system

Abstract

The system according to this embodiment aims to search for and provide nearby available facilities in real time. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit receives user conditions as input. The analysis unit searches for nearby available facilities in real time based on the conditions entered by the collection unit. The provision unit provides the search results obtained by the analysis unit in a list format.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is no means to accurately grasp the congestion situation of facilities, and there is a risk of long waiting times.

[0005] The system according to the embodiment aims to search for and provide nearby available facilities in real time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit inputs the conditions of the user. The analysis unit searches for nearby available facilities in real time based on the conditions input by the collection unit. The provision unit lists and provides the search results obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can search for and provide nearby available facilities in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The zero-waiting system according to an embodiment of the present invention is a system that uses AI to eliminate waiting times at facilities. The zero-waiting system works by having the user input conditions, and the AI ​​then searches for and lists available nearby facilities in real time. This allows users to avoid crowds, reduce waiting times, and enjoy a comfortable outing. First, the user inputs conditions. Next, the AI ​​searches for available nearby facilities in real time based on the input conditions. During this process, it analyzes congestion and reservation status using information from people-counting sensors installed at facilities and reservation websites. The search results are compiled into a list and provided to the user. Furthermore, directions avoiding crowded areas are also provided. This mechanism allows users to reduce waiting times and enjoy a comfortable outing. For example, when looking for a place for a second round of drinks on a Friday night, a list of matching establishments is generated, allowing the user to choose a less crowded place. Similarly, when searching for a less crowded restaurant during the Obon holiday period, the system searches for available restaurants in real time, allowing the user to eat immediately without waiting in line. The zero-waiting system aims to increase revenue by increasing the number of facility users, and generates revenue by charging facilities a monthly usage fee. Furthermore, the system reduces the burden on facilities by providing free sensor installation. The target audience includes general consumers who want to avoid crowded restaurants on the eve of holidays, and facility operators who face the problem of congestion concentrating in specific locations. This system is realized by combining generative AI, image analysis AI, and numerical processing AI. By distributing people-counting sensors to facilities in advance and analyzing congestion and reservation status from the people-counting sensors and reservation site information, it enables real-time searching of nearby available facilities. As a result, the zero-waiting system can reduce waiting times and enable comfortable outings by searching for nearby available facilities in real time based on the user's conditions and providing a list.

[0029] The zero-waiting-time system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit inputs user conditions. The collection unit can input conditions such as distance, number of people, priority conditions, genre, mode of transportation, desired genre, and whether or not there are children. The collection unit can input conditions using, for example, a smartphone or personal computer. The collection unit can also input conditions using, for example, voice input or text input. The analysis unit searches for nearby available facilities in real time based on the conditions input by the collection unit. The analysis unit analyzes congestion and reservation status using, for example, people measurement sensors installed in facilities and information from reservation sites. The analysis unit can grasp the congestion status of facilities in real time based on data obtained from people measurement sensors. The analysis unit can grasp the reservation status of facilities in real time based on information from reservation sites. The provision unit provides the search results obtained by the analysis unit in a list. The provision unit can provide the search results to the user in a list. The provision unit can display the search results in a list on the screen of a smartphone or personal computer. The service provider can, for example, create a list of search results and send it to the user via email. This allows the zero-waiting system according to the embodiment to reduce waiting times and enable a comfortable outing by searching for and listing available nearby facilities in real time based on the user's criteria.

[0030] The data collection unit inputs user criteria. These criteria can include, for example, distance, number of people, priority conditions, genre, mode of transport, desired genre, and whether or not the user has children. Specifically, users access a dedicated application or website using their smartphone or personal computer and enter the necessary criteria into an input form. The data collection unit can also accept criteria using voice input or text input. With voice input, the user simply speaks the criteria into a microphone, and the system uses speech recognition technology to convert it into text and automatically analyze the input. With text input, the user enters the criteria using a keyboard or touchscreen. Furthermore, the data collection unit also has a function to assist with criterion input by referring to the user's past input history and behavioral history. For example, it can suggest criteria suitable for the user based on previously used facilities and criteria. This allows the data collection unit to enable users to input criteria easily and quickly, improving the overall usability of the system.

