system

Abstract

The system according to this embodiment aims to provide a stress-free experience by autonomously suggesting and adjusting the user experience to match their preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a reservation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The reservation unit makes reservations and schedules based on the content proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that users need to spend a lot of time and effort on information collection and scheduling to find an experience suitable for themselves, and they feel stressed.

[0005] The system according to the embodiment aims to autonomously propose and adjust an experience suitable for the user's preference and provide a stress-free experience.

Means for Solving the Problems

[0006] [[ID=四十五]] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a reservation unit. The data collection unit collects user information. The analysis unit analyzes the information collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The reservation unit makes reservations and schedules based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can autonomously propose and adjust an experience tailored to the user's preferences, providing a stress-free experience. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 experience suggestion system according to an embodiment of the present invention is a system in which an AI agent autonomously suggests and adjusts experiences tailored to the user's preferences. This experience suggestion system considers the user's interests, hobbies, past activities, and real-time mood to customize and suggest experiences such as travel, events, and dining. For example, the experience suggestion system provides the user with a stress-free experience by having the AI ​​fine-tune the plan in real time based on user feedback and automatically making restaurant reservations and scheduling activities. This experience suggestion system suggests the optimal experience based on the user's past behavioral data and preference information. Furthermore, because the experience suggestion system makes adjustments and new suggestions in real time according to preferences during the activity, it can flexibly respond to schedule changes. The experience suggestion system maximizes user satisfaction by autonomously making reservations and cancellations and providing the optimal experience without stress. For example, the user inputs "I want to go from XX to XX" into the experience suggestion system. In this case, the user only needs to input the departure point and destination. For example, the user inputs "I want to go from my home to the station." This information is input into the generating AI. Next, the generating AI analyzes the input information and creates a video showing how to get from the current location to the destination. The AI ​​generates a video that follows a route calculated based on map data. For example, if a user enters a route from their home to the train station, a video following that route will be generated. The generated video starts navigating according to the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video will also start navigating north. This allows the user to receive navigation that is aligned with their direction of travel. Furthermore, the video screen moves in accordance with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it enjoyable for everyone. Users can receive intuitive navigation without having to perform complex operations.Furthermore, since the viewpoint of the smartphone becomes the axis of all directions, there is no confusion, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with the smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. As a result, the experience suggestion system can autonomously suggest and adjust experiences to match the user's preferences, providing a stress-free experience.

[0029] The experience suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a reservation unit. The collection unit collects user information. User information includes, but is not limited to, personal information, behavioral data, and preference information. The collection unit collects, for example, the user's past behavioral data. The collection unit can also collect the user's preference information. Furthermore, the collection unit can collect the user's mood in real time. For example, the collection unit collects the user's past purchase history. The collection unit can also collect the user's browsing history. The collection unit can also collect the user's survey results. The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, and machine learning, but is not limited to these methods. The analysis unit analyzes the user's interests and hobbies using, for example, data mining techniques. The analysis unit can also analyze the user's behavioral patterns using statistical analysis techniques. The analysis unit can also analyze the user's preferences using machine learning techniques. For example, the analysis unit uses data mining techniques to extract the user's interests and hobbies from their past behavioral data. The analysis unit uses statistical analysis techniques to analyze the user's behavioral patterns. The analysis unit uses machine learning techniques to predict the user's preferences. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. Suggestions may include, for example, restaurant recommendations or travel plan suggestions, but are not limited to these examples. For example, the suggestion unit may suggest the best restaurant for the user based on the analysis results. The suggestion unit may also suggest the best travel plan for the user based on the analysis results. The suggestion unit may also suggest the best event for the user based on the analysis results. For example, the suggestion unit may suggest the best restaurant for the user based on the analysis results. The suggestion unit may suggest the best travel plan for the user based on the analysis results. The suggestion unit may also suggest the best event for the user based on the analysis results. The reservation unit makes reservations and schedules based on the suggestions made by the suggestion unit. Reservations and scheduling may include, for example, online reservations or calendar integration, but are not limited to these examples. The reservation unit may, for example, make reservations for the suggested restaurants.The reservation unit can also schedule the suggested activities. The reservation unit can also make reservations for the suggested events. For example, the reservation unit can make online reservations for the suggested restaurants. The reservation unit can link the suggested activities to the calendar. The reservation unit can make online reservations for the suggested events. In this way, the experience suggestion system according to the embodiment can provide the user with the optimal experience by collecting, analyzing, suggesting, and making reservations and scheduling user information. Some or all of the above-described processes in the collection unit, analysis unit, suggestion unit, and reservation unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user information into the AI ​​and have the AI ​​perform information collection. The analysis unit can input the collected information into the AI ​​and have the AI ​​perform information analysis. The suggestion unit can input the analysis results into the AI ​​and have the AI ​​perform suggestion generation. The reservation unit can input the suggested content into the AI ​​and have the AI ​​perform reservations and scheduling.

[0030] The data collection unit collects user information. This information includes, but is not limited to, personal information, behavioral data, and preference information. For example, the unit collects users' past behavioral data. It can also collect user preference information. Furthermore, it can collect user mood data in real time. For example, the unit collects users' past purchase history. It can also collect users' browsing history. It can also collect user survey results. The data collection unit utilizes various devices and platforms to collect this information. For example, it can obtain location information and health data from users' smartphones and wearable devices. Furthermore, it can collect access logs and clickstream data from websites and applications used by users. This allows the data collection unit to obtain detailed data on user behavior and preferences, enabling it to accurately understand user needs and preferences. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning, but is not limited to these examples. For example, the analysis unit can use data mining techniques to analyze users' interests and hobbies. It can also use statistical analysis techniques to analyze users' behavioral patterns. Furthermore, it can use machine learning techniques to analyze users' preferences. For instance, the analysis unit can use data mining techniques to extract interests and hobbies from users' past behavioral data. It can use statistical analysis techniques to analyze users' behavioral patterns. It can use machine learning techniques to predict users' preferences. The analysis unit can combine these techniques to analyze user behavior and preferences from multiple perspectives. For example, by using data mining techniques to extract users' interests and hobbies, statistical analysis techniques to analyze behavioral patterns, and machine learning techniques to predict preferences, it can provide optimal suggestions to meet user needs. In addition, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to accurately understand user behavior and preferences, providing a foundation for making optimal suggestions.

