system

Abstract

The system according to this embodiment aims to suggest optimal hobbies and activities based on the user's lifestyle and past activity history. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a feedback collection unit. The collection unit collects the user's lifestyle and past activity history. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests optimal hobbies and activities based on the analysis results obtained by the analysis unit. The feedback collection unit collects feedback from the user after they have experienced something.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to find hobbies and activities suitable for themselves.

[0005] The system according to the embodiment aims to propose optimal hobbies and recommended activities based on the user's lifestyle and past activity history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a feedback collection unit. The data collection unit collects the user's lifestyle and past activity history. The analysis unit analyzes the data collected by the data collection unit. The suggestion unit suggests optimal hobbies and activities based on the analysis results obtained by the analysis unit. The feedback collection unit collects feedback from the user after they have experienced the activity. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal hobbies and activities based on the user's lifestyle and past activity history. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The experiential hobby discovery agent system according to an embodiment of the present invention is a system that suggests the most suitable hobbies and activities based on the user's lifestyle and past activity history. This system analyzes the user's areas of interest and recommends events and workshops held locally or online. For example, a user interested in music will be provided with information on instrument playing workshops and music festivals. By actually experiencing these activities, the user can confirm whether they are a good fit for them. After the experience, feedback is collected, and based on that data, even more suitable hobbies and activities are suggested. In this way, the system supports the user in finding a hobby that suits them. For example, the user inputs their areas of interest. For example, the AI ​​analyzes the user's lifestyle and past activity history and suggests the most suitable hobbies and activities. For example, if the user is interested in music, the AI ​​will provide information on instrument playing workshops and music festivals. By actually experiencing these activities, the user can confirm whether they are a good fit for them. After the experience, feedback is collected, and based on that data, even more suitable hobbies and activities are suggested. In this way, the system supports the user in finding a hobby that suits them. For example, this agent is very useful because it is often difficult for middle-aged and older adults to find new hobbies. For example, with the advancement of AI technology enabling personalized suggestions, experiential hobby discovery agents are a powerful tool for finding new hobbies. They aim to enrich daily life by supporting users in finding hobbies and passions that suit them. For instance, for middle-aged and older adults, finding new activities to enrich their post-retirement lives is crucial for preventing social isolation and maintaining mental health. This agent provides new stimulation and enjoyment to life by allowing users to experience areas of interest. Thus, the experiential hobby discovery agent system can support the process of finding suitable hobbies by suggesting optimal hobbies and passions based on the user's lifestyle and past activity history, and by collecting feedback after the experience.

[0029] The experiential hobby discovery agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a feedback collection unit. The collection unit collects the user's lifestyle and past activity history. For example, the collection unit collects the user's daily behavior patterns. The collection unit can also collect the user's hobbies and work style. The collection unit can also collect the user's past event participation history. For example, the collection unit collects information on events the user has participated in in the past. The collection unit can also collect the user's purchase history. For example, the collection unit collects information on products the user has purchased in the past. The collection unit can also collect the user's travel history. For example, the collection unit collects information on places the user has visited in the past. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the user's behavior patterns. The analysis unit can also analyze the user's hobbies and work style. The analysis unit can also analyze the user's past event participation history. For example, the analysis unit analyzes information on events the user has participated in in the past. The analysis unit can also analyze the user's purchase history. For example, the analysis unit can analyze information about products the user has purchased in the past. The analysis unit can also analyze the user's travel history. For example, the analysis unit can analyze information about places the user has visited in the past. The suggestion unit proposes the most suitable hobbies and activities based on the analysis results obtained by the analysis unit. For example, the suggestion unit makes suggestions based on the user's areas of interest. The suggestion unit can also suggest events and workshops held locally or online. The suggestion unit can also provide information about instrumental music workshops and music festivals based on the user's areas of interest. For example, if the user is interested in music, the suggestion unit will provide information about instrumental music workshops and music festivals. The feedback collection unit collects feedback from users after they have experienced an activity. For example, the feedback collection unit collects the user's impressions of the activity they experienced. The feedback collection unit can also collect the user's satisfaction level with the activity they experienced. The feedback collection unit can also collect suggestions for improvement regarding the activity the user experienced.For example, the feedback collection unit collects user feedback on activities they have experienced and uses that data to suggest more suitable hobbies and activities. Thus, the experiential hobby discovery agent system according to this embodiment can support the user's process of finding a suitable hobby by suggesting optimal hobbies and activities based on their lifestyle and past activity history, and by collecting feedback after the experience.