[0031] The analysis department searches for available nearby facilities in real time based on the conditions entered by the data collection department. The analysis department analyzes congestion and reservation status using, for example, people counting sensors installed in facilities and information from reservation websites. Specifically, people counting sensors are installed at the entrance and main areas of facilities to count the number of people entering and leaving. This allows for real-time tracking of the current number of people inside the facility. Information from reservation websites provides information on the reservation and availability status of facilities, and by integrating and analyzing this data, the congestion status of facilities can be accurately grasped. For example, the analysis department can grasp the congestion status of facilities in real time based on data obtained from people counting sensors. Furthermore, by utilizing AI-based data analysis technology, it can also predict future congestion based on past data and trends. This allows for the suggestion of the most suitable facility according to the time of day and day of the week the user visits. For example, the analysis department can grasp the reservation status of facilities in real time based on information from reservation websites. This allows for the quick discovery of facilities that match the user's desired conditions, minimizing waiting times.

[0032] The service provider provides the search results obtained by the analysis department in a list format. For example, the service provider can provide the user with a list of search results. Specifically, it can prioritize the search results based on the user's criteria and display the most suitable facilities at the top. For example, the service provider can display the search results in a list format on the screen of a smartphone or personal computer. The user can scroll through the list on the screen to check detailed information and congestion status for each facility. For example, the service provider can also send the search results in a list format to the user via email. This allows the user to check the search results even when on the go, improving convenience. Furthermore, the service provider also has a function to display the search results on a map, allowing the user to check the location of facilities and surrounding conditions while viewing the map. This allows the user to quickly find the most suitable facility and reduce waiting times. The service provider can also collect user feedback and continuously improve the accuracy and method of providing search results. For example, by collecting ratings and comments on facilities that users have actually visited and reflecting them in the next search results, the service provider can provide more accurate information. In this way, the service provider can always provide users with the latest and most suitable information, supporting a comfortable outing.

[0033] The data collection unit allows users to input conditions such as distance, number of people, priority criteria, genre, mode of transport, desired genre, and whether or not there are children. The data collection unit allows users to input conditions using, for example, a smartphone or personal computer. The data collection unit also allows users to input conditions using, for example, voice input or text input. The data collection unit allows users to input distance in kilometers or miles. The data collection unit allows users to input the number of people as a specific numerical value. The data collection unit allows users to select priority criteria from a list of options. This allows users to search for more suitable facilities by inputting detailed conditions.

[0034] The analysis unit can analyze congestion and reservation status using people-counting sensors installed at facilities and information from reservation websites. For example, the analysis unit can grasp the congestion status of a facility in real time based on data obtained from people-counting sensors. For example, the analysis unit can grasp the reservation status of a facility in real time based on information from reservation websites. For example, the analysis unit can measure the number of people using camera-based sensors or infrared sensors. For example, the analysis unit can analyze the congestion status of a facility using reservation status and cancellation information from reservation websites. This allows for accurate analysis of facility congestion and reservation status, enabling the provision of appropriate facilities to users.

[0035] The service provider can provide search results to users in a list format. For example, the service provider can display the search results in a list format on the screen of a smartphone or personal computer. For example, the service provider can send the search results in a list format to users via email. For example, the service provider can provide the search results in a list format that is easy for users to select from. For example, the service provider can customize the order of the list and the displayed items to the user's preferences. This makes it easier for users to select results by providing them in a list format.

[0036] The service provider can provide directions that avoid crowded areas. For example, the service provider can display directions that avoid crowded areas on the screen of a smartphone or personal computer. For example, the service provider can send directions that avoid crowded areas to the user via email. For example, the service provider can provide directions that avoid crowded areas based on a data source of congestion conditions. For example, the service provider can provide directions that avoid crowded areas using a method for calculating alternative routes. By providing directions that avoid crowded areas, users can travel more comfortably.

[0037] The data collection unit can analyze past condition input history and automatically suggest conditions that are easy for the user to input. For example, the data collection unit can automatically display conditions that the user has frequently entered in the past as suggestions. For example, the data collection unit can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the data collection unit can predict and suggest conditions that the user will use during a specific time period based on their past input history. In this way, by analyzing past condition input history, it is possible to automatically suggest conditions that are easy for the user to input.