[0032] The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. These suggestions may include, but are not limited to, restaurant recommendations or travel plan suggestions. For example, the suggestion unit can suggest the most suitable restaurant for the user based on the analysis results. It can also suggest the most suitable travel plan for the user based on the analysis results. It can also suggest the most suitable event for the user based on the analysis results. To make these suggestions, the suggestion unit can provide personalized suggestions based on the user's interests and preferences. For example, it can suggest new restaurants or events that match the user's preferences based on data from restaurants the user has visited or events they have attended in the past. Furthermore, the suggestion unit can also make suggestions that match the user's current situation and mood. For example, by inputting the user's current mood, it can suggest restaurants or events that suit that mood. This allows the suggestion unit to provide suggestions that best meet the user's needs and improve user satisfaction.

[0033] The Reservations Department makes reservations and schedules based on the proposals made by the Proposal Department. Reservations and scheduling are made through methods such as online reservations and calendar integration, but are not limited to these examples. For example, the Reservations Department can make reservations for suggested restaurants, schedule suggested activities, and make reservations for suggested events. For instance, the Reservations Department can make online reservations for suggested restaurants, integrate suggested activities into its calendar, and make online reservations for suggested events. The Reservations Department can integrate with various reservation systems and calendar applications to make these reservations and schedules. For example, it can integrate with restaurant reservation systems to allow users to easily make reservations for suggested restaurants, or integrate with calendar applications to automatically add suggested activities and events to users' schedules. This allows the Reservations Department to improve user convenience and smoothly realize the suggested experiences. Furthermore, the Reservations Department can collect user feedback and continuously improve the accuracy and effectiveness of reservations and scheduling. For example, based on user feedback, it can review its integration methods with reservation systems and calendar applications to provide a more user-friendly system. This allows the reservation department to provide users with quick and reliable reservation and scheduling services, thereby improving user satisfaction.

[0034] The proposal unit can fine-tune the plan in real time based on user feedback. For example, the proposal unit can collect user feedback and fine-tune the plan in real time. The proposal unit can also modify the proposal based on user feedback. The proposal unit can also add to the proposal based on user feedback. For example, the proposal unit collects user feedback and fine-tunes the plan in real time. The proposal unit modifies the proposal based on user feedback. The proposal unit adds to the proposal based on user feedback. This allows for improved user satisfaction by fine-tuning the plan in real time based on user feedback. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user feedback into AI and have AI perform the plan fine-tuning.

[0035] The reservation department can automatically make restaurant reservations and schedule activities. For example, the reservation department can automatically make reservations for suggested restaurants. The reservation department can also automatically schedule suggested activities. The reservation department can also automatically make reservations for suggested events. For example, the reservation department can automatically make online reservations for suggested restaurants. The reservation department can automatically link suggested activities to the calendar. The reservation department can automatically make online reservations for suggested events. This reduces the effort required from users by automating restaurant reservations and activity scheduling. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input suggested restaurant reservations into AI and have the AI ​​execute the reservations.

[0036] The data collection unit can collect data on the user's past behavior and preferences. For example, the data collection unit can collect the user's past purchase history. The data collection unit can also collect the user's browsing history. The data collection unit can also collect the user's survey results. For example, the data collection unit can collect the user's past purchase history. The data collection unit can collect the user's browsing history. The data collection unit can collect the user's survey results. By collecting data on the user's past behavior and preferences, it becomes possible to make more appropriate suggestions. 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 behavior data into AI and have AI perform the data collection.

[0037] The analysis unit can analyze users' interests and hobbies based on collected information. For example, the analysis unit can use data mining techniques to analyze users' interests and hobbies. The analysis unit can also use statistical analysis techniques to analyze users' behavioral patterns. The analysis unit can also use machine learning techniques to analyze users' preferences. For example, the analysis unit can use data mining techniques to extract interests and hobbies from users' past behavioral data. The analysis unit can use statistical analysis techniques to analyze users' behavioral patterns. The analysis unit can use machine learning techniques to predict users' preferences. As a result, by analyzing users' interests and hobbies based on collected information, it becomes possible to make suggestions that are tailored to the user. 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 collected information into AI and have the AI ​​perform the analysis of the information.

[0038] The suggestion unit can propose the optimal experience based on the analysis results. For example, the suggestion unit can suggest the best restaurant for the user based on the analysis results. The suggestion unit can also suggest the best travel plan for the user based on the analysis results. The suggestion unit can also suggest the best event for the user based on the analysis results. For example, the suggestion unit can suggest the best restaurant for the user based on the analysis results. The suggestion unit can suggest the best travel plan for the user based on the analysis results. The suggestion unit can suggest the best event for the user based on the analysis results. By proposing the optimal experience based on the analysis results, user satisfaction can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into AI and have the AI ​​generate suggestions.

[0039] The reservation department can autonomously make and cancel reservations. For example, the reservation department can autonomously make reservations for suggested restaurants. The reservation department can also autonomously schedule suggested activities. The reservation department can also autonomously make reservations for suggested events. For example, the reservation department can autonomously make online reservations for suggested restaurants. The reservation department can autonomously link suggested activities to the calendar. The reservation department can autonomously make online reservations for suggested events. This reduces the effort required from the user by allowing reservations and cancellations to be made autonomously. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input the reservation of a suggested restaurant into AI and have the AI ​​execute the reservation.

[0040] The data collection unit can analyze the user's past behavioral data and select the optimal information collection method. For example, the data collection unit can prioritize collecting information sources that the user has preferred to use in the past. The data collection unit can also exclude information sources that the user has avoided in the past. The data collection unit can also determine the optimal timing for information collection based on the user's behavioral patterns. For example, the data collection unit can prioritize collecting information sources that the user has preferred to use in the past. The data collection unit can exclude information sources that the user has avoided in the past. The data collection unit can determine the optimal timing for information collection based on the user's behavioral patterns. This allows the optimal information collection method to be selected by analyzing the user's past behavioral data. 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 behavioral data into AI and have AI select the information collection method.