[0030] The data collection unit collects users' lifestyles and past activity history. Specifically, it utilizes data from smartphones and wearable devices to collect users' daily behavior patterns. This includes users' travel history, exercise levels, and sleep patterns. It also analyzes social media posts, online shopping history, and usage of work-related applications to collect users' hobbies and work styles. Furthermore, it acquires data from calendar and event management apps to collect users' past event participation history. For example, it collects information on concerts, sporting events, and workshops that users have attended in the past. It also utilizes data from online shopping sites and electronic receipts to collect information on products that users have purchased in the past. This includes books, music, movies, and hobby-related items. In addition, it acquires data from travel booking sites and airline apps to collect information on places that users have visited in the past to collect users' travel history. In this way, the data collection unit can centrally collect a wide range of user data and gain a detailed understanding of users' lifestyles and interests. The collected data is stored on a cloud server and made accessible to the analysis unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, to analyze user behavior patterns, it uses machine learning algorithms to classify data and identify typical and abnormal user behavior patterns. Furthermore, to analyze users' hobbies and work styles, it uses natural language processing techniques to analyze social media posts and online shopping reviews to extract user interests and preferences. In addition, to analyze users' past event participation history, it analyzes the type, frequency, and timing of events to identify what kinds of events users are interested in. For example, it analyzes information on concerts and sporting events users have attended in the past to assess their interest in music and sports. It also analyzes users' purchase history, analyzing the categories, price ranges, and purchase frequency of purchased items to identify user consumption trends and interests. For example, it analyzes information on books, music, and movies users have purchased in the past to assess their cultural interests. Finally, to analyze users' travel history, it analyzes the geographical characteristics of visited locations, travel purposes, and length of stay to identify user travel styles and interests. This allows the analysis unit to quickly and accurately analyze collected data and gain a detailed understanding of users' lifestyles and interests. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns, thereby improving the accuracy of future recommendations. This allows the analysis unit to gain a detailed understanding of users' interests and preferences, providing a foundation for making optimal recommendations.

[0032] The suggestion department proposes optimal hobbies and activities based on the analysis results obtained by the analysis department. Specifically, it generates customized suggestions that reflect the user's past behavior and interests in order to make suggestions based on the user's areas of interest. For example, if the user is interested in music, the suggestion department will provide information on instrument playing workshops and music festivals. If the user is interested in sports, the suggestion department will provide information on local sports events and fitness classes. Furthermore, the suggestion department can also suggest events and workshops held locally or online. This includes suggestions that take into account the user's place of residence and accessible areas. For example, if the user is interested in cooking, the suggestion department will provide information on local cooking classes and online cooking workshops. The suggestion department can also recommend related books, movies, and music based on the user's areas of interest. For example, if the user is interested in travel, the suggestion department will provide information on travel guidebooks and travel documentaries. In this way, the suggestion department can make diverse suggestions based on the user's interests and help them discover new hobbies and activities. Furthermore, the suggestion department can continuously improve its suggestions based on user feedback, enabling it to make more accurate suggestions. This allows the proposal department to consistently provide users with the best possible proposals and improve user satisfaction.

[0033] The Feedback Collection Department collects feedback from users after they have experienced an activity. Specifically, it requests feedback in the form of questionnaires and reviews to collect users' impressions of the activities they have experienced. For example, it collects impressions and evaluations of events and workshops that users have participated in. The Feedback Collection Department can also collect user satisfaction levels with the activities they have experienced. This includes scoring systems and comment sections to evaluate how satisfied users were. Furthermore, the Feedback Collection Department can collect areas for improvement in the activities that users have experienced. For example, it collects specific areas for improvement and suggestions for activities that users have experienced, and uses this data to suggest more suitable hobbies and activities. In this way, the Feedback Collection Department can collect valuable information based on user experiences and use it to improve the entire system. In addition, the Feedback Collection Department provides the collected feedback to the Analysis Department, which can use it as data to understand user interests and preferences in more detail. In this way, the Feedback Collection Department can support a continuous improvement process based on user experiences and make more suitable suggestions to users. Furthermore, the Feedback Collection Department can identify new trends in hobbies and activities based on user feedback and support the Suggestion Department in making suggestions based on the latest information. This allows the feedback collection unit to play a crucial role in improving user satisfaction and enhance the overall system performance.