[0038] The data collection unit can automatically complete the optimal conditions when conditions are entered, taking into account the user's current location and past activity history. For example, when a user opens the app, the data collection unit can automatically acquire the user's current location and set it as a condition. For example, when a user enters conditions, the data collection unit can suggest the best options by considering the distance from the user's current location. For example, when a user uses the app while on the move, the data collection unit can update the user's current location in real time and reflect it as a condition. This allows the system to automatically complete the optimal conditions by taking into account the user's current location and past activity history.

[0039] The data collection unit can analyze the user's social media activity when conditions are entered and automatically suggest relevant conditions. For example, the data collection unit can suggest relevant conditions based on the locations the user has checked into on social media. For example, the data collection unit can analyze the content of posts the user has shared on social media and suggest relevant conditions. For example, the data collection unit can suggest relevant conditions based on information about accounts the user follows on social media. In this way, relevant conditions can be automatically suggested by analyzing the user's social media activity.

[0040] The data collection unit can provide the optimal input method by considering the user's device information when conditions are entered. For example, if the user is using a smartphone, the data collection unit can provide an input method that matches the screen size. For example, if the user is using a tablet, the data collection unit can provide an input method optimized for a large screen. For example, if the user is using a smartwatch, the data collection unit can provide a concise and highly visible input method. In this way, the optimal input method can be provided by considering the user's device information.

[0041] The analysis unit can predict current congestion levels by referring to past congestion data during analysis. For example, the analysis unit can predict current congestion levels based on past congestion data. For example, the analysis unit can predict congestion levels by combining past congestion data with current weather information. For example, the analysis unit can predict congestion levels by combining past congestion data with current event information. This allows for the prediction of current congestion levels by referring to past congestion data.

[0042] The analysis department can apply different analytical methods to each facility category during analysis. For example, in the case of a restaurant, the analysis department can analyze based on reservation status and current number of customers. For example, in the case of a shopping mall, the analysis department can comprehensively analyze the congestion status of each store. For example, in the case of a public facility, the analysis department can analyze based on user inflow and outflow data. By applying different analytical methods to each facility category, more accurate analysis becomes possible.

[0043] The analysis unit can select the optimal facility by considering the geographical distribution of facilities during analysis. For example, the analysis unit can prioritize selecting the facility closest to the user's current location. For example, the analysis unit can select a facility along the user's travel route. For example, the analysis unit can select a facility close to the user's destination. In this way, the optimal facility can be selected by considering the geographical distribution of facilities.

[0044] The analysis department can improve the accuracy of its analysis by referring to relevant literature and reviews of the facility during the analysis process. For example, the analysis department can predict congestion levels based on facility reviews. For example, the analysis department can analyze the causes of congestion by referring to relevant literature of the facility. For example, the analysis department can improve the accuracy of its analysis by combining facility reviews and relevant literature. This means that the accuracy of the analysis can be improved by referring to relevant literature and reviews of the facility.

[0045] The service provider can generate an optimal list by referring to the user's past selection history at the time of service provision. For example, the service provider can generate an optimal list based on the facilities the user has previously selected. For example, the service provider can list facilities to avoid congestion from the user's past selection history. For example, the service provider can analyze the user's past selection history and generate the most efficient list. In this way, the optimal list can be generated by referring to the user's past selection history.

[0046] The service provider can list the most suitable facilities at the time of service provision, taking into account the user's current location information. For example, the service provider can list the facilities closest to the user's current location. For example, the service provider can list facilities along the user's travel route. For example, the service provider can list facilities close to the user's destination. In this way, the service provider can list the most suitable facilities by taking into account the user's current location information.

[0047] The service provider can analyze the user's social media activity and list relevant facilities at the time of service provision. For example, the service provider can list relevant facilities based on places the user has checked in to on social media. For example, the service provider can list relevant facilities based on posts the user has shared on social media. For example, the service provider can list relevant facilities based on information about accounts the user follows on social media. In this way, relevant facilities can be listed by analyzing the user's social media activity.

[0048] The service provider can provide the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by considering the user's device information.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The zero-waiting system can further analyze users' past behavior patterns and suggest facilities based on those patterns. For example, it can suggest similar facilities based on data from facilities the user has visited in the past. It can also suggest facilities suitable for a particular time slot if the user tends to visit specific facilities at that time. Furthermore, if a user tends to visit specific facilities during certain events or seasons, it can suggest facilities suitable for those events or seasons. This allows for a more comfortable outing by suggesting optimal facilities based on the user's behavior patterns.