[0041] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to areas the user is currently interested in. The data collection unit can also filter and provide appropriate information according to the user's lifestyle. The data collection unit can also collect highly relevant information based on the user's areas of interest. For example, the data collection unit can prioritize collecting information related to areas the user is currently interested in. The data collection unit can filter and provide appropriate information according to the user's lifestyle. The data collection unit collects highly relevant information based on the user's areas of interest. This allows for the provision of more relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into AI and have the AI ​​perform the information filtering.

[0042] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize collecting event information related to the user's current location. The data collection unit can also prioritize collecting information about nearby restaurants based on the user's location. The data collection unit can also collect information about the most suitable tourist spots according to the user's geographical location. For example, the data collection unit can prioritize collecting event information related to the user's current location. The data collection unit can prioritize collecting information about nearby restaurants based on the user's location. The data collection unit can collect information about the most suitable tourist spots according to the user's geographical location. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the information collection.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect event information that the user has shown interest in on social media. The data collection unit can also collect relevant restaurant information based on the user's social media posts. The data collection unit can also collect event information that the user's social media followers are participating in. For example, the data collection unit can collect event information that the user has shown interest in on social media. The data collection unit can collect relevant restaurant information based on the user's social media posts. The data collection unit can collect event information that the user's social media followers are participating in. This allows the system to provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have AI perform the data collection.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can perform a concise analysis on information of low importance. The analysis unit can determine the priority of the analysis according to its importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a travel-specific analysis algorithm to travel information. The analysis unit can also apply a restaurant-specific analysis algorithm to restaurant information. The analysis unit can also apply an event-specific analysis algorithm to event information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. The analysis unit can also determine the priority of analysis according to the timing of information collection. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit analyzes older information as needed. The analysis unit determines the priority of analysis according to the timing of information collection. By determining the priority of analysis based on the timing of information collection, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information collection into the AI ​​and have the AI ​​determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may postpone the analysis of less relevant information. The analysis unit adjusts the order of analysis according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0048] The proposal unit can adjust the level of detail of its proposals based on the importance of the experience. For example, the proposal unit can provide detailed proposals for high-importance experiences, and concise proposals for low-importance experiences. The proposal unit can also determine the priority of proposals according to their importance. For example, the proposal unit can provide detailed proposals for high-importance experiences, and concise proposals for low-importance experiences, and determine the priority of proposals according to their importance. This allows for more appropriate proposals by adjusting the level of detail of proposals based on the importance of the experience. 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 the importance of the experience into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0049] The suggestion unit can apply different suggestion algorithms depending on the category of the experience when making suggestions. For example, the suggestion unit can apply a travel-specific suggestion algorithm to travel experiences. The suggestion unit can also apply a restaurant-specific suggestion algorithm to restaurant experiences. The suggestion unit can also apply an event-specific suggestion algorithm to event experiences. For example, the suggestion unit can apply a travel-specific suggestion algorithm to travel experiences. The suggestion unit can apply a restaurant-specific suggestion algorithm to restaurant experiences. The suggestion unit can apply an event-specific suggestion algorithm to event experiences. By applying different suggestion algorithms depending on the category of the experience, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the experience into the AI ​​and have the AI ​​perform the application of the suggestion algorithm.

[0050] The proposal department can determine the priority of proposals based on the timing of the experience. For example, the proposal department may prioritize proposals for the most recent experience. The proposal department may also make proposals for experiences in the distant future as needed. The proposal department can also determine the priority of proposals according to the timing of the experience. For example, the proposal department may prioritize proposals for the most recent experience. The proposal department may also make proposals for experiences in the distant future as needed. The proposal department determines the priority of proposals according to the timing of the experience. This allows for more appropriate proposals by determining the priority of proposals based on the timing of the experience. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of the experience into the AI ​​and have the AI ​​determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of experiences when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant experiences. The suggestion unit may also postpone suggesting less relevant experiences. The suggestion unit can also adjust the order of suggestions according to the relevance of experiences. For example, the suggestion unit may prioritize suggesting highly relevant experiences. The suggestion unit may postpone suggesting less relevant experiences. The suggestion unit adjusts the order of suggestions according to the relevance of experiences. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of experiences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of experiences into AI and have AI perform the adjustment of the suggestion order.

[0052] The reservation unit can analyze the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation unit may prioritize suggesting reservation methods the user has used in the past. The reservation unit can also select the optimal reservation method from the user's past reservation history. The reservation unit can also suggest the optimal reservation method based on the user's reservation history. For example, the reservation unit may prioritize suggesting reservation methods the user has used in the past. The reservation unit may select the optimal reservation method from the user's past reservation history. The reservation unit may suggest the optimal reservation method based on the user's reservation history. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit may input the user's past reservation history into AI and have AI perform the selection of a reservation method.

[0053] The reservation unit can customize the reservation process based on the user's current lifestyle. For example, if the user is busy, the reservation unit can provide a simple reservation method. If the user is relaxed, the reservation unit can also provide a detailed reservation method. The reservation unit can also suggest the most suitable reservation method according to the user's lifestyle. For example, if the user is busy, the reservation unit can provide a simple reservation method. If the user is relaxed, the reservation unit can provide a detailed reservation method. The reservation unit can suggest the most suitable reservation method according to the user's lifestyle. This allows for more appropriate reservations by customizing the reservation method based on the user's current lifestyle. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's lifestyle into AI and have AI perform the customization of the reservation method.

[0054] The reservation department can select the optimal reservation method by considering the user's geographical location information when a reservation is made. For example, the reservation department may prioritize reservations for restaurants related to the user's current location. The reservation department may also prioritize reservations for nearby activities based on the user's location information. The reservation department may also suggest the optimal reservation method according to the user's geographical location. For example, the reservation department may prioritize reservations for restaurants related to the user's current location. The reservation department may prioritize reservations for nearby activities based on the user's location information. The reservation department may suggest the optimal reservation method according to the user's geographical location. In this way, the optimal reservation method can be selected by considering the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department may input the user's geographical location information into AI and have the AI ​​perform the selection of the reservation method.

