system

Abstract

The system according to this embodiment aims to take into account the crowding situation at Disneyland and suggest the optimal order of attractions, meals, and events for the user. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a meal suggestion unit, and an event suggestion unit. The data collection unit collects the user's preferences and past visit history. The analysis unit analyzes the congestion status of Disneyland in real time based on the data collected by the data collection unit. The suggestion unit suggests the optimal order and time for queuing for attractions to the user based on the analysis results obtained by the analysis unit. The meal suggestion unit suggests the timing and location of meals. The event suggestion unit suggests the viewing location and time for events.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0007] The system according to this embodiment can take into account the crowd situation at Disneyland and suggest the optimal order of attractions, meals, and events for the user. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between multiple computers. As an example of the communication standard applied to the communication I / F, wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark) can be mentioned.

[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). <0​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 Disneyland suggestion system according to an embodiment of the present invention is a system that uses an AI agent to analyze the congestion situation at Disneyland in real time and suggests to the user the optimal order and time to queue for attractions, the timing and location of meals, and the location and time to watch events. When a user arrives at Disneyland, the AI ​​agent analyzes the congestion situation within Disneyland in real time. At this time, the AI ​​agent collects data such as the waiting time and congestion level of each attraction, the user's preferences, and past visit history. Next, based on the collected data, the AI ​​agent suggests to the user the optimal order and time to queue for attractions. Furthermore, the AI ​​agent also suggests the timing and location of meals for the user. Finally, the AI ​​agent also suggests to the user the optimal location and time to watch events held within Disneyland. For example, when a user arrives at Disneyland, the Disneyland suggestion system uses an AI agent to analyze the congestion situation within Disneyland in real time. The AI ​​agent collects data such as the waiting time and congestion level of each attraction, the user's preferences, and past visit history. Next, based on the collected data, the AI ​​agent suggests to the user the optimal order and time to queue for attractions. Furthermore, the AI ​​agent also suggests the timing and location of meals for the user. Finally, the AI ​​agent also suggests the best viewing locations and times for events held within Disneyland. In this way, the Disneyland suggestion system can suggest the optimal order and timing for queuing for attractions, the timing and location of meals, and the viewing locations and times for events, depending on the crowd situation at Disneyland. This allows users to enjoy Disneyland more efficiently and improves their satisfaction. In this way, the Disneyland suggestion system can analyze the crowd situation at Disneyland in real time based on the user's preferences and past visit history, and suggest the optimal order and timing for queuing for attractions, the timing and location of meals, and the viewing locations and times for events.

[0029] The Disneyland suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a meal suggestion unit, and an event suggestion unit. The collection unit collects user preferences and past visit history. For example, the collection unit can collect data such as the user's food preferences and attraction preferences. The collection unit can also collect the user's past visit history. For example, the collection unit can collect data such as the date and time of the user's visit, the number of visits, and the user's behavior during the visit. The analysis unit analyzes the congestion status of Disneyland in real time based on the data collected by the collection unit. For example, the analysis unit can analyze the waiting time and congestion level of each attraction in real time. The analysis unit can also predict the density of people and peak congestion times within Disneyland. The suggestion unit suggests the optimal order and time for queuing for attractions to the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest the order and time for queuing for attractions based on congestion status and user preferences. The suggestion unit can also suggest the optimal order of attractions based on the user's past visit history. The meal suggestion unit proposes the timing and location of meals. For example, the meal suggestion unit can propose the timing and location of meals based on factors such as crowd levels and user preferences. It can also propose the optimal menu based on the user's past dining history. The event suggestion unit proposes the location and time of events to watch. For example, the event suggestion unit can propose the location and time of events to watch based on factors such as crowd levels and user preferences. It can also propose the optimal event based on the user's past event participation history. As a result, the Disneyland suggestion system according to this embodiment can analyze the crowd levels of Disneyland in real time based on the user's preferences and past visit history, and propose the optimal order and time for queuing for attractions, the timing and location of meals, and the location and time for watching events.

[0030] The data collection unit collects user preferences and past visit history. Specifically, it collects detailed data such as the attractions and restaurants used by users within Disneyland, and the merchandise purchased. For example, it collects information such as which attractions users prefer, which restaurants they eat at, and what kind of merchandise they buy. It also collects the date and time of user visits, the number of visits, and their behavioral patterns during visits. This allows the data collection unit to understand user preferences and behavioral patterns in detail. Furthermore, the data collection unit also collects data from smartphone apps and wearable devices used by users within Disneyland. For example, through smartphone apps, it can collect real-time information such as the user's location, attraction usage history, and restaurant reservation status. From wearable devices, it can collect health data such as the user's heart rate, steps taken, and distance traveled. This allows the data collection unit to understand not only user preferences and behavioral patterns, but also their health status and fatigue level. The collected data is stored on a cloud server and made accessible to the analysis and proposal units. This allows the data collection unit to efficiently collect detailed user data and improve the overall system performance.

[0031] The analysis department analyzes the crowd situation at Disneyland in real time based on data collected by the data collection department. Specifically, it analyzes the wait times and crowd levels of each attraction in real time and predicts the crowd density and peak times of congestion within Disneyland. For example, it collects wait time data for each attraction and uses AI to analyze patterns of wait time fluctuations. This makes it possible to predict wait times at specific times. In addition, to understand the crowd density within Disneyland, it collects data from sensors and cameras installed in each area and uses AI to count the number of people. This allows for real-time understanding of the crowd level in each area. Furthermore, it can also predict peak times of congestion by utilizing past data and statistical information. For example, based on past visit data, it analyzes crowd trends at specific days of the week and times of day and predicts future peak times of congestion. This allows the analysis department to not only understand the real-time crowd situation but also predict future crowds. Furthermore, it can use anomaly detection algorithms to detect unusual crowd patterns and abnormal data and issue warnings early. This allows the analysis department to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion department proposes the optimal order and timing for queuing for attractions to users based on the analysis results obtained by the analysis department. Specifically, it proposes the order and timing for queuing for attractions based on crowd conditions and user preferences. For example, it can find times when the wait time for a user's favorite attraction is short and suggest using that attraction during those times. It can also propose the optimal order of attractions based on the user's past visit history. For example, it can analyze the order and time spent on attractions visited in the past and propose the optimal order. Furthermore, the suggestion department can also propose the optimal route based on the user's current location. For example, it can suggest the nearest attraction or restaurant from the user's current location to enable efficient travel. In this way, the suggestion department can propose the optimal order and timing for queuing for attractions based on user preferences and behavior patterns, thereby improving user satisfaction. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can check whether the suggested order and timing for attractions were actually used and use this to improve the suggestions. In this way, the suggestion department can provide users with the best possible suggestions and make their Disneyland experience more fulfilling.

