System

Abstract

An object of a system according to an embodiment is to grasp a congestion situation of a facility in real time and guide a user to an appropriate facility.SOLUTION: A system according to an embodiment includes a congestion situation acquisition unit, a facility search unit, and a listing unit. The congestion situation acquisition unit acquires a congestion situation of the facility in real time. The facility search unit searches for a vacant facility near the current location of the user based on the congestion situation acquired by the congestion situation acquisition unit. The listing unit lists the facilities retrieved by the facility retrieval unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide a means for grasping the congestion status of facilities in real time and guiding users to appropriate facilities, and there is room for improvement.

[0005] The system according to the embodiment aims to grasp the congestion status of facilities in real time and guide users to appropriate facilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a congestion status acquisition unit, a facility search unit, and a listing unit. The congestion status acquisition unit acquires the congestion status of facilities in real time. The facility search unit searches for available facilities near the user's current location based on the congestion status acquired by the congestion status acquisition unit. The listing unit lists the facilities searched for by the facility search unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the congestion status of a facility in real time and guide the user to an appropriate facility. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The MachiZero system according to an embodiment of the present invention is a system that grasps the congestion status of facilities in real time and provides users with the most suitable facilities. This allows users to avoid congestion, reduce waiting times, and realize a comfortable outing.

[0029] The MachiZero system according to the embodiment includes a congestion status acquisition unit, a facility search unit, and a listing unit. The congestion status acquisition unit acquires the congestion status of a facility in real time. For example, it analyzes data obtained from sensors and cameras within the facility to calculate the current congestion level. The congestion status acquisition unit can also analyze the number of people in the facility, the length of their stay, and the frequency of entry and exit. For example, a sensor detects people's movements within the facility and calculates the congestion level based on that data. The facility search unit searches for available facilities near the user's current location based on the congestion status acquired by the congestion status acquisition unit. For example, it acquires the user's current location from GPS data and searches for available facilities based on the congestion status of surrounding facilities. The facility search unit can also perform a search taking into account the type of facility desired by the user. For example, if the user desires a shopping mall, it searches for available shopping malls in the vicinity. The listing unit lists the facilities found by the facility search unit. For example, it displays the search results in a list format to make it easier for the user to select a facility. The listing unit can also include information such as the facility's congestion status, distance, and rating in the list. For example, the list displays the congestion level, distance, and user ratings of each facility. This allows the MachiZero system according to the embodiment to grasp the congestion status of facilities in real time and provide users with the most suitable facilities. For example, this allows users to avoid congestion and use facilities comfortably. It also makes it easier for users to select facilities based on the list.

[0030] The congestion status acquisition unit can analyze audio data within the facility and estimate the degree of congestion from the noise level. The congestion status acquisition unit, for example, analyzes audio data collected from microphones installed within the facility and measures the noise level. For example, it analyzes the audio data in real time and estimates that the degree of congestion is high if the noise level is high. The congestion status acquisition unit can also analyze the frequency and volume of the audio data. For example, if the volume of a specific frequency band is high, it estimates that the area is congested. The congestion status acquisition unit can also analyze the fluctuation pattern of the audio data. For example, if the audio data fluctuates significantly, it estimates that the area is congested. In this way, the degree of congestion can be accurately estimated by analyzing the audio data.

[0031] The congestion status acquisition unit can analyze data from temperature and humidity sensors and estimate the congestion level from environmental changes. The congestion status acquisition unit, for example, analyzes data collected from temperature and humidity sensors installed within the facility to monitor environmental changes. For example, if the temperature or humidity rises rapidly, it estimates that the congestion level is high. The congestion status acquisition unit can also analyze temperature and humidity fluctuation patterns. For example, if the temperature or humidity fluctuates drastically, it estimates that the area is congested. The congestion status acquisition unit can also predict the congestion level based on temperature and humidity data. For example, it can build a congestion level prediction model based on past data and predict the congestion level based on current data. This allows the congestion level to be accurately estimated by analyzing temperature and humidity data.

[0032] The congestion status acquisition unit can analyze traffic condition data outside the facility and associate it with the congestion level. The congestion status acquisition unit, for example, collects traffic condition data around the facility and analyzes traffic volume and congestion status. For example, if traffic volume is high, it estimates that the facility is highly congested. The congestion status acquisition unit can also monitor traffic conditions using traffic sensors and GPS data. For example, a traffic sensor detects the passage of a vehicle and calculates traffic volume based on that data. The congestion status acquisition unit can also analyze fluctuation patterns in the traffic condition data. For example, if traffic volume fluctuates significantly, it estimates that the area is congested. In this way, the congestion level of the facility can be accurately associated by analyzing the traffic condition data.

[0033] The congestion status acquisition unit can analyze the number of Wi-Fi connections and estimate the congestion level from the number of connected devices. The congestion status acquisition unit, for example, analyzes the number of connected devices collected from Wi-Fi access points within a facility and estimates the congestion level. For example, if the number of connected devices is large, it is estimated that the congestion level is high. The congestion status acquisition unit can also analyze fluctuation patterns in the number of Wi-Fi connections. For example, if there is a large fluctuation in the number of connected devices, it is estimated that the area is congested. The congestion status acquisition unit can also analyze the type and connection time of connected devices. For example, if there is a large number of smartphone and tablet connections, it is estimated that the area is congested. In this way, the congestion level can be accurately estimated by analyzing the number of Wi-Fi connections.