[0055] The reservation department can analyze the user's social media activity and suggest reservation options when a reservation is made. For example, the reservation department can suggest reservations for restaurants the user has shown interest in on social media. The reservation department can also suggest reservations for relevant activities based on the user's social media posts. The reservation department can also suggest reservations for events that the user's social media followers are attending. For example, the reservation department can suggest reservations for restaurants the user has shown interest in on social media. The reservation department can suggest reservations for relevant activities based on the user's social media posts. The reservation department can suggest reservations for events that the user's social media followers are attending. In this way, by analyzing the user's social media activity, the optimal reservation method can be suggested. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's social media activity into AI and have the AI ​​suggest reservation options.

[0056] The reservation unit can make reservations based on the user's schedule by referring to the user's calendar information. For example, the reservation unit can refer to the schedule registered in the user's calendar and suggest the most suitable reservation. The reservation unit can also suggest reservations related to specific events based on the user's calendar information. The reservation unit can also suggest the most suitable reservation method based on the user's schedule based on the user's calendar information. For example, the reservation unit can refer to the schedule registered in the user's calendar and suggest the most suitable reservation. The reservation unit can suggest reservations related to specific events based on the user's calendar information. The reservation unit can suggest the most suitable reservation method based on the user's schedule based on the user's calendar information. This makes it possible to make optimal reservations based on the schedule by referring to the user's calendar information. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's calendar information into AI and have the AI ​​make reservation suggestions.

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

[0058] The data collection unit can analyze the user's past behavioral data and select the optimal information collection method. For example, it can prioritize collecting information sources that the user has preferred to use in the past. It can also exclude information sources that the user has avoided in the past. Based on the user's behavioral patterns, it can also determine the optimal timing for information collection. In this way, by analyzing the user's past behavioral data, the optimal information collection method can be selected.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information and a concise analysis on less important information. It can also determine the priority of the analysis according to its importance. By adjusting the level of detail of the analysis based on the importance of the collected information, it is possible to provide more appropriate analysis results.

[0060] The proposal team can adjust the level of detail in their proposals based on the importance of the experience. For example, they can provide detailed proposals for high-importance experiences and concise proposals for lower-importance experiences. They can also prioritize proposals according to their importance. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the experience.

[0061] The reservation system can analyze a user's past reservation history to select the most suitable reservation method. For example, it can prioritize suggesting reservation methods the user has used in the past. It can also select the most suitable reservation method based on the user's past reservation history. It can also suggest the most suitable reservation method based on the user's reservation history. In this way, the system can select the most suitable reservation method by analyzing the user's past reservation history.

[0062] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, it can prioritize collecting event information related to the user's current location. It can also prioritize collecting information on nearby restaurants based on the user's location. It can also collect information on the most suitable tourist spots according to the user's geographical location. In this way, by considering the user's geographical location, it can provide highly relevant information.

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

[0064] Step 1: The collection unit collects user information. User information includes personal information, behavioral data, and preference information. For example, the collection unit collects the user's past behavioral data, preference information, real-time mood, purchase history, browsing history, and survey results. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit uses data mining techniques to analyze users' interests and hobbies, statistical analysis techniques to analyze users' behavioral patterns, and machine learning techniques to predict users' preferences. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. These proposals may include restaurant recommendations, travel plan suggestions, and event suggestions. For example, the proposal department will suggest the most suitable restaurant, travel plan, and event for the user based on the analysis results. Step 4: The Reservations Department makes reservations and schedules based on the proposals made by the Proposal Department. Reservations and scheduling are done through methods such as online reservations and calendar integration. For example, the Reservations Department makes online reservations for proposed restaurants, integrates proposed activities into the calendar, and makes online reservations for proposed events.

[0065] (Example of form 2) The experience suggestion system according to an embodiment of the present invention is a system in which an AI agent autonomously suggests and adjusts experiences tailored to the user's preferences. This experience suggestion system considers the user's interests, hobbies, past activities, and real-time mood to customize and suggest experiences such as travel, events, and dining. For example, the experience suggestion system provides the user with a stress-free experience by having the AI ​​fine-tune the plan in real time based on user feedback and automatically making restaurant reservations and scheduling activities. This experience suggestion system suggests the optimal experience based on the user's past behavioral data and preference information. Furthermore, because the experience suggestion system makes adjustments and new suggestions in real time according to preferences during the activity, it can flexibly respond to schedule changes. The experience suggestion system maximizes user satisfaction by autonomously making reservations and cancellations and providing the optimal experience without stress. For example, the user inputs "I want to go from XX to XX" into the experience suggestion system. In this case, the user only needs to input the departure point and destination. For example, the user inputs "I want to go from my home to the station." This information is input into the generating AI. Next, the generating AI analyzes the input information and creates a video showing how to get from the current location to the destination. The AI ​​generates a video that follows a route calculated based on map data. For example, if a user enters a route from their home to the train station, a video following that route will be generated. The generated video starts navigating according to the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video will also start navigating north. This allows the user to receive navigation that is aligned with their direction of travel. Furthermore, the video screen moves in accordance with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it enjoyable for everyone. Users can receive intuitive navigation without having to perform complex operations.Furthermore, since the viewpoint of the smartphone becomes the axis of all directions, there is no confusion, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with the smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. As a result, the experience suggestion system can autonomously suggest and adjust experiences to match the user's preferences, providing a stress-free experience.