[0033] The dining suggestion department proposes the timing and location of meals. Specifically, it suggests meal times and locations based on crowd levels and user preferences. For example, it can find less crowded times and less busy restaurants and suggest meals at those times and locations. It can also suggest optimal menus based on the user's past dining history. For example, it can analyze restaurants visited and menus ordered in the past to suggest menus the user would like. Furthermore, the dining suggestion department can suggest appropriate menus considering the user's health condition and allergy information. For example, it can suggest menus that avoid ingredients the user is allergic to, providing health-conscious meals. In this way, the dining suggestion department can suggest the optimal timing, location, and menu based on the user's preferences and health condition, thereby improving user satisfaction. In addition, the dining suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can check whether the suggested menus were actually ordered and use this to improve the suggestions. In this way, the dining suggestion department can provide users with optimal meal suggestions and make their dining experience at Disneyland even more fulfilling.

[0034] The Event Proposal Department suggests event viewing locations and times. Specifically, they propose viewing locations and times based on crowd conditions and user preferences. For example, they identify less crowded times and less crowded viewing locations and suggest viewing events at those times and locations. They can also suggest the most suitable events based on the user's past event participation history. For example, they analyze past events attended and viewing locations to suggest events the user would like. Furthermore, the Event Proposal Department can suggest the most suitable viewing locations based on the user's current location. For example, they suggest the viewing location closest to the user's current location to allow for efficient travel. In this way, the Event Proposal Department can suggest the most suitable event viewing locations and times based on user preferences and behavioral patterns, thereby improving user satisfaction. In addition, the Event Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, they can check whether the suggested events and viewing locations were actually used and use this information to improve the suggestions. In this way, the Event Proposal Department can provide users with the most suitable event suggestions and enhance their event experience at Disneyland.

[0035] The reminder function provides reminders. For example, the reminder function can notify users of suggested schedules. The reminder function provides timely reminders so that users do not forget to follow the suggested schedule. For example, the reminder function can notify users when the wait time for an attraction will be shorter. It can also notify users of meal times so that they do not miss meal times. Furthermore, the reminder function can notify users of event schedules so that they do not forget the location and time of the event. In this way, the reminder function supports users in remembering to follow the suggested schedule.

[0036] The restaurant recommendation department provides menus tailored to user preferences. For example, it can suggest the optimal menu based on a user's eating preferences. The restaurant recommendation department analyzes users' past dining history and preference data to suggest the most suitable menu. For instance, it can suggest the optimal menu based on dishes users have enjoyed eating in the past. It can also suggest the optimal menu based on dishes users have avoided in the past. Furthermore, it can suggest the optimal menu based on data from restaurants users have visited in the past. In this way, the restaurant recommendation department can improve dining satisfaction by providing menus tailored to users' preferences.

[0037] The data collection unit can analyze a user's past visit history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on attractions the user has visited in the past. It can also prioritize collecting data on restaurants the user has used in the past. Furthermore, it can prioritize collecting data on events the user has participated in in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past visit history.

[0038] The data collection unit can collect user behavior patterns within specific areas of Disneyland. For example, the unit can collect the amount of time users spend in a specific area. The unit can also collect the routes users take within a specific area. Furthermore, the unit can collect information on users' attraction usage within a specific area. By collecting user behavior patterns within specific areas, the unit can gain a detailed understanding of user behavior.

[0039] The data collection unit can analyze users' social media activity and collect relevant preference data. For example, it can analyze photos and posts that users share on social media to identify attractions and events that users are interested in. It can also analyze accounts that users follow and groups that users participate in to collect relevant preference data. Furthermore, it can analyze locations that users check in to on social media to identify areas and activities that users are interested in. In this way, the data collection unit can collect relevant preference data by analyzing users' social media activity.

[0040] The data collection unit can collect behavioral data in specific areas, taking into account the user's geographical location. For example, the data collection unit can prioritize collecting data on attractions and events in the area where the user is currently located. The data collection unit can also collect data on congestion levels and wait times in areas where the user plans to move. Furthermore, the data collection unit can analyze the user's behavioral patterns in areas they have visited in the past and collect relevant data. In this way, the data collection unit can collect behavioral data in specific areas by taking into account the user's geographical location.

[0041] The analysis department can analyze crowd levels within Disneyland in real time and predict peak times. For example, the analysis department can collect wait time data for each attraction in real time and predict peak times. The analysis department can also collect crowd level data for each area in real time and predict peak times. Furthermore, the analysis department can predict peak times for specific days of the week or time slots based on past crowd data. In this way, the analysis department can predict peak times by analyzing crowd levels in real time.

[0042] The analysis department can analyze the wait time data for each attraction in detail and identify the cause of congestion. For example, the analysis department can collect wait time data for each attraction and identify the cause of congestion. The analysis department can collect user count data for each attraction and identify the cause of congestion. In addition, the analysis department can collect operational status data for each attraction and identify the cause of congestion. In this way, the analysis department can identify the cause of congestion by analyzing the wait time data for each attraction in detail.

[0043] The analysis department can analyze crowd levels within Disneyland, taking weather data into consideration. For example, during rainy weather, the analysis department will focus on the crowd levels of indoor attractions. During sunny weather, the analysis department will focus on the crowd levels of outdoor attractions. Furthermore, the analysis department can predict crowd levels in response to changes in weather. This allows the analysis department to conduct more accurate crowd level analyses by considering weather data.

[0044] The analysis department can analyze crowd levels by taking into account the event schedule within Disneyland. For example, the analysis department can focus its analysis on crowd levels during specific events. The analysis department can predict crowd levels based on the start and end times of events. Furthermore, the analysis department can also predict crowd levels based on the popularity of events. This allows the analysis department to conduct more accurate crowd level analyses by considering the event schedule.

[0045] The suggestion function can propose the optimal order of attractions by considering the user's past visit history. For example, the suggestion function can propose the optimal order based on data of attractions the user has visited in the past. It can also propose the optimal order based on data of restaurants the user has used in the past. Furthermore, the suggestion function can propose the optimal order based on data of events the user has participated in in the past. In this way, the suggestion function can propose the optimal order of attractions by considering the user's past visit history.

[0046] The suggestion unit can propose the optimal travel route by considering the user's current location information. For example, the suggestion unit can suggest the attraction closest to the user's current location. The suggestion unit can also propose the optimal travel route by considering the congestion level of the area the user plans to visit. Furthermore, the suggestion unit can propose the optimal travel route based on the user's behavior patterns in areas they have visited in the past. In this way, the suggestion unit can propose the optimal travel route by considering the user's current location information.

[0047] The suggestion department can propose the most suitable attractions by considering the user's age and family structure. For example, if the user is with children, the suggestion department will suggest family-friendly attractions. If the user is elderly, the suggestion department can suggest attractions that take their physical abilities into consideration. Furthermore, if the user is a couple, the suggestion department can suggest romantic attractions. In this way, the suggestion department can propose the most suitable attractions by considering the user's age and family structure.