[0034] The facility search unit can analyze the user's visit history and prioritize listing favorite facilities. The facility search unit, for example, analyzes the user's past visit history and identifies favorite facilities. For example, prioritize listing facilities that the user has frequently visited in the past or highly rated facilities. The facility search unit can also analyze the type and characteristics of facilities based on the user's visit history. For example, the user can identify favorite facilities based on the type and characteristics of facilities that the user has visited in the past. The facility search unit can also analyze facility ratings based on the user's visit history. For example, prioritize listing facilities that the user has highly rated. In this way, favorite facilities can be prioritized by analyzing the user's past visit history.

[0035] The facility search unit can analyze facility reviews and ratings and prioritize listing highly rated facilities. The facility search unit, for example, collects and analyzes facility review and rating data. For example, it prioritizes listing highly rated facilities. The facility search unit can also analyze the content of reviews and ratings. For example, it identifies highly rated facilities based on the content of reviews and rating scores. The facility search unit can also analyze fluctuation patterns in reviews and ratings. For example, it prioritizes listing facilities with little fluctuation in rating scores. In this way, by analyzing reviews and ratings, it is possible to prioritize listing highly rated facilities.

[0036] The facility search unit can analyze facility benefits and discount information and list the most cost-effective facilities for the user. The facility search unit, for example, collects and analyzes benefit and discount information provided by facilities. For example, it prioritizes listing facilities with high discount rates. The facility search unit can also analyze the content of benefit and discount information. For example, it identifies the most cost-effective facility based on the content of benefit and discount rate. The facility search unit can also analyze fluctuation patterns in benefit and discount information. For example, it prioritizes listing facilities with least fluctuation in benefit and discount information. In this way, by analyzing benefit and discount information, it is possible to list the most cost-effective facilities for the user.

[0037] The facility search unit can analyze facility business hours and special event information, and list facilities that can be visited at the optimal time for the user. The facility search unit, for example, collects and analyzes facility business hours and special event information. For example, it prioritizes listing facilities with long business hours or facilities that hold special events. The facility search unit can also analyze the content of business hours and special event information. For example, it identifies facilities that can be visited at the optimal time based on the length of business hours and the content of the event. The facility search unit can also analyze fluctuation patterns in business hours and special event information. For example, it prioritizes listing facilities with little fluctuation in business hours. In this way, by analyzing business hours and special event information, it is possible to list facilities that can be visited at the optimal time for the user.

[0038] The system can work in conjunction with a reservation system to predict reservation cancellation rates and change frequency. For example, the system collects data from the reservation system and analyzes past reservation cancellation rates and change frequency. For example, if there are many cancellations during a particular time period or day of the week, the system can predict that trend. The system can also analyze fluctuation patterns in reservation data. For example, if there are large fluctuations in cancellation rates or change frequency, the system can identify those time periods or days of the week. The system can also predict cancellation rates and change frequency using a predictive algorithm. For example, a predictive model can be built based on past data and predictions can be made based on current data. This makes reservation management more efficient by predicting reservation cancellation rates and change frequency.

[0039] The system can work in conjunction with a reservation system to suggest the optimal reservation time slot. For example, the system collects data from the reservation system and analyzes past reservation status. For example, it identifies a tendency for reservations to be concentrated during certain time periods or days of the week, and suggests the optimal reservation time slot. The system can also analyze fluctuation patterns in reservation data. For example, it can identify time periods or days of the week with fewer reservations, and suggest those time slots. The system can also suggest reservation time slots taking into account the user's desired time and the availability of the facility. For example, it can suggest the optimal reservation time slot based on the user's desired time. This improves user convenience by suggesting the optimal reservation time slot.

[0040] The system can link with a reservation system to provide the function of managing reservations for multiple facilities in one place. For example, the system can link with a reservation system to build a system that manages reservations for multiple facilities in one place. For example, reservations for restaurants, hospitals, sports facilities, etc. can be managed on a single platform. The system can also integrate reservation data and manage it all together on a management screen. For example, data from multiple reservation systems can be integrated and managed centrally on a management screen. The system can also change or cancel reservations in one place. For example, reservations for multiple facilities can be changed or canceled in one place. This makes reservation management more efficient by managing reservations for multiple facilities in one place.

[0041] The system can work in conjunction with a reservation system and automatically adjust the optimal reservation time in conjunction with the user's schedule. For example, the system can work in conjunction with a reservation system to build a system that analyzes the user's schedule and automatically adjusts the optimal reservation time. For example, the system can adjust the reservation time in conjunction with a calendar app. The system can also automatically adjust the reservation time based on schedule data. For example, the system optimizes the reservation time based on the user's schedule. The system can also automatically readjust the reservation time in response to changes in the schedule. For example, if the schedule changes, the reservation time is automatically readjusted. This makes it possible to automatically adjust the optimal reservation time based on the user's schedule.

[0042] The system can analyze the user's location information and calculate the optimal notification timing. For example, the system can be constructed to analyze the user's location information in real time and calculate the optimal notification timing. For example, a notification is sent when the user approaches a facility. The system can also analyze the fluctuation pattern of the location information. For example, the notification timing is calculated based on the user's movement speed and direction. The system can also analyze the user's behavioral patterns and identify the optimal notification timing. For example, if the user is in a specific location at a specific time period, the notification is sent during that time period. This makes it possible to calculate the optimal notification timing based on the user's location information.

[0043] The system can analyze a user's device usage patterns and select the most effective notification method. For example, the system can analyze a user's device usage patterns and build a system that selects the most effective notification method. For example, the system can analyze the frequency of smartphone use and notification reception status. The system can also analyze fluctuations in device usage patterns. For example, if device usage is high during a specific time period, notifications can be sent during that time period. The system can also select the notification method based on the user's preferences. For example, the system can select the optimal method from push notifications, email notifications, SMS notifications, etc. This allows the system to select the most effective notification method based on the user's device usage patterns.