[0066] The experience suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a reservation unit. The collection unit collects user information. User information includes, but is not limited to, personal information, behavioral data, and preference information. The collection unit collects, for example, the user's past behavioral data. The collection unit can also collect the user's preference information. Furthermore, the collection unit can collect the user's mood in real time. For example, the collection unit collects the user's past purchase history. The collection unit can also collect the user's browsing history. The collection unit can also collect the user's survey results. The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, and machine learning, but is not limited to these methods. The analysis unit analyzes the user's interests and hobbies using, for example, data mining techniques. The analysis unit can also analyze the user's behavioral patterns using statistical analysis techniques. The analysis unit can also analyze the user's preferences using machine learning techniques. For example, the analysis unit uses data mining techniques to extract the user's interests and hobbies from their past behavioral data. The analysis unit uses statistical analysis techniques to analyze the user's behavioral patterns. The analysis unit uses machine learning techniques to predict the user's preferences. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. Suggestions may include, for example, restaurant recommendations or travel plan suggestions, but are not limited to these examples. For example, the suggestion unit may suggest the best restaurant for the user based on the analysis results. The suggestion unit may also suggest the best travel plan for the user based on the analysis results. The suggestion unit may also suggest the best event for the user based on the analysis results. For example, the suggestion unit may suggest the best restaurant for the user based on the analysis results. The suggestion unit may suggest the best travel plan for the user based on the analysis results. The suggestion unit may also suggest the best event for the user based on the analysis results. The reservation unit makes reservations and schedules based on the suggestions made by the suggestion unit. Reservations and scheduling may include, for example, online reservations or calendar integration, but are not limited to these examples. The reservation unit may, for example, make reservations for the suggested restaurants.The reservation unit can also schedule the suggested activities. The reservation unit can also make reservations for the suggested events. For example, the reservation unit can make online reservations for the suggested restaurants. The reservation unit can link the suggested activities to the calendar. The reservation unit can make online reservations for the suggested events. In this way, the experience suggestion system according to the embodiment can provide the user with the optimal experience by collecting, analyzing, suggesting, and making reservations and scheduling user information. Some or all of the above-described processes in the collection unit, analysis unit, suggestion unit, and reservation unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user information into the AI ​​and have the AI ​​perform information collection. The analysis unit can input the collected information into the AI ​​and have the AI ​​perform information analysis. The suggestion unit can input the analysis results into the AI ​​and have the AI ​​perform suggestion generation. The reservation unit can input the suggested content into the AI ​​and have the AI ​​perform reservations and scheduling.

[0067] The data collection unit collects user information. This information includes, but is not limited to, personal information, behavioral data, and preference information. For example, the unit collects users' past behavioral data. It can also collect user preference information. Furthermore, it can collect user mood data in real time. For example, the unit collects users' past purchase history. It can also collect users' browsing history. It can also collect user survey results. The data collection unit utilizes various devices and platforms to collect this information. For example, it can obtain location information and health data from users' smartphones and wearable devices. Furthermore, it can collect access logs and clickstream data from websites and applications used by users. This allows the data collection unit to obtain detailed data on user behavior and preferences, enabling it to accurately understand user needs and preferences. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning, but is not limited to these examples. For example, the analysis unit can use data mining techniques to analyze users' interests and hobbies. It can also use statistical analysis techniques to analyze users' behavioral patterns. Furthermore, it can use machine learning techniques to analyze users' preferences. For instance, the analysis unit can use data mining techniques to extract interests and hobbies from users' past behavioral data. It can use statistical analysis techniques to analyze users' behavioral patterns. It can use machine learning techniques to predict users' preferences. The analysis unit can combine these techniques to analyze user behavior and preferences from multiple perspectives. For example, by using data mining techniques to extract users' interests and hobbies, statistical analysis techniques to analyze behavioral patterns, and machine learning techniques to predict preferences, it can provide optimal suggestions to meet user needs. In addition, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to accurately understand user behavior and preferences, providing a foundation for making optimal suggestions.

[0069] The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. These suggestions may include, but are not limited to, restaurant recommendations or travel plan suggestions. For example, the suggestion unit can suggest the most suitable restaurant for the user based on the analysis results. It can also suggest the most suitable travel plan for the user based on the analysis results. It can also suggest the most suitable event for the user based on the analysis results. To make these suggestions, the suggestion unit can provide personalized suggestions based on the user's interests and preferences. For example, it can suggest new restaurants or events that match the user's preferences based on data from restaurants the user has visited or events they have attended in the past. Furthermore, the suggestion unit can also make suggestions that match the user's current situation and mood. For example, by inputting the user's current mood, it can suggest restaurants or events that suit that mood. This allows the suggestion unit to provide suggestions that best meet the user's needs and improve user satisfaction.

[0070] The Reservations Department makes reservations and schedules based on the proposals made by the Proposal Department. Reservations and scheduling are made through methods such as online reservations and calendar integration, but are not limited to these examples. For example, the Reservations Department can make reservations for suggested restaurants, schedule suggested activities, and make reservations for suggested events. For instance, the Reservations Department can make online reservations for suggested restaurants, integrate suggested activities into its calendar, and make online reservations for suggested events. The Reservations Department can integrate with various reservation systems and calendar applications to make these reservations and schedules. For example, it can integrate with restaurant reservation systems to allow users to easily make reservations for suggested restaurants, or integrate with calendar applications to automatically add suggested activities and events to users' schedules. This allows the Reservations Department to improve user convenience and smoothly realize the suggested experiences. Furthermore, the Reservations Department can collect user feedback and continuously improve the accuracy and effectiveness of reservations and scheduling. For example, based on user feedback, it can review its integration methods with reservation systems and calendar applications to provide a more user-friendly system. This allows the reservation department to provide users with quick and reliable reservation and scheduling services, thereby improving user satisfaction.

[0071] The proposal unit can fine-tune the plan in real time based on user feedback. For example, the proposal unit can collect user feedback and fine-tune the plan in real time. The proposal unit can also modify the proposal based on user feedback. The proposal unit can also add to the proposal based on user feedback. For example, the proposal unit collects user feedback and fine-tunes the plan in real time. The proposal unit modifies the proposal based on user feedback. The proposal unit adds to the proposal based on user feedback. This allows for improved user satisfaction by fine-tuning the plan in real time based on user feedback. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user feedback into AI and have AI perform the plan fine-tuning.

[0072] The reservation department can automatically make restaurant reservations and schedule activities. For example, the reservation department can automatically make reservations for suggested restaurants. The reservation department can also automatically schedule suggested activities. The reservation department can also automatically make reservations for suggested events. For example, the reservation department can automatically make online reservations for suggested restaurants. The reservation department can automatically link suggested activities to the calendar. The reservation department can automatically make online reservations for suggested events. This reduces the effort required from users by automating restaurant reservations and activity scheduling. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input suggested restaurant reservations into AI and have the AI ​​execute the reservations.