[0048] The suggestion department can propose the most suitable attraction by considering the user's health condition. For example, if the user is tired, the suggestion department can propose an attraction that takes their physical condition into account. If the user is seeking healthy exercise, the suggestion department can propose an active attraction. Furthermore, if the user is feeling unwell, the suggestion department can also suggest rest areas or restaurants. In this way, the suggestion department can propose the most suitable attraction by considering the user's health condition.

[0049] The meal suggestion system can propose the most suitable menu by considering the user's past eating history. For example, it can suggest the most suitable menu based on the menu the user has enjoyed eating in the past. It can also suggest the most suitable menu based on the menu the user has avoided in the past. Furthermore, it can suggest the most suitable menu based on data from restaurants the user has visited in the past. In this way, the meal suggestion system can propose the most suitable menu by considering the user's past eating history.

[0050] The meal suggestion department can propose the most suitable menu by taking into account the user's allergy information when suggesting meals. For example, the meal suggestion department can suggest menus that avoid ingredients the user is allergic to. The meal suggestion department can also suggest restaurants that do not contain ingredients the user is allergic to. Furthermore, the meal suggestion department can suggest safe dining locations based on the user's allergy information. In this way, the meal suggestion department can propose safe menus by taking the user's allergy information into consideration.

[0051] The meal suggestion function can propose the most suitable menu by considering the user's health condition. For example, if the user is tired, the function can suggest a highly nutritious menu. If the user is seeking a healthy meal, the function can suggest a healthy menu. Furthermore, if the user is feeling unwell, the function can suggest an easily digestible menu. In this way, the meal suggestion function can propose the most suitable menu by considering the user's health condition.

[0052] The dining recommendation department can suggest the most suitable restaurant when making dining recommendations, taking the user's budget into consideration. For example, the department can suggest restaurants that fit the user's budget. It can also suggest menus that fit the user's budget. Furthermore, it can suggest dining plans that fit the user's budget. In this way, the dining recommendation department can suggest the most suitable restaurant by considering the user's budget.

[0053] The event proposal department can suggest the most suitable event by considering the user's past event participation history. For example, it can suggest the most suitable event based on data of events the user has previously attended. It can also suggest the most suitable event based on data of events the user has previously avoided. Furthermore, it can suggest the most suitable event based on data of events the user has previously enjoyed. In this way, the event proposal department can suggest the most suitable event by considering the user's past event participation history.

[0054] The event proposal department can propose the most suitable events by considering the user's interests and preferences when proposing events. For example, the event proposal department can propose events on themes that interest the user. The event proposal department can propose events featuring characters that interest the user. Furthermore, the event proposal department can propose events involving activities that the user can enjoy. In this way, the event proposal department can propose the most suitable events by considering the user's interests and preferences.

[0055] The event proposal department can suggest the most suitable event by considering the user's age and family structure. For example, if the user is with children, the department can suggest a family-friendly event. If the user is elderly, the department can suggest an event that takes their physical abilities into consideration. Furthermore, if the user is a couple, the department can suggest a romantic event. In this way, the event proposal department can suggest the most suitable event by considering the user's age and family structure.

[0056] The event proposal department can propose the most suitable event by taking the user's schedule into consideration. For example, the event proposal department can propose an event that fits the user's schedule. The event proposal department can propose an event viewing time that fits the user's schedule. Furthermore, the event proposal department can propose an event viewing location that fits the user's schedule. In this way, the event proposal department can propose the most suitable event by taking the user's schedule into consideration.

[0057] The reminder function can provide optimal reminder content by considering the user's past behavior history when a reminder is issued. For example, the reminder function can provide optimal reminder content based on data of reminders the user has used in the past. The reminder function can provide optimal reminder content based on data of reminders the user has avoided in the past. Furthermore, the reminder function can provide optimal reminder content based on data of reminders the user has preferred in the past. In this way, the reminder function can provide optimal reminder content by considering the user's past behavior history.

[0058] The reminder function can provide optimal reminder content by considering the user's current location information when a reminder is issued. For example, the reminder function can provide optimal reminder content based on information about the area the user is currently in. The reminder function can provide optimal reminder content based on information about the area the user plans to move to. Furthermore, the reminder function can provide optimal reminder content based on information about areas the user has visited in the past. In this way, the reminder function can provide optimal reminder content by considering the user's current location information.

[0059] The restaurant recommendation department can suggest the most suitable restaurant by considering the user's past dining history. For example, it can suggest the most suitable restaurant based on data of restaurants the user has visited in the past. It can also suggest the most suitable restaurant based on data of restaurants the user has avoided in the past. Furthermore, it can suggest the most suitable restaurant based on data of restaurants the user has enjoyed in the past. In this way, the restaurant recommendation department can suggest the most suitable restaurant by considering the user's past dining history.

[0060] The restaurant recommendation department can suggest the most suitable restaurant, taking the user's budget into consideration. For example, the department can suggest restaurants that fit the user's budget, menus that fit the budget, and meal plans that fit the budget. In this way, the restaurant recommendation department can suggest the most suitable restaurant by considering the user's budget.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze users' social media activity and collect relevant preference data. For example, it can analyze photos and posts shared by users on social media to identify attractions and events of interest. The data collection unit can analyze accounts followed and groups joined by users to collect relevant preference data. It can also analyze locations checked in by users on social media to identify areas and activities of interest. In this way, the data collection unit can collect relevant preference data by analyzing users' social media activity.

[0063] The data collection unit can collect user behavior patterns in specific areas within Disneyland. For example, it can collect the amount of time users spend in a specific area. It can also collect the routes users take within a specific area. Furthermore, it can collect information on users' attraction usage within a specific area. By collecting user behavior patterns in specific areas, the data collection unit can gain a detailed understanding of user behavior.

[0064] The data collection unit can collect behavioral data in specific areas, taking into account the user's geographical location. For example, the unit can prioritize collecting data on attractions and events in the area where the user is currently located. It can also collect data on congestion levels and wait times in areas the user plans to visit. Furthermore, the unit can analyze behavioral patterns in areas the user has visited in the past and collect relevant data. In this way, the data collection unit can collect behavioral data in specific areas by considering the user's geographical location.

[0065] The analysis department can analyze crowd levels within Disneyland by taking weather data into consideration. For example, during rainy weather, the analysis department can focus on analyzing crowd levels at indoor attractions. During sunny weather, the analysis department can focus on analyzing crowd levels at outdoor attractions. Furthermore, the analysis department can predict crowd levels in response to changes in weather. This allows the analysis department to conduct more accurate crowd level analyses by considering weather data.