[0044] The system can work in conjunction with a user's calendar app to provide notifications based on a schedule. For example, the system can work in conjunction with a user's calendar app to build a system that provides notifications based on a schedule. For example, a notification can be sent just before an appointment. The system can also adjust the timing of notifications based on data from the calendar app. For example, a notification can be sent to coincide with the start time of an appointment. The system can also analyze data from the calendar app to identify the optimal timing of notifications. For example, the notification timing can be adjusted in response to changes in the appointment. In this way, the timing of notifications can be optimized by providing notifications based on the user's schedule.

[0045] The system can link with a user's social media account to notify the user of related event and facility information. For example, the system can link with a user's social media account to build a system that notifies the user of related event and facility information. For example, the system can notify the user of information about events that the user is interested in. The system can also analyze social media data to identify the user's interests. For example, the system can identify interests based on the accounts the user follows and the content of posts. The system can also customize the content of notifications based on social media data. For example, the system can notify the user of event information tailored to the user's interests. In this way, by linking with a user's social media account, the system can effectively notify the user of related event and facility information.

[0046] The system can analyze the text data of feedback and classify positive and negative opinions. For example, the system analyzes the text data of feedback collected from users and builds a system for classifying positive and negative opinions. For example, the system analyzes the text using natural language processing technology. The system can also analyze the content of the text data. For example, it can classify opinions based on positive or negative expressions. The system can also analyze the fluctuation patterns of the text data. For example, if there are many positive opinions, it can analyze those opinions preferentially. In this way, it is possible to classify positive and negative opinions by analyzing the text data of feedback.

[0047] The system can analyze the voice data of the feedback and identify the intensity and type of emotion. For example, the system is constructed to analyze the voice data of the feedback collected from the user and identify the intensity and type of emotion. For example, the tone and pitch of the voice are analyzed using voice analysis technology. The system can also analyze the content of the voice data. For example, the intensity of the emotion is identified based on the tone and volume of the voice. The system can also analyze the fluctuation pattern of the voice data. For example, if the intensity of the emotion is high, that opinion is analyzed preferentially. In this way, the intensity and type of emotion can be identified by analyzing the voice data of the feedback.

[0048] The system can collect feedback in real time and reflect the analysis results immediately. For example, the system can be constructed to collect feedback from users in real time and reflect the analysis results immediately. For example, analysis can begin as soon as feedback is sent. The system can also analyze fluctuation patterns in the feedback data. For example, if the content of the feedback fluctuates, the fluctuations can be reflected immediately. The system can also display the analysis results of the feedback in real time. For example, the analysis results can be displayed on a dashboard. This allows feedback to be collected in real time and the analysis results to be reflected immediately.

[0049] The system can provide a function to share feedback with other users and gain sympathy and approval. For example, the system builds a system that provides a function to share feedback collected from users with other users and gain sympathy and approval. For example, it provides a function to "like" or comment on feedback. The system can also analyze the content of the feedback and identify feedback that is likely to gain sympathy and approval. For example, it can preferentially share positive feedback. The system can also set the scope of sharing of feedback. For example, it can share feedback only within a specific group. In this way, by sharing feedback with other users, sympathy and approval can be gained.

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

[0051] The congestion status acquisition unit can also analyze lighting data within the facility and estimate the congestion level from changes in illuminance. For example, if the lights within the facility flicker frequently, it can estimate that the area is crowded. It can also estimate that the congestion level is high if the illuminance changes suddenly. Furthermore, it is possible to estimate the congestion level of a specific area by analyzing changes in the color temperature of the lighting. This allows for accurate estimation of the congestion level by analyzing the lighting data.

[0052] The facility search unit can analyze the user's health data and suggest the most suitable facility based on the user's health condition. For example, it can suggest a facility where the user can relax based on the user's heart rate and blood pressure data. It can also suggest sports facilities if the user needs to exercise based on the health data. It can also analyze fluctuation patterns in the health data and dynamically suggest facilities according to the user's health condition. This makes it possible to suggest the most suitable facility based on the user's health condition.

[0053] The congestion status acquisition unit analyzes CO2 concentration data within the facility and can estimate the level of congestion from changes in air quality. For example, if the CO2 concentration is high, it is estimated that the area is congested. It is also possible to predict the level of congestion in a specific area by analyzing the fluctuation pattern of CO2 concentration. Furthermore, by combining and analyzing other air quality data (for example, PM2.5 and VOCs), it is possible to estimate the level of congestion more accurately. This allows for accurate estimation of the level of congestion by analyzing air quality data.

[0054] The facility search unit can analyze the content of a user's social media posts and suggest the most suitable facility based on their interests. For example, if a user has recently posted a lot about "cafes," it will prioritize suggesting cafes. It can also identify the user's interests based on the content of their social media posts and suggest facilities based on those. It can also analyze social media data in real time and dynamically suggest facilities based on the user's latest interests. This makes it possible to suggest the most suitable facility based on the user's interests.

[0055] The facility search unit can analyze the user's past search history and suggest optimal facilities based on the search patterns. For example, it can prioritize suggesting facilities that have been frequently searched for in the past. It can also identify the user's preferences and tendencies based on the search history and suggest facilities based on those. It can also analyze patterns of change in the search history and dynamically suggest facilities based on the user's latest interests. This makes it possible to suggest optimal facilities based on the user's search history.