[0073] The data collection unit can collect data on the user's past behavior and preferences. For example, the data collection unit can collect the user's past purchase history. The data collection unit can also collect the user's browsing history. The data collection unit can also collect the user's survey results. For example, the data collection unit can collect the user's past purchase history. The data collection unit can collect the user's browsing history. The data collection unit can collect the user's survey results. By collecting data on the user's past behavior and preferences, it becomes possible to make more appropriate suggestions. 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 behavior data into AI and have AI perform the data collection.

[0074] The analysis unit can analyze users' interests and hobbies based on collected information. For example, the analysis unit can use data mining techniques to analyze users' interests and hobbies. The analysis unit can also use statistical analysis techniques to analyze users' behavioral patterns. The analysis unit can also use machine learning techniques to analyze users' preferences. For example, the analysis unit can use data mining techniques to extract interests and hobbies from users' past behavioral data. The analysis unit can use statistical analysis techniques to analyze users' behavioral patterns. The analysis unit can use machine learning techniques to predict users' preferences. As a result, by analyzing users' interests and hobbies based on collected information, it becomes possible to make suggestions that are tailored to the user. 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 collected information into AI and have the AI ​​perform the analysis of the information.

[0075] The suggestion unit can propose the optimal experience based on the analysis results. For example, the suggestion unit can suggest the best restaurant for the user based on the analysis results. The suggestion unit can also suggest the best travel plan for the user based on the analysis results. The suggestion unit can also suggest the best event for the user based on the analysis results. For example, the suggestion unit can suggest the best restaurant for the user based on the analysis results. The suggestion unit can suggest the best travel plan for the user based on the analysis results. The suggestion unit can suggest the best event for the user based on the analysis results. By proposing the optimal experience based on the analysis results, user satisfaction can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into AI and have the AI ​​generate suggestions.

[0076] The reservation department can autonomously make and cancel reservations. For example, the reservation department can autonomously make reservations for suggested restaurants. The reservation department can also autonomously schedule suggested activities. The reservation department can also autonomously make reservations for suggested events. For example, the reservation department can autonomously make online reservations for suggested restaurants. The reservation department can autonomously link suggested activities to the calendar. The reservation department can autonomously make online reservations for suggested events. This reduces the effort required from the user by allowing reservations and cancellations to be made autonomously. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input the reservation of a suggested restaurant into AI and have the AI ​​execute the reservation.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during a time when the user can relax. If the user is excited, the data collection unit can start collecting information immediately and provide suggestions in real time. If the user is tired, the data collection unit can collect information after the user has rested. For example, if the user is stressed, the data collection unit will collect information during a time when the user can relax. If the user is excited, the data collection unit will start collecting information immediately and provide suggestions in real time. If the user is tired, the data collection unit will collect information after the user has rested. By adjusting the timing of information collection based on the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of information collection.

[0078] The data collection unit can analyze the user's past behavioral data and select the optimal information collection method. For example, the data collection unit can prioritize collecting information sources that the user has preferred to use in the past. The data collection unit can also exclude information sources that the user has avoided in the past. The data collection unit can also determine the optimal timing for information collection based on the user's behavioral patterns. For example, the data collection unit can prioritize collecting information sources that the user has preferred to use in the past. The data collection unit can exclude information sources that the user has avoided in the past. The data collection unit can determine the optimal timing for information collection based on the user's behavioral patterns. This allows the optimal information collection method to be selected by analyzing the user's past behavioral data. 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 behavioral data into AI and have AI select the information collection method.

[0079] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to areas the user is currently interested in. The data collection unit can also filter and provide appropriate information according to the user's lifestyle. The data collection unit can also collect highly relevant information based on the user's areas of interest. For example, the data collection unit can prioritize collecting information related to areas the user is currently interested in. The data collection unit can filter and provide appropriate information according to the user's lifestyle. The data collection unit collects highly relevant information based on the user's areas of interest. This allows for the provision of more relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into AI and have the AI ​​perform the information filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. If the user is in a hurry, the data collection unit may prioritize collecting concise information. If the user is excited, the data collection unit may prioritize collecting the latest information. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. If the user is in a hurry, the data collection unit may prioritize collecting concise information. If the user is excited, the data collection unit may prioritize collecting the latest information. This allows for the provision of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the information.

[0081] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize collecting event information related to the user's current location. The data collection unit can also prioritize collecting information about nearby restaurants based on the user's location. The data collection unit can also collect information about the most suitable tourist spots according to the user's geographical location. For example, the data collection unit can prioritize collecting event information related to the user's current location. The data collection unit can prioritize collecting information about nearby restaurants based on the user's location. The data collection unit can collect information about the most suitable tourist spots according to the user's geographical location. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the information collection.

[0082] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect event information that the user has shown interest in on social media. The data collection unit can also collect relevant restaurant information based on the user's social media posts. The data collection unit can also collect event information that the user's social media followers are participating in. For example, the data collection unit can collect event information that the user has shown interest in on social media. The data collection unit can collect relevant restaurant information based on the user's social media posts. The data collection unit can collect event information that the user's social media followers are participating in. This allows the system to provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have AI perform the data collection.

[0083] 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 can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can also provide visually appealing analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can provide visually appealing analysis results. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described 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 the AI ​​and have the AI ​​adjust the way the analysis is presented.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can perform a concise analysis on information of low importance. The analysis unit can determine the priority of the analysis according to its importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a travel-specific analysis algorithm to travel information. The analysis unit can also apply a restaurant-specific analysis algorithm to restaurant information. The analysis unit can also apply an event-specific analysis algorithm to event information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually appealing analysis result. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described 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 the AI ​​and have the AI ​​adjust the length of the analysis.

[0087] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. The analysis unit can also determine the priority of analysis according to the timing of information collection. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit analyzes older information as needed. The analysis unit determines the priority of analysis according to the timing of information collection. By determining the priority of analysis based on the timing of information collection, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information collection into the AI ​​and have the AI ​​determine the priority of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may postpone the analysis of less relevant information. The analysis unit adjusts the order of analysis according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0089] 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 in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide visually appealing suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way it presents suggestions based on the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 processing described above in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into the AI ​​and have the AI ​​adjust the way the proposal is expressed.