[0066] The suggestion department can propose the most suitable attractions by considering the user's age and family structure. For example, if the user is with children, the suggestion department can suggest family-friendly attractions. If the user is elderly, the suggestion department can suggest attractions that take their physical abilities into consideration. Also, if the user is a couple, the suggestion department can suggest romantic attractions. In this way, the suggestion department can propose the most suitable attractions by considering the user's age and family structure.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects user preferences and past visit history. For example, it can collect data such as the user's food preferences, attraction preferences, visit dates and times, number of visits, and behavior during visits. Step 2: The analysis unit analyzes the crowd situation at Disneyland in real time based on the data collected by the data collection unit. For example, it can predict wait times, crowd levels, crowd density, and peak hours for each attraction. Step 3: The proposal department proposes the optimal queue order and timing for attractions to the user based on the analysis results obtained by the analysis department. For example, it can propose the queue order and timing for attractions based on crowd conditions, user preferences, and past visit history. Step 4: The meal suggestion department proposes the timing and location of meals. For example, it can suggest the timing, location, and optimal menu based on factors such as crowd levels, user preferences, and past dining history. Step 5: The event proposal team suggests event viewing locations and times. For example, they can suggest optimal events and viewing locations based on factors such as crowd conditions, user preferences, and past event participation history.

[0069] (Example of form 2) The Disneyland suggestion system according to an embodiment of the present invention is a system that uses an AI agent to analyze the congestion situation at Disneyland in real time and suggests to the user the optimal order and time to queue for attractions, the timing and location of meals, and the location and time to watch events. When a user arrives at Disneyland, the AI ​​agent analyzes the congestion situation within Disneyland in real time. At this time, the AI ​​agent collects data such as the waiting time and congestion level of each attraction, the user's preferences, and past visit history. Next, based on the collected data, the AI ​​agent suggests to the user the optimal order and time to queue for attractions. Furthermore, the AI ​​agent also suggests the timing and location of meals for the user. Finally, the AI ​​agent also suggests to the user the optimal location and time to watch events held within Disneyland. For example, when a user arrives at Disneyland, the Disneyland suggestion system uses an AI agent to analyze the congestion situation within Disneyland in real time. The AI ​​agent collects data such as the waiting time and congestion level of each attraction, the user's preferences, and past visit history. Next, based on the collected data, the AI ​​agent suggests to the user the optimal order and time to queue for attractions. Furthermore, the AI ​​agent also suggests the timing and location of meals for the user. Finally, the AI ​​agent also suggests the best viewing locations and times for events held within Disneyland. In this way, the Disneyland suggestion system can suggest the optimal order and timing for queuing for attractions, the timing and location of meals, and the viewing locations and times for events, depending on the crowd situation at Disneyland. This allows users to enjoy Disneyland more efficiently and improves their satisfaction. In this way, the Disneyland suggestion system can analyze the crowd situation at Disneyland in real time based on the user's preferences and past visit history, and suggest the optimal order and timing for queuing for attractions, the timing and location of meals, and the viewing locations and times for events.

[0070] The Disneyland suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a meal suggestion unit, and an event suggestion unit. The collection unit collects user preferences and past visit history. For example, the collection unit can collect data such as the user's food preferences and attraction preferences. The collection unit can also collect the user's past visit history. For example, the collection unit can collect data such as the date and time of the user's visit, the number of visits, and the user's behavior during the visit. The analysis unit analyzes the congestion status of Disneyland in real time based on the data collected by the collection unit. For example, the analysis unit can analyze the waiting time and congestion level of each attraction in real time. The analysis unit can also predict the density of people and peak congestion times within Disneyland. The suggestion unit suggests the optimal order and time for queuing for attractions to the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest the order and time for queuing for attractions based on congestion status and user preferences. The suggestion unit can also suggest the optimal order of attractions based on the user's past visit history. The meal suggestion unit proposes the timing and location of meals. For example, the meal suggestion unit can propose the timing and location of meals based on factors such as crowd levels and user preferences. It can also propose the optimal menu based on the user's past dining history. The event suggestion unit proposes the location and time of events to watch. For example, the event suggestion unit can propose the location and time of events to watch based on factors such as crowd levels and user preferences. It can also propose the optimal event based on the user's past event participation history. As a result, the Disneyland suggestion system according to this embodiment can analyze the crowd levels of Disneyland in real time based on the user's preferences and past visit history, and propose the optimal order and time for queuing for attractions, the timing and location of meals, and the location and time for watching events.

[0071] The data collection unit collects user preferences and past visit history. Specifically, it collects detailed data such as the attractions and restaurants used by users within Disneyland, and the merchandise purchased. For example, it collects information such as which attractions users prefer, which restaurants they eat at, and what kind of merchandise they buy. It also collects the date and time of user visits, the number of visits, and their behavioral patterns during visits. This allows the data collection unit to understand user preferences and behavioral patterns in detail. Furthermore, the data collection unit also collects data from smartphone apps and wearable devices used by users within Disneyland. For example, through smartphone apps, it can collect real-time information such as the user's location, attraction usage history, and restaurant reservation status. From wearable devices, it can collect health data such as the user's heart rate, steps taken, and distance traveled. This allows the data collection unit to understand not only user preferences and behavioral patterns, but also their health status and fatigue level. The collected data is stored on a cloud server and made accessible to the analysis and proposal units. This allows the data collection unit to efficiently collect detailed user data and improve the overall system performance.

[0072] The analysis department analyzes the crowd situation at Disneyland in real time based on data collected by the data collection department. Specifically, it analyzes the wait times and crowd levels of each attraction in real time and predicts the crowd density and peak times of congestion within Disneyland. For example, it collects wait time data for each attraction and uses AI to analyze patterns of wait time fluctuations. This makes it possible to predict wait times at specific times. In addition, to understand the crowd density within Disneyland, it collects data from sensors and cameras installed in each area and uses AI to count the number of people. This allows for real-time understanding of the crowd level in each area. Furthermore, it can also predict peak times of congestion by utilizing past data and statistical information. For example, based on past visit data, it analyzes crowd trends at specific days of the week and times of day and predicts future peak times of congestion. This allows the analysis department to not only understand the real-time crowd situation but also predict future crowds. Furthermore, it can use anomaly detection algorithms to detect unusual crowd patterns and abnormal data and issue warnings early. This allows the analysis department to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0073] The suggestion department proposes the optimal order and timing for queuing for attractions to users based on the analysis results obtained by the analysis department. Specifically, it proposes the order and timing for queuing for attractions based on crowd conditions and user preferences. For example, it can find times when the wait time for a user's favorite attraction is short and suggest using that attraction during those times. It can also propose the optimal order of attractions based on the user's past visit history. For example, it can analyze the order and time spent on attractions visited in the past and propose the optimal order. Furthermore, the suggestion department can also propose the optimal route based on the user's current location. For example, it can suggest the nearest attraction or restaurant from the user's current location to enable efficient travel. In this way, the suggestion department can propose the optimal order and timing for queuing for attractions based on user preferences and behavior patterns, thereby improving user satisfaction. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can check whether the suggested order and timing for attractions were actually used and use this to improve the suggestions. In this way, the suggestion department can provide users with the best possible suggestions and make their Disneyland experience more fulfilling.