[0056] The congestion status acquisition unit can analyze temperature data within the facility and estimate the congestion level from temperature changes. For example, if the temperature rises suddenly, it can estimate that the area is congested. It is also possible to predict the congestion level of a specific area by analyzing the temperature fluctuation pattern. Furthermore, by combining and analyzing other environmental data (e.g., humidity and air pressure), it is possible to estimate the congestion level more accurately. This allows for accurate estimation of the congestion level by analyzing the temperature data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The congestion status acquisition unit acquires the congestion status of the facility in real time. For example, it analyzes data obtained from sensors and cameras within the facility and calculates the current congestion level. The congestion status acquisition unit can also analyze the number of people in the facility, the length of their stay, and the frequency of entry and exit. For example, a sensor detects the movement of people within the facility and calculates the congestion level based on that data. Step 2: The facility search unit searches for nearby, unoccupied facilities based on the congestion status acquired by the congestion status acquisition unit from the user's current location. For example, the user's current location is acquired from GPS data, and unoccupied facilities are searched for based on the congestion status of surrounding facilities. The facility search unit can also perform a search taking into account the type of facility desired by the user. For example, if the user desires a shopping mall, unoccupied shopping malls in the vicinity are searched for. Step 3: The listing unit lists the facilities found by the facility search unit. For example, the search results may be displayed in list format to make it easier for the user to select a facility. The listing unit may also include information such as the facility's congestion status, distance, and ratings in the list. For example, the list may display the congestion level, distance, and user ratings for each facility.

[0059] (Example 2) The MachiZero system according to an embodiment of the present invention is a system that grasps the congestion status of facilities in real time and provides users with the most suitable facilities. This allows users to avoid congestion, reduce waiting times, and realize a comfortable outing.

[0060] The MachiZero system according to the embodiment includes a congestion status acquisition unit, a facility search unit, and a listing unit. The congestion status acquisition unit acquires the congestion status of a facility in real time. For example, it analyzes data obtained from sensors and cameras within the facility to calculate the current congestion level. The congestion status acquisition unit can also analyze the number of people in the facility, the length of their stay, and the frequency of entry and exit. For example, a sensor detects people's movements within the facility and calculates the congestion level based on that data. The facility search unit searches for available facilities near the user's current location based on the congestion status acquired by the congestion status acquisition unit. For example, it acquires the user's current location from GPS data and searches for available facilities based on the congestion status of surrounding facilities. The facility search unit can also perform a search taking into account the type of facility desired by the user. For example, if the user desires a shopping mall, it searches for available shopping malls in the vicinity. The listing unit lists the facilities found by the facility search unit. For example, it displays the search results in a list format to make it easier for the user to select a facility. The listing unit can also include information such as the facility's congestion status, distance, and rating in the list. For example, the list displays the congestion level, distance, and user ratings of each facility. This allows the MachiZero system according to the embodiment to grasp the congestion status of facilities in real time and provide users with the most suitable facilities. For example, this allows users to avoid congestion and use facilities comfortably. It also makes it easier for users to select facilities based on the list.

[0061] The congestion status acquisition unit can analyze audio data within the facility and estimate the degree of congestion from the noise level. The congestion status acquisition unit, for example, analyzes audio data collected from microphones installed within the facility and measures the noise level. For example, it analyzes the audio data in real time and estimates that the degree of congestion is high if the noise level is high. The congestion status acquisition unit can also analyze the frequency and volume of the audio data. For example, if the volume of a specific frequency band is high, it estimates that the area is congested. The congestion status acquisition unit can also analyze the fluctuation pattern of the audio data. For example, if the audio data fluctuates significantly, it estimates that the area is congested. In this way, the degree of congestion can be accurately estimated by analyzing the audio data.

[0062] The congestion status acquisition unit can analyze data from temperature and humidity sensors and estimate the congestion level from environmental changes. The congestion status acquisition unit, for example, analyzes data collected from temperature and humidity sensors installed within the facility to monitor environmental changes. For example, if the temperature or humidity rises rapidly, it estimates that the congestion level is high. The congestion status acquisition unit can also analyze temperature and humidity fluctuation patterns. For example, if the temperature or humidity fluctuates drastically, it estimates that the area is congested. The congestion status acquisition unit can also predict the congestion level based on temperature and humidity data. For example, it can build a congestion level prediction model based on past data and predict the congestion level based on current data. This allows the congestion level to be accurately estimated by analyzing temperature and humidity data.

[0063] The congestion status acquisition unit can use an emotion estimation function to analyze the emotional state within the facility and estimate the congestion level from the stress level. The congestion status acquisition unit, for example, analyzes data collected from cameras and microphones installed within the facility and estimates the emotional state from people's facial expressions and tone of voice. For example, if emotions of stress or anxiety are strong, it estimates that the congestion level is high. The congestion status acquisition unit can also analyze the emotional state using an emotion estimation algorithm. For example, it can analyze people's facial expressions using facial expression recognition technology to estimate the emotional state. The congestion status acquisition unit can also analyze voice tone and pitch using voice analysis technology to estimate the emotional state. For example, it can estimate that a high voice tone indicates high stress. In this way, the congestion level can be accurately estimated by analyzing the emotional state.

[0064] The congestion status acquisition unit can analyze traffic condition data outside the facility and associate it with the congestion level. The congestion status acquisition unit, for example, collects traffic condition data around the facility and analyzes traffic volume and congestion status. For example, if traffic volume is high, it estimates that the facility is highly congested. The congestion status acquisition unit can also monitor traffic conditions using traffic sensors and GPS data. For example, a traffic sensor detects the passage of a vehicle and calculates traffic volume based on that data. The congestion status acquisition unit can also analyze fluctuation patterns in the traffic condition data. For example, if traffic volume fluctuates significantly, it estimates that the area is congested. In this way, the congestion level of the facility can be accurately associated by analyzing the traffic condition data.