[0090] The proposal unit can adjust the level of detail of its proposals based on the importance of the experience. For example, the proposal unit can provide detailed proposals for high-importance experiences, and concise proposals for low-importance experiences. The proposal unit can also determine the priority of proposals according to their importance. For example, the proposal unit can provide detailed proposals for high-importance experiences, and concise proposals for low-importance experiences, and determine the priority of proposals according to their importance. This allows for more appropriate proposals by adjusting the level of detail of proposals based on the importance of the experience. 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 the importance of the experience into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0091] The suggestion unit can apply different suggestion algorithms depending on the category of the experience when making suggestions. For example, the suggestion unit can apply a travel-specific suggestion algorithm to travel experiences. The suggestion unit can also apply a restaurant-specific suggestion algorithm to restaurant experiences. The suggestion unit can also apply an event-specific suggestion algorithm to event experiences. For example, the suggestion unit can apply a travel-specific suggestion algorithm to travel experiences. The suggestion unit can apply a restaurant-specific suggestion algorithm to restaurant experiences. The suggestion unit can apply an event-specific suggestion algorithm to event experiences. By applying different suggestion algorithms depending on the category of the experience, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the experience into the AI ​​and have the AI ​​perform the application of the suggestion algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make detailed suggestions. If the user is excited, the suggestion unit can also make visually appealing suggestions. For example, if the user is in a hurry, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit will make detailed suggestions. If the user is excited, the suggestion unit will make visually appealing suggestions. This allows for more appropriate suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, or not using AI. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the length of the suggestions.

[0093] The proposal department can determine the priority of proposals based on the timing of the experience. For example, the proposal department may prioritize proposals for the most recent experience. The proposal department may also make proposals for experiences in the distant future as needed. The proposal department can also determine the priority of proposals according to the timing of the experience. For example, the proposal department may prioritize proposals for the most recent experience. The proposal department may also make proposals for experiences in the distant future as needed. The proposal department determines the priority of proposals according to the timing of the experience. This allows for more appropriate proposals by determining the priority of proposals based on the timing of the experience. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of the experience into the AI ​​and have the AI ​​determine the priority of proposals.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of experiences when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant experiences. The suggestion unit may also postpone suggesting less relevant experiences. The suggestion unit can also adjust the order of suggestions according to the relevance of experiences. For example, the suggestion unit may prioritize suggesting highly relevant experiences. The suggestion unit may postpone suggesting less relevant experiences. The suggestion unit adjusts the order of suggestions according to the relevance of experiences. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of experiences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of experiences into AI and have AI perform the adjustment of the suggestion order.

[0095] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated emotions. For example, if the user is relaxed, the reservation unit can provide detailed reservation instructions. If the user is in a hurry, the reservation unit can also provide concise reservation instructions. If the user is excited, the reservation unit can also provide visually appealing reservation instructions. For example, if the user is relaxed, the reservation unit can provide detailed reservation instructions. If the user is in a hurry, the reservation unit can provide concise reservation instructions. If the user is excited, the reservation unit can provide visually appealing reservation instructions. This allows for more appropriate reservations by adjusting the reservation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user emotion data into AI and have the AI ​​adjust the reservation method.

[0096] The reservation unit can analyze the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation unit may prioritize suggesting reservation methods the user has used in the past. The reservation unit can also select the optimal reservation method from the user's past reservation history. The reservation unit can also suggest the optimal reservation method based on the user's reservation history. For example, the reservation unit may prioritize suggesting reservation methods the user has used in the past. The reservation unit may select the optimal reservation method from the user's past reservation history. The reservation unit may suggest the optimal reservation method based on the user's reservation history. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit may input the user's past reservation history into AI and have AI perform the selection of a reservation method.

[0097] The reservation unit can customize the reservation process based on the user's current lifestyle. For example, if the user is busy, the reservation unit can provide a simple reservation method. If the user is relaxed, the reservation unit can also provide a detailed reservation method. The reservation unit can also suggest the most suitable reservation method according to the user's lifestyle. For example, if the user is busy, the reservation unit can provide a simple reservation method. If the user is relaxed, the reservation unit can provide a detailed reservation method. The reservation unit can suggest the most suitable reservation method according to the user's lifestyle. This allows for more appropriate reservations by customizing the reservation method based on the user's current lifestyle. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's lifestyle into AI and have AI perform the customization of the reservation method.

[0098] The reservation unit can estimate the user's emotions and determine reservation priorities based on those emotions. For example, if the user is relaxed, the reservation unit can provide detailed reservation instructions. If the user is in a hurry, the reservation unit can also provide concise reservation instructions. If the user is excited, the reservation unit can also provide visually appealing reservation instructions. For example, if the user is relaxed, the reservation unit can provide detailed reservation instructions. If the user is in a hurry, the reservation unit can provide concise reservation instructions. If the user is excited, the reservation unit can provide visually appealing reservation instructions. This allows for more appropriate reservations by determining reservation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user emotion data into an AI and have the AI ​​determine reservation priorities.

[0099] The reservation department can select the optimal reservation method by considering the user's geographical location information when a reservation is made. For example, the reservation department may prioritize reservations for restaurants related to the user's current location. The reservation department may also prioritize reservations for nearby activities based on the user's location information. The reservation department may also suggest the optimal reservation method according to the user's geographical location. For example, the reservation department may prioritize reservations for restaurants related to the user's current location. The reservation department may prioritize reservations for nearby activities based on the user's location information. The reservation department may suggest the optimal reservation method according to the user's geographical location. In this way, the optimal reservation method can be selected by considering the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department may input the user's geographical location information into AI and have the AI ​​perform the selection of the reservation method.

[0100] The reservation department can analyze the user's social media activity and suggest reservation options when a reservation is made. For example, the reservation department can suggest reservations for restaurants the user has shown interest in on social media. The reservation department can also suggest reservations for relevant activities based on the user's social media posts. The reservation department can also suggest reservations for events that the user's social media followers are attending. For example, the reservation department can suggest reservations for restaurants the user has shown interest in on social media. The reservation department can suggest reservations for relevant activities based on the user's social media posts. The reservation department can suggest reservations for events that the user's social media followers are attending. In this way, by analyzing the user's social media activity, the optimal reservation method can be suggested. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's social media activity into AI and have the AI ​​suggest reservation options.