[0074] The dining suggestion department proposes the timing and location of meals. Specifically, it suggests meal times and locations based on crowd levels and user preferences. For example, it can find less crowded times and less busy restaurants and suggest meals at those times and locations. It can also suggest optimal menus based on the user's past dining history. For example, it can analyze restaurants visited and menus ordered in the past to suggest menus the user would like. Furthermore, the dining suggestion department can suggest appropriate menus considering the user's health condition and allergy information. For example, it can suggest menus that avoid ingredients the user is allergic to, providing health-conscious meals. In this way, the dining suggestion department can suggest the optimal timing, location, and menu based on the user's preferences and health condition, thereby improving user satisfaction. In addition, the dining suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can check whether the suggested menus were actually ordered and use this to improve the suggestions. In this way, the dining suggestion department can provide users with optimal meal suggestions and make their dining experience at Disneyland even more fulfilling.

[0075] The Event Proposal Department suggests event viewing locations and times. Specifically, they propose viewing locations and times based on crowd conditions and user preferences. For example, they identify less crowded times and less crowded viewing locations and suggest viewing events at those times and locations. They can also suggest the most suitable events based on the user's past event participation history. For example, they analyze past events attended and viewing locations to suggest events the user would like. Furthermore, the Event Proposal Department can suggest the most suitable viewing locations based on the user's current location. For example, they suggest the viewing location closest to the user's current location to allow for efficient travel. In this way, the Event Proposal Department can suggest the most suitable event viewing locations and times based on user preferences and behavioral patterns, thereby improving user satisfaction. In addition, the Event Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, they can check whether the suggested events and viewing locations were actually used and use this information to improve the suggestions. In this way, the Event Proposal Department can provide users with the most suitable event suggestions and enhance their event experience at Disneyland.

[0076] The reminder function provides reminders. For example, the reminder function can notify users of suggested schedules. The reminder function provides timely reminders so that users do not forget to follow the suggested schedule. For example, the reminder function can notify users when the wait time for an attraction will be shorter. It can also notify users of meal times so that they do not miss meal times. Furthermore, the reminder function can notify users of event schedules so that they do not forget the location and time of the event. In this way, the reminder function supports users in remembering to follow the suggested schedule.

[0077] The restaurant recommendation department provides menus tailored to user preferences. For example, it can suggest the optimal menu based on a user's eating preferences. The restaurant recommendation department analyzes users' past dining history and preference data to suggest the most suitable menu. For instance, it can suggest the optimal menu based on dishes users have enjoyed eating in the past. It can also suggest the optimal menu based on dishes users have avoided in the past. Furthermore, it can suggest the optimal menu based on data from restaurants users have visited in the past. In this way, the restaurant recommendation department can improve dining satisfaction by providing menus tailored to users' preferences.

[0078] The data collection unit can estimate the user's emotions and adjust the types of data it collects based on those emotions. For example, if the user is excited, the unit may prioritize collecting data on attraction wait times and crowd levels. If the user is tired, it may prioritize collecting information on rest areas and restaurants. If the user is relaxed, it may prioritize collecting information on events and shows. In this way, the data collection unit can collect more appropriate data by adjusting the types of data it collects according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The data collection unit can analyze a user's past visit history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on attractions the user has visited in the past. It can also prioritize collecting data on restaurants the user has used in the past. Furthermore, it can prioritize collecting data on events the user has participated in in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past visit history.

[0080] The data collection unit can collect user behavior patterns within specific areas of Disneyland. For example, the unit can collect the amount of time users spend in a specific area. The unit can also collect the routes users take within a specific area. Furthermore, the unit can collect information on users' attraction usage within a specific area. By collecting user behavior patterns within specific areas, the unit can gain a detailed understanding of user behavior.

[0081] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, the unit will prioritize collecting data on attraction wait times and crowd levels. If the user is tired, the unit will prioritize collecting information on rest areas and restaurants. If the user is relaxed, the unit will prioritize collecting information on events and shows. In this way, the data collection unit can prioritize collecting more important data by prioritizing the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The data collection unit can analyze users' social media activity and collect relevant preference data. For example, it can analyze photos and posts that users share on social media to identify attractions and events that users are interested in. It can also analyze accounts that users follow and groups that users participate in to collect relevant preference data. Furthermore, it can analyze locations that users check in to on social media to identify areas and activities that users are interested in. In this way, the data collection unit can collect relevant preference data by analyzing users' social media activity.

[0083] The data collection unit can collect behavioral data in specific areas, taking into account the user's geographical location. For example, the data collection unit can prioritize collecting data on attractions and events in the area where the user is currently located. The data collection unit can also collect data on congestion levels and wait times in areas where the user plans to move. Furthermore, the data collection unit can analyze the user's behavioral patterns in areas they have visited in the past and collect relevant data. In this way, the data collection unit can collect behavioral data in specific areas by taking into account the user's geographical location.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize the wait times and crowd levels of attractions. If the user is tired, the analysis unit can prioritize the crowd levels of rest areas and restaurants. If the user is relaxed, the analysis unit can prioritize the schedules of events and shows. In this way, the analysis unit can obtain more appropriate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0085] The analysis department can analyze crowd levels within Disneyland in real time and predict peak times. For example, the analysis department can collect wait time data for each attraction in real time and predict peak times. The analysis department can also collect crowd level data for each area in real time and predict peak times. Furthermore, the analysis department can predict peak times for specific days of the week or time slots based on past crowd data. In this way, the analysis department can predict peak times by analyzing crowd levels in real time.

[0086] The analysis department can analyze the wait time data for each attraction in detail and identify the cause of congestion. For example, the analysis department can collect wait time data for each attraction and identify the cause of congestion. The analysis department can collect user count data for each attraction and identify the cause of congestion. In addition, the analysis department can collect operational status data for each attraction and identify the cause of congestion. In this way, the analysis department can identify the cause of congestion by analyzing the wait time data for each attraction in detail.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. If the user is tired, the analysis unit can provide a simple and easy-to-read display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. In this way, the analysis unit can provide a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis department can analyze crowd levels within Disneyland, taking weather data into consideration. For example, during rainy weather, the analysis department will focus on the crowd levels of indoor attractions. During sunny weather, the analysis department will focus on the crowd levels of outdoor attractions. Furthermore, the analysis department can predict crowd levels in response to changes in weather. This allows the analysis department to conduct more accurate crowd level analyses by considering weather data.