[0065] The congestion status acquisition unit can analyze the number of Wi-Fi connections and estimate the congestion level from the number of connected devices. The congestion status acquisition unit, for example, analyzes the number of connected devices collected from Wi-Fi access points within a facility and estimates the congestion level. For example, if the number of connected devices is large, it is estimated that the congestion level is high. The congestion status acquisition unit can also analyze fluctuation patterns in the number of Wi-Fi connections. For example, if there is a large fluctuation in the number of connected devices, it is estimated that the area is congested. The congestion status acquisition unit can also analyze the type and connection time of connected devices. For example, if there is a large number of smartphone and tablet connections, it is estimated that the area is congested. In this way, the congestion level can be accurately estimated by analyzing the number of Wi-Fi connections.

[0066] The congestion status acquisition unit can use the emotion estimation function to monitor the emotional state within the facility in real time and predict the level of congestion. The congestion status acquisition unit, for example, analyzes data collected from cameras and microphones installed within the facility and monitors the emotional state in real time from people's facial expressions and tone of voice. For example, if emotions of stress or anxiety are strong, it predicts that the level of congestion is high. The congestion status acquisition unit can also analyze the emotional state in real time using an emotion estimation algorithm. For example, it can use facial expression recognition technology to analyze people's facial expressions in real time and monitor their emotional state. The congestion status acquisition unit can also use voice analysis technology to analyze voice tone and pitch in real time and monitor their emotional state. For example, it can predict that a high voice tone indicates high stress. In this way, by monitoring the emotional state in real time, it is possible to accurately predict the level of congestion.

[0067] The facility search unit can analyze the user's visit history and prioritize listing favorite facilities. The facility search unit, for example, analyzes the user's past visit history and identifies favorite facilities. For example, prioritize listing facilities that the user has frequently visited in the past or highly rated facilities. The facility search unit can also analyze the type and characteristics of facilities based on the user's visit history. For example, the user can identify favorite facilities based on the type and characteristics of facilities that the user has visited in the past. The facility search unit can also analyze facility ratings based on the user's visit history. For example, prioritize listing facilities that the user has highly rated. In this way, favorite facilities can be prioritized by analyzing the user's past visit history.

[0068] The facility search unit can analyze facility reviews and ratings and prioritize listing highly rated facilities. The facility search unit, for example, collects and analyzes facility review and rating data. For example, it prioritizes listing highly rated facilities. The facility search unit can also analyze the content of reviews and ratings. For example, it identifies highly rated facilities based on the content of reviews and rating scores. The facility search unit can also analyze fluctuation patterns in reviews and ratings. For example, it prioritizes listing facilities with little fluctuation in rating scores. In this way, by analyzing reviews and ratings, it is possible to prioritize listing highly rated facilities.

[0069] The facility search unit can use the emotion estimation function to suggest the most suitable facility based on the user's emotional state. For example, the facility search unit analyzes the user's current emotional state in real time and suggests the most suitable facility based on that emotion. For example, if the user wants to relax, it can suggest a quiet cafe. The facility search unit can also analyze the emotional state using an emotion estimation algorithm. For example, it can use facial expression recognition technology to analyze the user's facial expression and estimate the emotional state. The facility search unit can also use voice analysis technology to analyze the tone and pitch of the voice and estimate the emotional state. For example, it can estimate that a high voice tone indicates high stress. This makes it possible to suggest the most suitable facility based on the user's emotional state.

[0070] The facility search unit can analyze facility benefits and discount information and list the most cost-effective facilities for the user. The facility search unit, for example, collects and analyzes benefit and discount information provided by facilities. For example, it prioritizes listing facilities with high discount rates. The facility search unit can also analyze the content of benefit and discount information. For example, it identifies the most cost-effective facility based on the content of benefit and discount rate. The facility search unit can also analyze fluctuation patterns in benefit and discount information. For example, it prioritizes listing facilities with least fluctuation in benefit and discount information. In this way, by analyzing benefit and discount information, it is possible to list the most cost-effective facilities for the user.

[0071] The facility search unit can analyze facility business hours and special event information, and list facilities that can be visited at the optimal time for the user. The facility search unit, for example, collects and analyzes facility business hours and special event information. For example, it prioritizes listing facilities with long business hours or facilities that hold special events. The facility search unit can also analyze the content of business hours and special event information. For example, it identifies facilities that can be visited at the optimal time based on the length of business hours and the content of the event. The facility search unit can also analyze fluctuation patterns in business hours and special event information. For example, it prioritizes listing facilities with little fluctuation in business hours. In this way, by analyzing business hours and special event information, it is possible to list facilities that can be visited at the optimal time for the user.

[0072] The facility search unit can use the emotion estimation function to dynamically change the order of listed facilities based on the user's emotional state. The facility search unit, for example, analyzes the user's emotional state in real time and dynamically changes the order of listed facilities based on the emotion. For example, if the user wants to relax, quiet facilities are displayed at the top. The facility search unit can also analyze the emotional state using an emotion estimation algorithm. For example, facial expression recognition technology can be used to analyze the user's facial expression and estimate the emotional state. The facility search unit can also use voice analysis technology to analyze the tone and pitch of the voice and estimate the emotional state. For example, a high-pitched voice can be estimated to indicate high stress. This allows the order of listed facilities to be dynamically changed based on the user's emotional state.

[0073] The system can work in conjunction with a reservation system to predict reservation cancellation rates and change frequency. For example, the system collects data from the reservation system and analyzes past reservation cancellation rates and change frequency. For example, if there are many cancellations during a particular time period or day of the week, the system can predict that trend. The system can also analyze fluctuation patterns in reservation data. For example, if there are large fluctuations in cancellation rates or change frequency, the system can identify those time periods or days of the week. The system can also predict cancellation rates and change frequency using a predictive algorithm. For example, a predictive model can be built based on past data and predictions can be made based on current data. This makes reservation management more efficient by predicting reservation cancellation rates and change frequency.