[0034] The data collection unit can analyze the user's past activity history and select the optimal data collection method. For example, based on activities the user has frequently performed in the past, the data collection unit prioritizes collecting data related to those activities. The data collection unit can also collect data based on activities performed during specific time periods from the user's past activity history. Furthermore, the data collection unit can analyze the user's past activity history and select the most efficient data collection method. This enables efficient data collection by selecting the optimal data collection method based on past activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past activity history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter the collected lifestyle and activity history based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize collecting data related to areas the user is currently interested in. The data collection unit can also collect highly relevant data considering the user's current living situation. Furthermore, the data collection unit can filter out unnecessary data based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current living situation and areas of interest data into a generating AI and have the generating AI perform the data filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifestyle and activity history. For example, the data collection unit can prioritize the collection of information on events and workshops related to the area where the user is currently located. The data collection unit can also collect data on nearby hobby activities based on the user's geographical location. Furthermore, the data collection unit can collect data related to places visited by considering the user's travel history. In this way, by considering geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0037] The data collection unit can analyze a user's social media activity and collect relevant data when collecting lifestyle and activity history. For example, the data collection unit can collect information on events and workshops that the user has shown interest in on social media. The data collection unit can also analyze the content of a user's social media posts and collect data on related hobbies and activities. Furthermore, the data collection unit can collect relevant data based on the activity of accounts that the user follows. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a hobby-specific analysis algorithm to data related to hobbies. It can also apply a fan-activity-specific analysis algorithm to data related to fan activities. Furthermore, it can apply a lifestyle-specific analysis algorithm to lifestyle data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to identify the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also lower the priority of analysis for older data. Furthermore, the analysis unit can adjust the order of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also lower the priority of analysis for less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0042] The proposal unit can adjust the level of detail in its proposals based on the importance of the hobbies and activities. For example, it can provide detailed proposals for hobbies and activities of high importance, and concise proposals for hobbies and activities of low importance. It can also prioritize proposals according to the importance of the hobbies and activities. This allows for efficient proposals by adjusting the level of detail based on the importance of the hobbies and activities. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data to evaluate the importance of hobbies and activities into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0043] The suggestion unit can apply different suggestion algorithms depending on the category of hobby or fan activity when making suggestions. For example, for a hobby related to music, the suggestion unit can apply a music-specific suggestion algorithm. Similarly, for a hobby related to sports, it can apply a sports-specific suggestion algorithm. Furthermore, for a hobby related to art, it can apply an art-specific suggestion algorithm. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the category of hobby or fan activity. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input data to identify the category of hobby or fan activity into a generating AI and have the generating AI apply different suggestion algorithms.

[0044] The proposal department can determine the priority of proposals based on the timing of hobbies and fan activities. For example, the proposal department will prioritize proposals for upcoming events and workshops. Conversely, it can lower the priority of proposals for events and workshops that are far in the future. The proposal department can also adjust the order of proposals according to the timing of hobbies and fan activities. This allows for efficient proposals by determining the priority of proposals based on the timing of hobbies and fan activities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data for evaluating the timing of hobbies and fan activities into a generating AI and have the generating AI determine the priority of proposals.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of hobbies and fan activities. For example, the suggestion unit will prioritize suggestions for highly relevant hobbies and fan activities. Conversely, it can lower the priority of suggestions for less relevant hobbies and fan activities. The suggestion unit can also adjust the order of suggestions according to the relevance of hobbies and fan activities. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of hobbies and fan activities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data for evaluating the relevance of hobbies and fan activities into a generating AI and have the generating AI adjust the order of suggestions.

[0046] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit will request similar detailed feedback. Alternatively, if the user has provided concise feedback in the past, the feedback collection unit can request concise feedback. Furthermore, the feedback collection unit can analyze the user's past feedback history and select the most efficient collection method. This enables efficient feedback collection by selecting the optimal collection method based on past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, or without AI. For example, the feedback collection unit can input the user's past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0047] The feedback collection unit can customize the means of feedback based on the user's current living situation when collecting feedback. For example, if the user is busy, the feedback collection unit can provide a concise feedback form. Alternatively, if the user is relaxed, the feedback collection unit can provide a detailed feedback form. Furthermore, the feedback collection unit can provide the most appropriate means of feedback, taking into account the user's current living situation. This allows for the collection of more appropriate feedback by customizing the means of feedback based on the current living situation. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the feedback means.

[0048] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting feedback related to the area where the user is currently located. The feedback collection unit can also collect feedback about nearby events and workshops based on the user's geographical location information. Furthermore, the feedback collection unit can collect feedback related to places visited, taking into account the user's travel history. This allows for the collection of highly relevant feedback by considering geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal collection method.

[0049] The feedback collection unit can analyze the user's social media activity and suggest methods for providing feedback when collecting feedback. For example, the feedback collection unit can collect feedback on events and workshops that the user has shown interest in on social media. The feedback collection unit can also analyze the content of the user's social media posts and collect relevant feedback. Furthermore, the feedback collection unit can collect relevant feedback based on the activity of accounts that the user follows. This allows for the efficient collection of relevant feedback by analyzing social media activity. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's social media activity data into a generating AI and have the generating AI suggest methods for providing feedback.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect user lifestyle and activity history while taking user health data into consideration. For example, it can collect a user's exercise level and sleep patterns and suggest appropriate hobbies and activities based on their health status. It can also collect a user's dietary history and suggest hobbies and activities that support a healthy lifestyle. Furthermore, it can monitor a user's stress level and suggest hobbies and activities that have a relaxing effect. This allows for data collection based on the user's health status, enabling more appropriate suggestions.