[0089] The analysis department can analyze crowd levels by taking into account the event schedule within Disneyland. For example, the analysis department can focus its analysis on crowd levels during specific events. The analysis department can predict crowd levels based on the start and end times of events. Furthermore, the analysis department can also predict crowd levels based on the popularity of events. This allows the analysis department to conduct more accurate crowd level analyses by considering the event schedule.

[0090] The suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is excited, the suggestion unit will prioritize suggesting popular attractions. If the user is tired, the suggestion unit can prioritize suggesting rest areas and restaurants. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggesting events and shows. This allows the suggestion unit to provide more appropriate suggestions by adjusting its recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The suggestion function can propose the optimal order of attractions by considering the user's past visit history. For example, the suggestion function can propose the optimal order based on data of attractions the user has visited in the past. It can also propose the optimal order based on data of restaurants the user has used in the past. Furthermore, the suggestion function can propose the optimal order based on data of events the user has participated in in the past. In this way, the suggestion function can propose the optimal order of attractions by considering the user's past visit history.

[0092] The suggestion unit can propose the optimal travel route by considering the user's current location information. For example, the suggestion unit can suggest the attraction closest to the user's current location. The suggestion unit can also propose the optimal travel route by considering the congestion level of the area the user plans to visit. Furthermore, the suggestion unit can propose the optimal travel route based on the user's behavior patterns in areas they have visited in the past. In this way, the suggestion unit can propose the optimal travel route by considering the user's current location information.

[0093] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is excited, the suggestion unit will prioritize suggesting popular attractions. If the user is tired, the suggestion unit can prioritize suggesting rest areas and restaurants. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggesting events and shows. This allows the suggestion unit to provide more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The suggestion department can propose the most suitable attractions by considering the user's age and family structure. For example, if the user is with children, the suggestion department will suggest family-friendly attractions. If the user is elderly, the suggestion department can suggest attractions that take their physical abilities into consideration. Furthermore, if the user is a couple, the suggestion department can suggest romantic attractions. In this way, the suggestion department can propose the most suitable attractions by considering the user's age and family structure.

[0095] The suggestion department can propose the most suitable attraction by considering the user's health condition. For example, if the user is tired, the suggestion department can propose an attraction that takes their physical condition into account. If the user is seeking healthy exercise, the suggestion department can propose an active attraction. Furthermore, if the user is feeling unwell, the suggestion department can also suggest rest areas or restaurants. In this way, the suggestion department can propose the most suitable attraction by considering the user's health condition.

[0096] The meal suggestion unit can estimate the user's emotions and adjust the meal suggestions based on those emotions. For example, if the user is excited, the meal suggestion unit can suggest a menu suitable for energy replenishment. If the user is tired, the meal suggestion unit can suggest a relaxing dining location. If the user is relaxed, the meal suggestion unit can also suggest a dining location where they can enjoy themselves at a leisurely pace. In this way, the meal suggestion unit can provide more appropriate meal suggestions by adjusting the meal suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The meal suggestion system can propose the most suitable menu by considering the user's past eating history. For example, it can suggest the most suitable menu based on the menu the user has enjoyed eating in the past. It can also suggest the most suitable menu based on the menu the user has avoided in the past. Furthermore, it can suggest the most suitable menu based on data from restaurants the user has visited in the past. In this way, the meal suggestion system can propose the most suitable menu by considering the user's past eating history.

[0098] The meal suggestion department can propose the most suitable menu by taking into account the user's allergy information when suggesting meals. For example, the meal suggestion department can suggest menus that avoid ingredients the user is allergic to. The meal suggestion department can also suggest restaurants that do not contain ingredients the user is allergic to. Furthermore, the meal suggestion department can suggest safe dining locations based on the user's allergy information. In this way, the meal suggestion department can propose safe menus by taking the user's allergy information into consideration.

[0099] The meal suggestion unit can estimate the user's emotions and adjust the timing of meals based on those emotions. For example, if the user is excited, the unit can suggest an earlier meal to replenish energy. If the user is tired, the unit can suggest a meal that also serves as a rest. If the user is relaxed, the unit can suggest a meal that can be enjoyed at a leisurely pace. In this way, the meal suggestion unit can suggest more appropriate meal times by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The meal suggestion function can propose the most suitable menu by considering the user's health condition. For example, if the user is tired, the function can suggest a highly nutritious menu. If the user is seeking a healthy meal, the function can suggest a healthy menu. Furthermore, if the user is feeling unwell, the function can suggest an easily digestible menu. In this way, the meal suggestion function can propose the most suitable menu by considering the user's health condition.

[0101] The dining recommendation department can suggest the most suitable restaurant when making dining recommendations, taking the user's budget into consideration. For example, the department can suggest restaurants that fit the user's budget. It can also suggest menus that fit the user's budget. Furthermore, it can suggest dining plans that fit the user's budget. In this way, the dining recommendation department can suggest the most suitable restaurant by considering the user's budget.

[0102] The event suggestion unit can estimate the user's emotions and adjust the event suggestions based on those emotions. For example, if the user is excited, the event suggestion unit can suggest active events. If the user is tired, the event suggestion unit can suggest relaxing events. If the user is relaxed, the event suggestion unit can also suggest events that can be enjoyed at a leisurely pace. In this way, the event suggestion unit can provide more appropriate event suggestions by adjusting the event suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The event proposal department can suggest the most suitable event by considering the user's past event participation history. For example, it can suggest the most suitable event based on data of events the user has previously attended. It can also suggest the most suitable event based on data of events the user has previously avoided. Furthermore, it can suggest the most suitable event based on data of events the user has previously enjoyed. In this way, the event proposal department can suggest the most suitable event by considering the user's past event participation history.

[0104] The event proposal department can propose the most suitable events by considering the user's interests and preferences when proposing events. For example, the event proposal department can propose events on themes that interest the user. The event proposal department can propose events featuring characters that interest the user. Furthermore, the event proposal department can propose events involving activities that the user can enjoy. In this way, the event proposal department can propose the most suitable events by considering the user's interests and preferences.

[0105] The event suggestion unit can estimate the user's emotions and adjust the event viewing location based on those emotions. For example, if the user is excited, the event suggestion unit can suggest an active viewing location. If the user is tired, the event suggestion unit can suggest a relaxing viewing location. Furthermore, if the user is relaxed, the event suggestion unit can suggest a viewing location that allows for leisurely enjoyment. In this way, the event suggestion unit can suggest a more appropriate viewing location by adjusting the event viewing location according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The event proposal department can suggest the most suitable event by considering the user's age and family structure. For example, if the user is with children, the department can suggest a family-friendly event. If the user is elderly, the department can suggest an event that takes their physical abilities into consideration. Furthermore, if the user is a couple, the department can suggest a romantic event. In this way, the event proposal department can suggest the most suitable event by considering the user's age and family structure.