[0074] The system can work in conjunction with a reservation system to suggest the optimal reservation time slot. For example, the system collects data from the reservation system and analyzes past reservation status. For example, it identifies a tendency for reservations to be concentrated during certain time periods or days of the week, and suggests the optimal reservation time slot. The system can also analyze fluctuation patterns in reservation data. For example, it can identify time periods or days of the week with fewer reservations, and suggest those time slots. The system can also suggest reservation time slots taking into account the user's desired time and the availability of the facility. For example, it can suggest the optimal reservation time slot based on the user's desired time. This improves user convenience by suggesting the optimal reservation time slot.

[0075] The system can use an emotion estimation function to suggest an optimal reservation time based on the user's emotional state. For example, the system analyzes the user's emotional state in real time and suggests an optimal reservation time based on that emotion. For example, if the user wants to relax, the system can suggest a quiet time slot. The system can also analyze the emotional state using an emotion estimation algorithm. For example, facial expression recognition technology can be used to analyze the user's facial expression to estimate the emotional state. The system can also use voice analysis technology to analyze the tone and pitch of the voice to estimate the emotional state. For example, if the voice tone is high, it can be estimated that the user is stressed. This allows the system to suggest an optimal reservation time based on the user's emotional state.

[0076] The system can link with a reservation system to provide the function of managing reservations for multiple facilities in one place. For example, the system can link with a reservation system to build a system that manages reservations for multiple facilities in one place. For example, reservations for restaurants, hospitals, sports facilities, etc. can be managed on a single platform. The system can also integrate reservation data and manage it all together on a management screen. For example, data from multiple reservation systems can be integrated and managed centrally on a management screen. The system can also change or cancel reservations in one place. For example, reservations for multiple facilities can be changed or canceled in one place. This makes reservation management more efficient by managing reservations for multiple facilities in one place.

[0077] The system can work in conjunction with a reservation system and automatically adjust the optimal reservation time in conjunction with the user's schedule. For example, the system can work in conjunction with a reservation system to build a system that analyzes the user's schedule and automatically adjusts the optimal reservation time. For example, the system can adjust the reservation time in conjunction with a calendar app. The system can also automatically adjust the reservation time based on schedule data. For example, the system optimizes the reservation time based on the user's schedule. The system can also automatically readjust the reservation time in response to changes in the schedule. For example, if the schedule changes, the reservation time is automatically readjusted. This makes it possible to automatically adjust the optimal reservation time based on the user's schedule.

[0078] The system can use an emotion estimation function to take into account the user's emotional state at the time of reservation and provide a less stressful reservation process. For example, the system can use the emotion estimation function to analyze the user's emotional state at the time of reservation in real time and provide a less stressful reservation process. For example, the system can enable the user to complete the reservation in a relaxed state. The system can also analyze the emotional state using an emotion estimation algorithm. For example, facial expression recognition technology can be used to analyze the user's facial expressions and estimate the emotional state. The system can also use voice analysis technology to analyze the tone and pitch of the voice and estimate the emotional state. For example, a high-pitched voice tone can be estimated to indicate high stress. This makes it possible to provide a less stressful reservation process based on the user's emotional state.

[0079] The system can analyze the user's location information and calculate the optimal notification timing. For example, the system can be constructed to analyze the user's location information in real time and calculate the optimal notification timing. For example, a notification is sent when the user approaches a facility. The system can also analyze the fluctuation pattern of the location information. For example, the notification timing is calculated based on the user's movement speed and direction. The system can also analyze the user's behavioral patterns and identify the optimal notification timing. For example, if the user is in a specific location at a specific time period, the notification is sent during that time period. This makes it possible to calculate the optimal notification timing based on the user's location information.

[0080] The system can analyze a user's device usage patterns and select the most effective notification method. For example, the system can analyze a user's device usage patterns and build a system that selects the most effective notification method. For example, the system can analyze the frequency of smartphone use and notification reception status. The system can also analyze fluctuations in device usage patterns. For example, if device usage is high during a specific time period, notifications can be sent during that time period. The system can also select the notification method based on the user's preferences. For example, the system can select the optimal method from push notifications, email notifications, SMS notifications, etc. This allows the system to select the most effective notification method based on the user's device usage patterns.

[0081] The system can use an emotion estimation function to customize notification content based on the user's emotional state. For example, the system uses the emotion estimation function to analyze the user's emotional state in real time and customize notification content based on that emotion. For example, if the user wants to relax, the system can notify the user of information about relaxation facilities. The system can also analyze the emotional state using an emotion estimation algorithm. For example, the system can use facial expression recognition technology to analyze the user's facial expressions and estimate the emotional state. The system can also use voice analysis technology to analyze the tone and pitch of the voice and estimate the emotional state. For example, if the voice tone is high, the system can estimate that the user is under high stress. This allows the system to customize notification content based on the user's emotional state.

[0082] The system can work in conjunction with a user's calendar app to provide notifications based on a schedule. For example, the system can work in conjunction with a user's calendar app to build a system that provides notifications based on a schedule. For example, a notification can be sent just before an appointment. The system can also adjust the timing of notifications based on data from the calendar app. For example, a notification can be sent to coincide with the start time of an appointment. The system can also analyze data from the calendar app to identify the optimal timing of notifications. For example, the notification timing can be adjusted in response to changes in the appointment. In this way, the timing of notifications can be optimized by providing notifications based on the user's schedule.