[0052] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, it can make similar proposals based on proposals the user has accepted in the past. It can also make more appropriate proposals by avoiding proposals the user has rejected in the past. Furthermore, it can analyze a user's past proposal history and select the most effective proposal method. This allows for more efficient proposals by selecting the optimal proposal method based on past proposal history.

[0053] The data collection unit can collect user lifestyles and activity history while considering the user's device usage history. For example, it can collect the user's smartphone and PC usage history and suggest appropriate hobbies and activities based on device usage patterns. It can also collect the user's app usage history and prioritize the collection of data related to areas of interest. Furthermore, it can collect the user's internet search history and collect data based on topics of interest. By considering device usage history, it is possible to collect more relevant data.

[0054] The data collection unit can collect user lifestyle and activity history while considering the user's purchase history. For example, it can collect information on products the user has purchased in the past and suggest appropriate hobbies and activities based on their purchasing patterns. It can also prioritize the collection of data related to the user's areas of interest based on their purchase history. Furthermore, it can analyze the user's purchase history and collect the most relevant data. In this way, by considering purchase history, it is possible to collect more relevant data.

[0055] The suggestion department can propose hobbies and activities tailored to the season and weather, based on the user's lifestyle and activity history. For example, it can suggest outdoor activities and gardening in spring, water sports and beach activities in summer, hiking and reading in autumn, and indoor hobbies and activities in winter. Furthermore, it can suggest indoor activities on rainy days and outdoor activities on sunny days, depending on the weather. By providing suggestions tailored to the season and weather, it can suggest more appropriate hobbies and activities.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the user's lifestyle and past activity history. For example, it collects information such as the user's daily behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes information such as user behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. Step 3: The proposal department suggests the most suitable hobbies and activities based on the analysis results obtained by the analysis department. For example, based on the user's areas of interest, it provides information on local and online events and workshops, as well as workshops on playing musical instruments and music festivals. Step 4: The feedback collection unit collects feedback from users after their experience. For example, it collects information such as the user's impressions of the activity, satisfaction level, and areas for improvement, and uses this data to suggest more suitable hobbies and activities.

[0058] (Example of form 2) The experiential hobby discovery agent system according to an embodiment of the present invention is a system that suggests the most suitable hobbies and activities based on the user's lifestyle and past activity history. This system analyzes the user's areas of interest and recommends events and workshops held locally or online. For example, a user interested in music will be provided with information on instrument playing workshops and music festivals. By actually experiencing these activities, the user can confirm whether they are a good fit for them. After the experience, feedback is collected, and based on that data, even more suitable hobbies and activities are suggested. In this way, the system supports the user in finding a hobby that suits them. For example, the user inputs their areas of interest. For example, the AI ​​analyzes the user's lifestyle and past activity history and suggests the most suitable hobbies and activities. For example, if the user is interested in music, the AI ​​will provide information on instrument playing workshops and music festivals. By actually experiencing these activities, the user can confirm whether they are a good fit for them. After the experience, feedback is collected, and based on that data, even more suitable hobbies and activities are suggested. In this way, the system supports the user in finding a hobby that suits them. For example, this agent is very useful because it is often difficult for middle-aged and older adults to find new hobbies. For example, with the advancement of AI technology enabling personalized suggestions, experiential hobby discovery agents are a powerful tool for finding new hobbies. They aim to enrich daily life by supporting users in finding hobbies and passions that suit them. For instance, for middle-aged and older adults, finding new activities to enrich their post-retirement lives is crucial for preventing social isolation and maintaining mental health. This agent provides new stimulation and enjoyment to life by allowing users to experience areas of interest. Thus, the experiential hobby discovery agent system can support the process of finding suitable hobbies by suggesting optimal hobbies and passions based on the user's lifestyle and past activity history, and by collecting feedback after the experience.