[0101] The reservation unit can make reservations based on the user's schedule by referring to the user's calendar information. For example, the reservation unit can refer to the schedule registered in the user's calendar and suggest the most suitable reservation. The reservation unit can also suggest reservations related to specific events based on the user's calendar information. The reservation unit can also suggest the most suitable reservation method based on the user's schedule based on the user's calendar information. For example, the reservation unit can refer to the schedule registered in the user's calendar and suggest the most suitable reservation. The reservation unit can suggest reservations related to specific events based on the user's calendar information. The reservation unit can suggest the most suitable reservation method based on the user's schedule based on the user's calendar information. This makes it possible to make optimal reservations based on the schedule by referring to the user's calendar information. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's calendar information into AI and have the AI ​​make reservation suggestions.

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

[0103] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those estimates. For example, if the user is stressed, suggestions can be made during a time when they can relax. If the user is excited, suggestions can be made immediately, providing real-time support. If the user is tired, suggestions can be made after they have rested. By adjusting the timing of suggestions based on the user's emotions, more appropriate suggestions can be made.

[0104] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is relaxed, it can prioritize detailed analysis. If the user is in a hurry, it can prioritize concise analysis. If the user is excited, it can prioritize analyzing the latest information. By prioritizing analysis based on the user's emotions, it can provide more appropriate analysis results.

[0105] The reservation system can estimate the user's emotions and adjust the timing of reservations based on those emotions. For example, if the user is relaxed, it can provide detailed reservation instructions. If the user is in a hurry, it can provide concise instructions. If the user is excited, it can provide visually appealing instructions. This allows for more appropriate reservations by adjusting the timing of reservations based on the user's emotions.

[0106] The information gathering unit can estimate the user's emotions and adjust its information gathering method based on those emotions. For example, if the user is relaxed, it can gather detailed information. If the user is in a hurry, it can gather concise information. If the user is excited, it can gather up-to-date information. By adjusting the information gathering method based on the user's emotions, it can provide more relevant information.

[0107] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. If the user is excited, it can provide visually appealing suggestions. By adjusting the content of suggestions based on the user's emotions, it becomes possible to provide more appropriate suggestions.

[0108] The data collection unit can analyze the user's past behavioral data and select the optimal information collection method. For example, it can prioritize collecting information sources that the user has preferred to use in the past. It can also exclude information sources that the user has avoided in the past. Based on the user's behavioral patterns, it can also determine the optimal timing for information collection. In this way, by analyzing the user's past behavioral data, the optimal information collection method can be selected.

[0109] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information and a concise analysis on less important information. It can also determine the priority of the analysis according to its importance. By adjusting the level of detail of the analysis based on the importance of the collected information, it is possible to provide more appropriate analysis results.

[0110] The proposal team can adjust the level of detail in their proposals based on the importance of the experience. For example, they can provide detailed proposals for high-importance experiences and concise proposals for lower-importance experiences. They can also prioritize proposals according to their importance. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the experience.

[0111] The reservation system can analyze a user's past reservation history to select the most suitable reservation method. For example, it can prioritize suggesting reservation methods the user has used in the past. It can also select the most suitable reservation method based on the user's past reservation history. It can also suggest the most suitable reservation method based on the user's reservation history. In this way, the system can select the most suitable reservation method by analyzing the user's past reservation history.

[0112] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, it can prioritize collecting event information related to the user's current location. It can also prioritize collecting information on nearby restaurants based on the user's location. It can also collect information on the most suitable tourist spots according to the user's geographical location. In this way, by considering the user's geographical location, it can provide highly relevant information.

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

[0114] Step 1: The collection unit collects user information. User information includes personal information, behavioral data, and preference information. For example, the collection unit collects the user's past behavioral data, preference information, real-time mood, purchase history, browsing history, and survey results. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit uses data mining techniques to analyze users' interests and hobbies, statistical analysis techniques to analyze users' behavioral patterns, and machine learning techniques to predict users' preferences. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. These proposals may include restaurant recommendations, travel plan suggestions, and event suggestions. For example, the proposal department will suggest the most suitable restaurant, travel plan, and event for the user based on the analysis results. Step 4: The Reservations Department makes reservations and schedules based on the proposals made by the Proposal Department. Reservations and scheduling are done through methods such as online reservations and calendar integration. For example, the Reservations Department makes online reservations for proposed restaurants, integrates proposed activities into the calendar, and makes online reservations for proposed events.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The reservation unit is implemented in the specific processing unit 46A of the smart device 14 and makes reservations and scheduling based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

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

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and makes reservations and scheduling based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The reservation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and makes reservations and scheduling based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and reservation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The reservation unit is implemented, for example, by the control unit 46A of the robot 414 and makes reservations and scheduling based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, A reservation unit that makes reservations and schedules based on the content proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, The plan is fine-tuned in real time based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reservation section is, Automate restaurant reservations and activity scheduling. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect data on the user's past behavior and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Based on the collected information, we analyze the user's interests and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the optimal experience based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reservation section is, Autonomously make and cancel reservations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the experience. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the experience. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on the timing of the experience. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the experience. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, When making a reservation, the reservation method will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When a reservation is made, the system will refer to the user's calendar information to provide suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, A reservation unit that makes reservations and schedules based on the content proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, The plan is fine-tuned in real time based on user feedback. The system according to feature 1.

3. The aforementioned reservation section is, Automate restaurant reservations and activity scheduling. The system according to feature 1.

4. The aforementioned collection unit is Collect data on the user's past behavior and preferences. The system according to feature 1.

5. The aforementioned analysis unit, Based on the collected information, we analyze the user's interests and hobbies. The system according to feature 1.

6. The aforementioned proposal section is, We propose the optimal experience based on the analysis results. The system according to feature 1.

7. The aforementioned reservation section is, Autonomously make and cancel reservations. The system according to feature 1.

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

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