[0083] The system can link with a user's social media account to notify the user of related event and facility information. For example, the system can link with a user's social media account to build a system that notifies the user of related event and facility information. For example, the system can notify the user of information about events that the user is interested in. The system can also analyze social media data to identify the user's interests. For example, the system can identify interests based on the accounts the user follows and the content of posts. The system can also customize the content of notifications based on social media data. For example, the system can notify the user of event information tailored to the user's interests. In this way, by linking with a user's social media account, the system can effectively notify the user of related event and facility information.

[0084] The system can use an emotion estimation function to adjust the frequency and timing of notifications based on the user's emotional state. For example, the system uses the emotion estimation function to analyze the user's emotional state in real time and adjust the frequency and timing of notifications based on that emotion. For example, if the user is feeling stressed, the system can reduce the frequency of notifications. The system can also analyze the emotional state using an emotion estimation algorithm. For example, facial expression recognition technology can be used to analyze the user's facial expressions to estimate the emotional state. The system can also use voice analysis technology to analyze the tone and pitch of the voice to estimate the emotional state. For example, a high-pitched voice can be estimated to indicate high stress. This allows the system to adjust the frequency and timing of notifications based on the user's emotional state.

[0085] The system can analyze the text data of feedback and classify positive and negative opinions. For example, the system analyzes the text data of feedback collected from users and builds a system for classifying positive and negative opinions. For example, the system analyzes the text using natural language processing technology. The system can also analyze the content of the text data. For example, it can classify opinions based on positive or negative expressions. The system can also analyze the fluctuation patterns of the text data. For example, if there are many positive opinions, it can analyze those opinions preferentially. In this way, it is possible to classify positive and negative opinions by analyzing the text data of feedback.

[0086] The system can analyze the voice data of the feedback and identify the intensity and type of emotion. For example, the system is constructed to analyze the voice data of the feedback collected from the user and identify the intensity and type of emotion. For example, the tone and pitch of the voice are analyzed using voice analysis technology. The system can also analyze the content of the voice data. For example, the intensity of the emotion is identified based on the tone and volume of the voice. The system can also analyze the fluctuation pattern of the voice data. For example, if the intensity of the emotion is high, that opinion is analyzed preferentially. In this way, the intensity and type of emotion can be identified by analyzing the voice data of the feedback.

[0087] The system can use the emotion estimation function to analyze the emotional state of feedback and determine priorities for service improvements. For example, the system uses the emotion estimation function to analyze the emotional state of feedback collected from users and build a system that determines priorities for service improvements. For example, feedback with a high level of positive emotions is analyzed preferentially. The system can also analyze the emotional state using an emotion estimation algorithm. For example, facial expression recognition technology is used to analyze the user's facial expressions and estimate the emotional state. The system can also analyze voice tone and pitch using voice analysis technology to estimate the emotional state. For example, a high-pitched voice tone is estimated to indicate a strong positive emotion. In this way, the emotional state of feedback can be analyzed to determine priorities for service improvements.

[0088] The system can collect feedback in real time and reflect the analysis results immediately. For example, the system can be constructed to collect feedback from users in real time and reflect the analysis results immediately. For example, analysis can begin as soon as feedback is sent. The system can also analyze fluctuation patterns in the feedback data. For example, if the content of the feedback fluctuates, the fluctuations can be reflected immediately. The system can also display the analysis results of the feedback in real time. For example, the analysis results can be displayed on a dashboard. This allows feedback to be collected in real time and the analysis results to be reflected immediately.

[0089] The system can provide a function to share feedback with other users and gain sympathy and approval. For example, the system builds a system that provides a function to share feedback collected from users with other users and gain sympathy and approval. For example, it provides a function to "like" or comment on feedback. The system can also analyze the content of the feedback and identify feedback that is likely to gain sympathy and approval. For example, it can preferentially share positive feedback. The system can also set the scope of sharing of feedback. For example, it can share feedback only within a specific group. In this way, by sharing feedback with other users, sympathy and approval can be gained.

[0090] The system can use the emotion estimation function to analyze the emotional state of feedback and provide incentives to encourage positive feedback. For example, the system uses the emotion estimation function to analyze the emotional state of feedback collected from users and builds a system that provides incentives to encourage positive feedback. For example, points are awarded to users who send positive feedback. The system can also analyze the emotional state using an emotion estimation algorithm. For example, facial expression recognition technology is used to analyze the user's facial expression and estimate the emotional state. The system can also analyze the tone and pitch of a voice using voice analysis technology to estimate the emotional state. For example, a high-pitched voice tone is estimated to indicate a strong positive emotion. This makes it possible to improve user satisfaction by providing incentives to encourage positive feedback.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The congestion status acquisition unit can also analyze lighting data within the facility and estimate the congestion level from changes in illuminance. For example, if the lights within the facility flicker frequently, it can estimate that the area is crowded. It can also estimate that the congestion level is high if the illuminance changes suddenly. Furthermore, it is possible to estimate the congestion level of a specific area by analyzing changes in the color temperature of the lighting. This allows for accurate estimation of the congestion level by analyzing the lighting data.

[0093] The facility search unit can analyze the user's health data and suggest the most suitable facility based on the user's health condition. For example, it can suggest a facility where the user can relax based on the user's heart rate and blood pressure data. It can also suggest sports facilities if the user needs to exercise based on the health data. It can also analyze fluctuation patterns in the health data and dynamically suggest facilities according to the user's health condition. This makes it possible to suggest the most suitable facility based on the user's health condition.