[0059] The experiential hobby discovery agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a feedback collection unit. The collection unit collects the user's lifestyle and past activity history. For example, the collection unit collects the user's daily behavior patterns. The collection unit can also collect the user's hobbies and work style. The collection unit can also collect the user's past event participation history. For example, the collection unit collects information on events the user has participated in in the past. The collection unit can also collect the user's purchase history. For example, the collection unit collects information on products the user has purchased in the past. The collection unit can also collect the user's travel history. For example, the collection unit collects information on places the user has visited in the past. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the user's behavior patterns. The analysis unit can also analyze the user's hobbies and work style. The analysis unit can also analyze the user's past event participation history. For example, the analysis unit analyzes information on events the user has participated in in the past. The analysis unit can also analyze the user's purchase history. For example, the analysis unit can analyze information about products the user has purchased in the past. The analysis unit can also analyze the user's travel history. For example, the analysis unit can analyze information about places the user has visited in the past. The suggestion unit proposes the most suitable hobbies and activities based on the analysis results obtained by the analysis unit. For example, the suggestion unit makes suggestions based on the user's areas of interest. The suggestion unit can also suggest events and workshops held locally or online. The suggestion unit can also provide information about instrumental music workshops and music festivals based on the user's areas of interest. For example, if the user is interested in music, the suggestion unit will provide information about instrumental music workshops and music festivals. The feedback collection unit collects feedback from users after they have experienced an activity. For example, the feedback collection unit collects the user's impressions of the activity they experienced. The feedback collection unit can also collect the user's satisfaction level with the activity they experienced. The feedback collection unit can also collect suggestions for improvement regarding the activity the user experienced.For example, the feedback collection unit collects user feedback on activities they have experienced and uses that data to suggest more suitable hobbies and activities. Thus, the experiential hobby discovery agent system according to this embodiment can support the user's process of finding a suitable hobby by suggesting optimal hobbies and activities based on their lifestyle and past activity history, and by collecting feedback after the experience.

[0060] The data collection unit collects users' lifestyles and past activity history. Specifically, it utilizes data from smartphones and wearable devices to collect users' daily behavior patterns. This includes users' travel history, exercise levels, and sleep patterns. It also analyzes social media posts, online shopping history, and usage of work-related applications to collect users' hobbies and work styles. Furthermore, it acquires data from calendar and event management apps to collect users' past event participation history. For example, it collects information on concerts, sporting events, and workshops that users have attended in the past. It also utilizes data from online shopping sites and electronic receipts to collect information on products that users have purchased in the past. This includes books, music, movies, and hobby-related items. In addition, it acquires data from travel booking sites and airline apps to collect information on places that users have visited in the past to collect users' travel history. In this way, the data collection unit can centrally collect a wide range of user data and gain a detailed understanding of users' lifestyles and interests. The collected data is stored on a cloud server and made accessible to the analysis unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the data collection unit. Specifically, to analyze user behavior patterns, it uses machine learning algorithms to classify data and identify typical and abnormal user behavior patterns. Furthermore, to analyze users' hobbies and work styles, it uses natural language processing techniques to analyze social media posts and online shopping reviews to extract user interests and preferences. In addition, to analyze users' past event participation history, it analyzes the type, frequency, and timing of events to identify what kinds of events users are interested in. For example, it analyzes information on concerts and sporting events users have attended in the past to assess their interest in music and sports. It also analyzes users' purchase history, analyzing the categories, price ranges, and purchase frequency of purchased items to identify user consumption trends and interests. For example, it analyzes information on books, music, and movies users have purchased in the past to assess their cultural interests. Finally, to analyze users' travel history, it analyzes the geographical characteristics of visited locations, travel purposes, and length of stay to identify user travel styles and interests. This allows the analysis unit to quickly and accurately analyze collected data and gain a detailed understanding of users' lifestyles and interests. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns, thereby improving the accuracy of future recommendations. This allows the analysis unit to gain a detailed understanding of users' interests and preferences, providing a foundation for making optimal recommendations.

[0062] The suggestion department proposes optimal hobbies and activities based on the analysis results obtained by the analysis department. Specifically, it generates customized suggestions that reflect the user's past behavior and interests in order to make suggestions based on the user's areas of interest. For example, if the user is interested in music, the suggestion department will provide information on instrument playing workshops and music festivals. If the user is interested in sports, the suggestion department will provide information on local sports events and fitness classes. Furthermore, the suggestion department can also suggest events and workshops held locally or online. This includes suggestions that take into account the user's place of residence and accessible areas. For example, if the user is interested in cooking, the suggestion department will provide information on local cooking classes and online cooking workshops. The suggestion department can also recommend related books, movies, and music based on the user's areas of interest. For example, if the user is interested in travel, the suggestion department will provide information on travel guidebooks and travel documentaries. In this way, the suggestion department can make diverse suggestions based on the user's interests and help them discover new hobbies and activities. Furthermore, the suggestion department can continuously improve its suggestions based on user feedback, enabling it to make more accurate suggestions. This allows the proposal department to consistently provide users with the best possible proposals and improve user satisfaction.