[0107] The event proposal department can propose the most suitable event by taking the user's schedule into consideration. For example, the event proposal department can propose an event that fits the user's schedule. The event proposal department can propose an event viewing time that fits the user's schedule. Furthermore, the event proposal department can propose an event viewing location that fits the user's schedule. In this way, the event proposal department can propose the most suitable event by taking the user's schedule into consideration.

[0108] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is excited, the reminder unit can set an earlier reminder. If the user is tired, the reminder unit can set a reminder that also serves as a break. Furthermore, if the user is relaxed, the reminder unit can set a slower reminder. In this way, the reminder unit can provide more appropriate reminders by adjusting the timing of reminders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The reminder function can provide optimal reminder content by considering the user's past behavior history when a reminder is issued. For example, the reminder function can provide optimal reminder content based on data of reminders the user has used in the past. The reminder function can provide optimal reminder content based on data of reminders the user has avoided in the past. Furthermore, the reminder function can provide optimal reminder content based on data of reminders the user has preferred in the past. In this way, the reminder function can provide optimal reminder content by considering the user's past behavior history.

[0110] The reminder function can estimate the user's emotions and determine the priority of reminders based on those emotions. For example, if the user is excited, the reminder function will prioritize important reminders. If the user is tired, the reminder function will prioritize reminders that also serve as breaks. If the user is relaxed, the reminder function will also prioritize slow-paced reminders. In this way, the reminder function can prioritize more important reminders by determining the priority of reminders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The reminder function can provide optimal reminder content by considering the user's current location information when a reminder is issued. For example, the reminder function can provide optimal reminder content based on information about the area the user is currently in. The reminder function can provide optimal reminder content based on information about the area the user plans to move to. Furthermore, the reminder function can provide optimal reminder content based on information about areas the user has visited in the past. In this way, the reminder function can provide optimal reminder content by considering the user's current location information.

[0112] The restaurant recommendation system can estimate the user's emotions and adjust its restaurant recommendations based on those emotions. For example, if the user is excited, the system can suggest a restaurant suitable for replenishing energy. If the user is tired, it can suggest a restaurant where they can relax. If the user is relaxed, it can also suggest a restaurant where they can enjoy themselves at a leisurely pace. In this way, the system can make more appropriate restaurant recommendations by adjusting its recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The restaurant recommendation department can suggest the most suitable restaurant by considering the user's past dining history. For example, it can suggest the most suitable restaurant based on data of restaurants the user has visited in the past. It can also suggest the most suitable restaurant based on data of restaurants the user has avoided in the past. Furthermore, it can suggest the most suitable restaurant based on data of restaurants the user has enjoyed in the past. In this way, the restaurant recommendation department can suggest the most suitable restaurant by considering the user's past dining history.

[0114] The restaurant recommendation system can estimate the user's emotions and prioritize restaurants based on those emotions. For example, if the user is excited, the system will prioritize restaurants suitable for replenishing energy. If the user is tired, the system will prioritize restaurants where they can relax. If the user is relaxed, the system will also prioritize restaurants where they can enjoy themselves at a leisurely pace. In this way, the restaurant recommendation system can provide more appropriate restaurant recommendations by prioritizing restaurants according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The restaurant recommendation department can suggest the most suitable restaurant, taking the user's budget into consideration. For example, the department can suggest restaurants that fit the user's budget, menus that fit the budget, and meal plans that fit the budget. In this way, the restaurant recommendation department can suggest the most suitable restaurant by considering the user's budget.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The suggestion function can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is excited, the suggestion function can prioritize suggesting active attractions and events. If the user is tired, the suggestion function can suggest resting places and relaxing attractions. If the user is relaxed, the suggestion function can also suggest attractions and events that can be enjoyed at a leisurely pace. In this way, the suggestion function can make more appropriate suggestions by adjusting its content according to the user's emotions.

[0118] The reminder function can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is excited, the reminder function can set an earlier reminder. If the user is tired, the reminder function can set a reminder that also serves as a break. If the user is relaxed, the reminder function can set a slower reminder. In this way, the reminder function can provide more appropriate reminders by adjusting the timing of reminders according to the user's emotions.

[0119] The meal suggestion unit can estimate the user's emotions and adjust the meal suggestions based on those emotions. For example, if the user is excited, the meal suggestion unit can suggest a menu suitable for energy replenishment. If the user is tired, the meal suggestion unit can suggest a relaxing dining location. Also, if the user is relaxed, the meal suggestion unit can suggest a dining location where they can enjoy themselves at a leisurely pace. In this way, the meal suggestion unit can provide more appropriate meal suggestions by adjusting the suggestions according to the user's emotions.

[0120] The event suggestion department can estimate the user's emotions and adjust the event suggestions based on those emotions. For example, if the user is excited, the department can suggest an active event. If the user is tired, the department can suggest a relaxing event. Also, if the user is relaxed, the department can suggest an event that can be enjoyed at a leisurely pace. In this way, the event suggestion department can make more appropriate event suggestions by adjusting the event suggestions according to the user's emotions.

[0121] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is excited, the analysis unit can prioritize the wait times and crowd levels of attractions. If the user is tired, the analysis unit can prioritize the crowd levels of rest areas and restaurants. If the user is relaxed, the analysis unit can prioritize the schedules of events and shows. By adjusting the analysis algorithm according to the user's emotions, the analysis unit can obtain more appropriate analysis results.

[0122] The data collection unit can analyze users' social media activity and collect relevant preference data. For example, it can analyze photos and posts shared by users on social media to identify attractions and events of interest. The data collection unit can analyze accounts followed and groups joined by users to collect relevant preference data. It can also analyze locations checked in by users on social media to identify areas and activities of interest. In this way, the data collection unit can collect relevant preference data by analyzing users' social media activity.

[0123] The data collection unit can collect user behavior patterns in specific areas within Disneyland. For example, it can collect the amount of time users spend in a specific area. It can also collect the routes users take within a specific area. Furthermore, it can collect information on users' attraction usage within a specific area. By collecting user behavior patterns in specific areas, the data collection unit can gain a detailed understanding of user behavior.

[0124] The data collection unit can collect behavioral data in specific areas, taking into account the user's geographical location. For example, the unit can prioritize collecting data on attractions and events in the area where the user is currently located. It can also collect data on congestion levels and wait times in areas the user plans to visit. Furthermore, the unit can analyze behavioral patterns in areas the user has visited in the past and collect relevant data. In this way, the data collection unit can collect behavioral data in specific areas by considering the user's geographical location.

[0125] The analysis department can analyze crowd levels within Disneyland by taking weather data into consideration. For example, during rainy weather, the analysis department can focus on analyzing crowd levels at indoor attractions. During sunny weather, the analysis department can focus on analyzing crowd levels at outdoor attractions. Furthermore, the analysis department can predict crowd levels in response to changes in weather. This allows the analysis department to conduct more accurate crowd level analyses by considering weather data.

