature 1.

7. It includes a health analysis unit that analyzes the user's health status. The system according to feature 1.

8. It includes a stress analysis unit that analyzes the user's stress level. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of log data collection based on the estimated emotions. The system according to feature 1.

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

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