[0063] The Feedback Collection Department collects feedback from users after they have experienced an activity. Specifically, it requests feedback in the form of questionnaires and reviews to collect users' impressions of the activities they have experienced. For example, it collects impressions and evaluations of events and workshops that users have participated in. The Feedback Collection Department can also collect user satisfaction levels with the activities they have experienced. This includes scoring systems and comment sections to evaluate how satisfied users were. Furthermore, the Feedback Collection Department can collect areas for improvement in the activities that users have experienced. For example, it collects specific areas for improvement and suggestions for activities that users have experienced, and uses this data to suggest more suitable hobbies and activities. In this way, the Feedback Collection Department can collect valuable information based on user experiences and use it to improve the entire system. In addition, the Feedback Collection Department provides the collected feedback to the Analysis Department, which can use it as data to understand user interests and preferences in more detail. In this way, the Feedback Collection Department can support a continuous improvement process based on user experiences and make more suitable suggestions to users. Furthermore, the Feedback Collection Department can identify new trends in hobbies and activities based on user feedback and support the Suggestion Department in making suggestions based on the latest information. This allows the feedback collection unit to play a crucial role in improving user satisfaction and enhance the overall system performance.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of collecting lifestyle and activity history based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect lifestyle and activity history during times when the user is relaxed. If the user is stressed, the data collection unit can also collect data during times when the user is not stressed. If the user is busy, the data collection unit can take the user's schedule into consideration and collect data during free time. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past activity history and select the optimal data collection method. For example, based on activities the user has frequently performed in the past, the data collection unit prioritizes collecting data related to those activities. The data collection unit can also collect data based on activities performed during specific time periods from the user's past activity history. Furthermore, the data collection unit can analyze the user's past activity history and select the most efficient data collection method. This enables efficient data collection by selecting the optimal data collection method based on past activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past activity history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter the collected lifestyle and activity history based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize collecting data related to areas the user is currently interested in. The data collection unit can also collect highly relevant data considering the user's current living situation. Furthermore, the data collection unit can filter out unnecessary data based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current living situation and areas of interest data into a generating AI and have the generating AI perform the data filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting data related to the areas in which the user is excited. Similarly, if the user is relaxed, the data collection unit can prioritize collecting data related to the areas in which the user is relaxed. Furthermore, if the user is stressed, the data collection unit can prioritize collecting data related to areas in which the user is not stressed. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifestyle and activity history. For example, the data collection unit can prioritize the collection of information on events and workshops related to the area where the user is currently located. The data collection unit can also collect data on nearby hobby activities based on the user's geographical location. Furthermore, the data collection unit can collect data related to places visited by considering the user's travel history. In this way, by considering geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0069] The data collection unit can analyze a user's social media activity and collect relevant data when collecting lifestyle and activity history. For example, the data collection unit can collect information on events and workshops that the user has shown interest in on social media. The data collection unit can also analyze the content of a user's social media posts and collect data on related hobbies and activities. Furthermore, the data collection unit can collect relevant data based on the activity of accounts that the user follows. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0070] 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 relaxed, the analysis unit provides detailed analysis results. If the user is stressed, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a hobby-specific analysis algorithm to data related to hobbies. It can also apply a fan-activity-specific analysis algorithm to data related to fan activities. Furthermore, it can apply a lifestyle-specific analysis algorithm to lifestyle data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to identify the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] 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 relaxed, the analysis unit will provide a longer analysis result. If the user is in a hurry, the analysis unit can also provide a shorter analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also lower the priority of analysis for older data. Furthermore, the analysis unit can adjust the order of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also lower the priority of analysis for less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. If the user is excited, it can provide visually appealing suggestions. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The proposal unit can adjust the level of detail in its proposals based on the importance of the hobbies and activities. For example, it can provide detailed proposals for hobbies and activities of high importance, and concise proposals for hobbies and activities of low importance. It can also prioritize proposals according to the importance of the hobbies and activities. This allows for efficient proposals by adjusting the level of detail based on the importance of the hobbies and activities. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data to evaluate the importance of hobbies and activities into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0078] The suggestion unit can apply different suggestion algorithms depending on the category of hobby or fan activity when making suggestions. For example, for a hobby related to music, the suggestion unit can apply a music-specific suggestion algorithm. Similarly, for a hobby related to sports, it can apply a sports-specific suggestion algorithm. Furthermore, for a hobby related to art, it can apply an art-specific suggestion algorithm. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the category of hobby or fan activity. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input data to identify the category of hobby or fan activity into a generating AI and have the generating AI apply different suggestion algorithms.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can make a longer suggestion. If the user is in a hurry, the suggestion unit can make a shorter suggestion. If the user is excited, the suggestion unit can make a visually appealing suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The proposal department can determine the priority of proposals based on the timing of hobbies and fan activities. For example, the proposal department will prioritize proposals for upcoming events and workshops. Conversely, it can lower the priority of proposals for events and workshops that are far in the future. The proposal department can also adjust the order of proposals according to the timing of hobbies and fan activities. This allows for efficient proposals by determining the priority of proposals based on the timing of hobbies and fan activities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data for evaluating the timing of hobbies and fan activities into a generating AI and have the generating AI determine the priority of proposals.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of hobbies and fan activities. For example, the suggestion unit will prioritize suggestions for highly relevant hobbies and fan activities. Conversely, it can lower the priority of suggestions for less relevant hobbies and fan activities. The suggestion unit can also adjust the order of suggestions according to the relevance of hobbies and fan activities. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of hobbies and fan activities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data for evaluating the relevance of hobbies and fan activities into a generating AI and have the generating AI adjust the order of suggestions.