[0094] The congestion status acquisition unit analyzes CO2 concentration data within the facility and can estimate the level of congestion from changes in air quality. For example, if the CO2 concentration is high, it is estimated that the area is congested. It is also possible to predict the level of congestion in a specific area by analyzing the fluctuation pattern of CO2 concentration. Furthermore, by combining and analyzing other air quality data (for example, PM2.5 and VOCs), it is possible to estimate the level of congestion more accurately. This allows for accurate estimation of the level of congestion by analyzing air quality data.

[0095] The facility search unit can analyze the content of a user's social media posts and suggest the most suitable facility based on their interests. For example, if a user has recently posted a lot about "cafes," it will prioritize suggesting cafes. It can also identify the user's interests based on the content of their social media posts and suggest facilities based on those. It can also analyze social media data in real time and dynamically suggest facilities based on the user's latest interests. This makes it possible to suggest the most suitable facility based on the user's interests.

[0096] The facility search unit can analyze the user's past search history and suggest optimal facilities based on the search patterns. For example, it can prioritize suggesting facilities that have been frequently searched for in the past. It can also identify the user's preferences and tendencies based on the search history and suggest facilities based on those. It can also analyze patterns of change in the search history and dynamically suggest facilities based on the user's latest interests. This makes it possible to suggest optimal facilities based on the user's search history.

[0097] The congestion status acquisition unit can analyze temperature data within the facility and estimate the congestion level from temperature changes. For example, if the temperature rises suddenly, it can estimate that the area is congested. It is also possible to predict the congestion level of a specific area by analyzing the temperature fluctuation pattern. Furthermore, by combining and analyzing other environmental data (e.g., humidity and air pressure), it is possible to estimate the congestion level more accurately. This allows for accurate estimation of the congestion level by analyzing the temperature data.

[0098] The facility search unit can dynamically change the order of listed facilities based on the user's emotional state. For example, if the user wants to relax, quiet facilities will be displayed at the top. It can also analyze the user's emotional state using an emotion estimation algorithm and change the order of facilities based on that emotion. It can also monitor the user's emotional state in real time and dynamically change the order of facilities based on the user's latest emotion. This allows the system to suggest optimal facilities based on the user's emotional state.

[0099] The facility search unit can analyze facility reviews and ratings based on the user's emotional state and suggest the most suitable facility. For example, if the user is feeling stressed, it can prioritize highly rated reviews of relaxing facilities. It can also analyze the user's emotional state using an emotion estimation algorithm and filter reviews and ratings based on that emotion. It can also monitor the user's emotional state in real time and dynamically analyze reviews and ratings based on the latest emotions. This allows it to suggest the most suitable facility based on the user's emotional state.

[0100] The facility search unit can analyze facility benefits and discount information based on the user's emotional state and suggest the most suitable facility. For example, if the user wants to relax, it can prioritize displaying benefit information for relaxation facilities. It can also analyze the user's emotional state using an emotion estimation algorithm and filter benefit and discount information based on that emotion. It can also monitor the user's emotional state in real time and dynamically analyze benefit and discount information based on the user's latest emotion. This allows it to suggest the most suitable facility based on the user's emotional state.

[0101] The facility search unit can analyze facility opening hours and special event information based on the user's emotional state and suggest the most suitable facility. For example, if the user wants to relax, facilities that are open during quiet hours can be displayed preferentially. It can also analyze the user's emotional state using an emotion estimation algorithm and filter opening hours and special event information based on that emotion. Furthermore, it can monitor the user's emotional state in real time and dynamically analyze opening hours and special event information based on the user's latest emotion. This allows the system to suggest the most suitable facility based on the user's emotional state.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The congestion status acquisition unit acquires the congestion status of the facility in real time. For example, it analyzes data obtained from sensors and cameras within the facility and calculates the current congestion level. The congestion status acquisition unit can also analyze the number of people in the facility, the length of their stay, and the frequency of entry and exit. For example, a sensor detects the movement of people within the facility and calculates the congestion level based on that data. Step 2: The facility search unit searches for nearby, unoccupied facilities based on the congestion status acquired by the congestion status acquisition unit from the user's current location. For example, the user's current location is acquired from GPS data, and unoccupied facilities are searched for based on the congestion status of surrounding facilities. The facility search unit can also perform a search taking into account the type of facility desired by the user. For example, if the user desires a shopping mall, unoccupied shopping malls in the vicinity are searched for. Step 3: The listing unit lists the facilities found by the facility search unit. For example, the search results may be displayed in list format to make it easier for the user to select a facility. The listing unit may also include information such as the facility's congestion status, distance, and ratings in the list. For example, the list may display the congestion level, distance, and user ratings for each facility.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a congestion status acquisition unit that acquires the congestion status of the facility in real time; a facility search unit that searches for nearby vacant facilities from the user's current location based on the congestion status acquired by the congestion status acquisition unit; a listing unit that lists the facilities searched by the facility search unit; A system characterized by:

2. The congestion status acquisition unit Analyzing traffic data outside the facility and correlating it with congestion levels 2. The system of claim 1.

3. The facility search unit Analyze the user's visit history and prioritize the list of preferred facilities.

2. The system of claim 1.

4. The system comprises: Link with reservation systems to predict cancellation rates and change frequency 2. The system of claim 1.

5. The system comprises: Analyze the user's location information and calculate the optimal notification timing 2. The system of claim 1.

6. The congestion status acquisition unit Analyzing the emotional state within a facility and estimating congestion levels based on stress levels 2. The system of claim 1.

7. The facility search unit Suggesting optimal facilities based on the user's emotional state 2. The system of claim 1.

8. The system comprises: Consider the user's emotional state when booking to provide a less stressful booking process 2. The system of claim 1.

Images