[0126] The suggestion department can propose the most suitable attractions by considering the user's age and family structure. For example, if the user is with children, the suggestion department can suggest family-friendly attractions. If the user is elderly, the suggestion department can suggest attractions that take their physical abilities into consideration. Also, if the user is a couple, the suggestion department can suggest romantic attractions. In this way, the suggestion department can propose the most suitable attractions by considering the user's age and family structure.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects user preferences and past visit history. For example, it can collect data such as the user's food preferences, attraction preferences, visit dates and times, number of visits, and behavior during visits. Step 2: The analysis unit analyzes the crowd situation at Disneyland in real time based on the data collected by the data collection unit. For example, it can predict wait times, crowd levels, crowd density, and peak hours for each attraction. Step 3: The proposal department proposes the optimal queue order and timing for attractions to the user based on the analysis results obtained by the analysis department. For example, it can propose the queue order and timing for attractions based on crowd conditions, user preferences, and past visit history. Step 4: The meal suggestion department proposes the timing and location of meals. For example, it can suggest the timing, location, and optimal menu based on factors such as crowd levels, user preferences, and past dining history. Step 5: The event proposal team suggests event viewing locations and times. For example, they can suggest optimal events and viewing locations based on factors such as crowd conditions, user preferences, and past event participation history.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, meal proposal unit, event proposal unit, reminder unit, and restaurant proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user preferences and past visit history using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the congestion status of Disneyland in real time based on the collected data. The proposal unit, meal proposal unit, event proposal unit, reminder unit, and restaurant proposal unit are implemented by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and propose to the user the optimal order and time to queue for attractions, the timing and location of meals, the viewing location and time of events, reminder functions, and menus. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, meal suggestion unit, event suggestion unit, reminder unit, and restaurant suggestion unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect user preferences and past visit history. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the congestion status of Disneyland in real time based on the collected data. The suggestion unit, meal suggestion unit, event suggestion unit, reminder unit, and restaurant suggestion unit are implemented by, for example, the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and suggest to the user the optimal order and time to queue for attractions, the timing and location of meals, the viewing location and time of events, reminder functions, and menus. 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.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

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

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

[0159] In 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.

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0163] The data processing system 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, meal suggestion unit, event suggestion unit, reminder unit, and restaurant suggestion unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect user preferences and past visit history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and analyzes the congestion status of Disneyland in real time based on the collected data. The suggestion unit, meal suggestion unit, event suggestion unit, reminder unit, and restaurant suggestion unit are implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, for example, and suggest the optimal order and time to queue for attractions, the timing and location of meals, the viewing location and time of events, reminder functions, and menus to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, meal proposal unit, event proposal unit, reminder unit, and restaurant proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect user preferences and past visit history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the congestion status of Disneyland in real time based on the collected data. The proposal unit, meal proposal unit, event proposal unit, reminder unit, and restaurant proposal unit are implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and propose to the user the optimal order and time to queue for attractions, the timing and location of meals, the viewing location and time of events, reminder functions, and menus. 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A data collection unit that collects user preferences and past visit history, Based on the data collected by the aforementioned collection unit, an analysis unit analyzes the congestion situation at Disneyland in real time. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit suggests the optimal order and time for queuing for attractions to the user. The Meal Planning Department proposes the timing and location of meals, It includes an event proposal department that suggests viewing locations and times for events. A system characterized by the following features. (Note 2) It features a reminder section that provides a reminder function. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a restaurant proposal department that provides menus tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past visit history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect user behavior patterns in specific areas within Disneyland. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' social media activity and collect relevant preference data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Collect behavioral data in specific areas, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is We analyze the crowd situation inside Disneyland in real time and predict peak times. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We will analyze the wait time data for each attraction in detail to identify the causes of congestion. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We will analyze crowd levels by taking weather data within Disneyland into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We will analyze the crowd situation, taking into account the event schedule within Disneyland. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, we take into account the user's past visit history to propose the optimal order of attractions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a suggestion, the system takes the user's current location into account and proposes the optimal travel route. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we consider the user's age and family structure to suggest the most suitable attractions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will consider the user's health condition and suggest the most suitable attraction. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned meal proposal department, The system estimates the user's emotions and adjusts meal suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned meal proposal department, When suggesting meals, the system takes into account the user's past eating history to propose the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned meal proposal department, When suggesting meals, the system takes the user's allergy information into consideration and proposes the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned meal proposal department, It estimates the user's emotions and adjusts meal timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned meal proposal department, When suggesting meals, the system takes the user's health condition into consideration and proposes the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned meal proposal department, When suggesting restaurants, we take the user's budget into consideration and propose the most suitable restaurant. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned event proposal department, It estimates the user's emotions and adjusts event suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned event proposal department, When proposing events, we consider the user's past event participation history to suggest the most suitable events. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned event proposal department, When proposing events, we consider the user's interests and preferences to suggest the most suitable events. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned event proposal department, It estimates the user's emotions and adjusts the event viewing location based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned event proposal department, When proposing events, we take into account the user's age and family structure to suggest the most suitable event. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned event proposal department, When proposing events, we take the user's schedule into consideration and suggest the most suitable event. The system described in Appendix 1, characterized by the features described herein. (Note 34) The reminder unit is, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The reminder unit is, When sending reminders, the system takes into account the user's past behavior history to provide the most appropriate reminder content. The system described in Appendix 1, characterized by the features described herein. (Note 36) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The reminder unit is, When a reminder is sent, the system takes the user's current location into consideration to provide the most appropriate reminder content. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned restaurant proposal department, The system estimates the user's emotions and adjusts restaurant recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned restaurant proposal department, When suggesting restaurants, the system takes into account the user's past dining history to recommend the most suitable restaurant. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned restaurant proposal department, The system estimates the user's emotions and prioritizes restaurants based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned restaurant proposal department, When suggesting restaurants, we take the user's budget into consideration and propose the most suitable restaurant. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user preferences and past visit history, Based on the data collected by the aforementioned collection unit, an analysis unit analyzes the congestion situation at Disneyland in real time. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit suggests the optimal order and time for queuing for attractions to the user. The Meal Planning Department proposes the timing and location of meals, It includes an event proposal department that suggests viewing locations and times for events. A system characterized by the following features.

2. It features a reminder section that provides a reminder function. The system according to feature 1.

3. We have a restaurant proposal department that provides menus tailored to the user's preferences. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

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

6. The aforementioned collection unit is Collect user behavior patterns in specific areas within Disneyland. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze users' social media activity and collect relevant preference data. The system according to feature 1.

9. The aforementioned collection unit is Collect behavioral data in specific areas, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

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