[0082] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit may request detailed feedback. If the user is stressed, the feedback collection unit may request concise feedback. If the user is excited, the feedback collection unit may provide a visually appealing feedback form. This allows for the collection of more appropriate feedback by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit will request similar detailed feedback. Alternatively, if the user has provided concise feedback in the past, the feedback collection unit can request concise feedback. Furthermore, the feedback collection unit can analyze the user's past feedback history and select the most efficient collection method. This enables efficient feedback collection by selecting the optimal collection method based on past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, or without AI. For example, the feedback collection unit can input the user's past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0084] The feedback collection unit can customize the means of feedback based on the user's current living situation when collecting feedback. For example, if the user is busy, the feedback collection unit can provide a concise feedback form. Alternatively, if the user is relaxed, the feedback collection unit can provide a detailed feedback form. Furthermore, the feedback collection unit can provide the most appropriate means of feedback, taking into account the user's current living situation. This allows for the collection of more appropriate feedback by customizing the means of feedback based on the current living situation. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the feedback means.

[0085] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is excited, the feedback collection unit will prioritize collecting that feedback. Similarly, if the user is relaxed, the feedback collection unit can prioritize collecting that feedback. Furthermore, if the user is stressed, the feedback collection unit can prioritize collecting that feedback. This allows for the collection of more appropriate feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting feedback related to the area where the user is currently located. The feedback collection unit can also collect feedback about nearby events and workshops based on the user's geographical location information. Furthermore, the feedback collection unit can collect feedback related to places visited, taking into account the user's travel history. This allows for the collection of highly relevant feedback by considering geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal collection method.

[0087] The feedback collection unit can analyze the user's social media activity and suggest methods for providing feedback when collecting feedback. For example, the feedback collection unit can collect feedback on events and workshops that the user has shown interest in on social media. The feedback collection unit can also analyze the content of the user's social media posts and collect relevant feedback. Furthermore, the feedback collection unit can collect relevant feedback based on the activity of accounts that the user follows. This allows for the efficient collection of relevant feedback by analyzing social media activity. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's social media activity data into a generating AI and have the generating AI suggest methods for providing feedback.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can suggest new hobbies or activities related to their favorite idols at that time. If the user is stressed, the suggestion function can also suggest suggestions when the user's stress has subsided. Furthermore, if the user is excited, the suggestion function can leverage that excitement to suggest more active hobbies or activities related to their favorite idols. By adjusting the timing of suggestions according to the user's emotions, more effective suggestions become possible.

[0090] The data collection unit can collect user lifestyle and activity history while taking user health data into consideration. For example, it can collect a user's exercise level and sleep patterns and suggest appropriate hobbies and activities based on their health status. It can also collect a user's dietary history and suggest hobbies and activities that support a healthy lifestyle. Furthermore, it can monitor a user's stress level and suggest hobbies and activities that have a relaxing effect. This allows for data collection based on the user's health status, enabling more appropriate suggestions.

[0091] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is stressed, the analysis unit can perform a concise analysis to avoid burdening the user. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results to capture the user's interest. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0092] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, it can make similar proposals based on proposals the user has accepted in the past. It can also make more appropriate proposals by avoiding proposals the user has rejected in the past. Furthermore, it can analyze a user's past proposal history and select the most effective proposal method. This allows for more efficient proposals by selecting the optimal proposal method based on past proposal history.

[0093] The feedback collection unit can estimate the user's emotions and adjust the frequency of feedback collection based on those estimates. For example, if the user is relaxed, the feedback collection unit will request feedback more frequently. Conversely, if the user is stressed, the feedback collection unit can reduce the frequency of feedback. Furthermore, if the user is excited, the feedback collection unit can request feedback at that time. By adjusting the frequency of feedback collection according to the user's emotions, more appropriate feedback can be collected.

[0094] The data collection unit can collect user lifestyles and activity history while considering the user's device usage history. For example, it can collect the user's smartphone and PC usage history and suggest appropriate hobbies and activities based on device usage patterns. It can also collect the user's app usage history and prioritize the collection of data related to areas of interest. Furthermore, it can collect the user's internet search history and collect data based on topics of interest. By considering device usage history, it is possible to collect more relevant data.

[0095] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will suggest hobbies or activities that promote relaxation. If the user is stressed, the suggestion function can also suggest hobbies or activities that help relieve stress. Furthermore, if the user is excited, the suggestion function can suggest active hobbies or activities. By adjusting the content of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0096] The data collection unit can collect user lifestyle and activity history while considering the user's purchase history. For example, it can collect information on products the user has purchased in the past and suggest appropriate hobbies and activities based on their purchasing patterns. It can also prioritize the collection of data related to the user's areas of interest based on their purchase history. Furthermore, it can analyze the user's purchase history and collect the most relevant data. In this way, by considering purchase history, it is possible to collect more relevant data.

[0097] The analysis unit can estimate the user's emotions and adjust the visualization method of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed graphs and charts. If the user is stressed, the analysis unit can provide a concise visualization. Furthermore, if the user is excited, the analysis unit can provide a visually appealing infographic. By adjusting the visualization method of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0098] The suggestion department can propose hobbies and activities tailored to the season and weather, based on the user's lifestyle and activity history. For example, it can suggest outdoor activities and gardening in spring, water sports and beach activities in summer, hiking and reading in autumn, and indoor hobbies and activities in winter. Furthermore, it can suggest indoor activities on rainy days and outdoor activities on sunny days, depending on the weather. By providing suggestions tailored to the season and weather, it can suggest more appropriate hobbies and activities.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects the user's lifestyle and past activity history. For example, it collects information such as the user's daily behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes information such as user behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. Step 3: The proposal department suggests the most suitable hobbies and activities based on the analysis results obtained by the analysis department. For example, based on the user's areas of interest, it provides information on local and online events and workshops, as well as workshops on playing musical instruments and music festivals. Step 4: The feedback collection unit collects feedback from users after their experience. For example, it collects information such as the user's impressions of the activity, satisfaction level, and areas for improvement, and uses this data to suggest more suitable hobbies and activities.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and feedback collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's daily behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history using the control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to analyze the user's behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. The proposal unit, using the specific processing unit 290 of the data processing unit 12, proposes optimal hobbies and activities based on the analysis results and provides information on events and workshops held locally or online. The feedback collection unit collects the user's impressions, satisfaction level, and areas for improvement regarding the activities they have experienced using the control unit 46A of the smart device 14, and proposes more suitable hobbies and activities based on that data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and feedback collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's daily behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to analyze the user's behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. The suggestion unit, using the specific processing unit 290 of the data processing unit 12, suggests optimal hobbies and activities based on the analysis results and provides information on events and workshops held locally or online. The feedback collection unit collects the user's impressions, satisfaction level, and areas for improvement regarding the activities experienced by the user using the control unit 46A of the smart glasses 214, and suggests more suitable hobbies and activities based on that data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and feedback collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's daily behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to analyze the user's behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. The suggestion unit, using the specific processing unit 290 of the data processing unit 12, suggests optimal hobbies and activities based on the analysis results and provides information on events and workshops held locally or online. The feedback collection unit collects the user's impressions, satisfaction level, and areas for improvement regarding the activities experienced by the user using the control unit 46A of the headset terminal 314, and suggests more suitable hobbies and activities based on that data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[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 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.

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

[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 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).

[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] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and feedback collection unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's daily behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history using the control unit 46A of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12, for example, to analyze the user's behavior patterns, hobbies, work style, past event participation history, purchase history, and travel history. The suggestion unit, for example, uses the specific processing unit 290 of the data processing unit 12 to suggest optimal hobbies and activities based on the analysis results and provides information on events and workshops held locally or online. The feedback collection unit collects the user's impressions, satisfaction level, and areas for improvement regarding the activities experienced by the user using the control unit 46A of the robot 414, and suggests more suitable hobbies and activities based on that data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A data collection unit that collects users' lifestyles and past activity history, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable hobbies and activities for fans, It includes a feedback collection unit that collects user feedback after the user has experienced the product. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting lifestyle and activity history based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past activity history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting lifestyle and activity history data, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting lifestyle and activity history 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 7) The aforementioned collection unit is When collecting lifestyle and activity history data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of your hobbies and activities related to your favorite idols. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of hobbies or fan activities. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of your hobbies and fan activities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on their relevance to your hobbies and activities related to your favorite idols / characters. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback collection unit is When collecting feedback, customize the feedback method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback collection unit is When collecting feedback, we analyze users' social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects users' lifestyles and past activity history, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable hobbies and activities for fans, It includes a feedback collection unit that collects user feedback after the user has experienced the product. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting lifestyle and activity history based on the estimated user emotions. The system according to feature 1.

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

4. The aforementioned collection unit is When collecting lifestyle and activity history data, filtering is performed based on the user's current living situation and areas of interest. The system according to feature 1.

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

6. The aforementioned collection unit is When collecting lifestyle and activity history data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting lifestyle and activity history data, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

8. 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 according to feature 1.

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