[0051] The zero-waiting system can further learn user preferences and tastes, and suggest facilities based on those preferences. For example, if a user prefers a particular type of restaurant, it can prioritize suggesting restaurants of that type. Similarly, if a user prefers a particular activity, it can suggest facilities offering that activity. Furthermore, if a user prefers a specific brand or chain store, it can suggest facilities of that brand or chain store. This allows for a more comfortable outing by suggesting the optimal facility based on the user's preferences and tastes.

[0052] The zero-waiting system can further analyze the user's social network and suggest places visited by friends and family. For example, it can suggest restaurants and cafes visited by the user's friends. It can also suggest tourist spots and activities visited by the user's family. Furthermore, it can suggest places that are popular on the user's social network. In this way, by suggesting the most suitable places based on the user's social network, it can make outings more comfortable.

[0053] The zero-waiting system can further analyze users' past reviews and ratings to suggest facilities based on those ratings. For example, it can suggest restaurants and cafes that users have previously given high ratings to, and avoid facilities that users have previously given low ratings to. Furthermore, if a user tends to give high ratings to a particular genre or category, the system can prioritize suggesting facilities in that genre or category. In this way, by suggesting the most suitable facilities based on the user's past reviews and ratings, it can make outings more comfortable.

[0054] The zero-waiting system can also suggest the most suitable facilities by considering the user's current weather and climate conditions. For example, it can prioritize indoor facilities on rainy days, and suggest outdoor facilities and activities on sunny days. Furthermore, it can suggest warm facilities on cold days and cool facilities on hot days. This allows for a more comfortable outing by suggesting the most suitable facilities based on the user's current weather and climate conditions.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit inputs the user's criteria. The data collection unit can input criteria such as distance, number of people, priority conditions, genre, mode of transportation, desired genre, and whether or not there are children. Users can input criteria using a smartphone or personal computer, and can also use voice input or text input. Step 2: The analysis unit searches for available nearby facilities in real time based on the conditions entered by the data collection unit. The analysis unit analyzes congestion and reservation status using people counting sensors installed at facilities and information from reservation websites. Based on the data obtained from people counting sensors and information from reservation websites, the congestion and reservation status of facilities can be grasped in real time. Step 3: The service provider compiles and provides the search results obtained by the analysis service provider. The service provider can provide the search results to the user in a list format, which can be displayed on a smartphone or personal computer screen or sent via email.

[0057] (Example of form 2) The zero-waiting system according to an embodiment of the present invention is a system that uses AI to eliminate waiting times at facilities. The zero-waiting system works by having the user input conditions, and the AI ​​then searches for and lists available nearby facilities in real time. This allows users to avoid crowds, reduce waiting times, and enjoy a comfortable outing. First, the user inputs conditions. Next, the AI ​​searches for available nearby facilities in real time based on the input conditions. During this process, it analyzes congestion and reservation status using information from people-counting sensors installed at facilities and reservation websites. The search results are compiled into a list and provided to the user. Furthermore, directions avoiding crowded areas are also provided. This mechanism allows users to reduce waiting times and enjoy a comfortable outing. For example, when looking for a place for a second round of drinks on a Friday night, a list of matching establishments is generated, allowing the user to choose a less crowded place. Similarly, when searching for a less crowded restaurant during the Obon holiday period, the system searches for available restaurants in real time, allowing the user to eat immediately without waiting in line. The zero-waiting system aims to increase revenue by increasing the number of facility users, and generates revenue by charging facilities a monthly usage fee. Furthermore, the system reduces the burden on facilities by providing free sensor installation. The target audience includes general consumers who want to avoid crowded restaurants on the eve of holidays, and facility operators who face the problem of congestion concentrating in specific locations. This system is realized by combining generative AI, image analysis AI, and numerical processing AI. By distributing people-counting sensors to facilities in advance and analyzing congestion and reservation status from the people-counting sensors and reservation site information, it enables real-time searching of nearby available facilities. As a result, the zero-waiting system can reduce waiting times and enable comfortable outings by searching for nearby available facilities in real time based on the user's conditions and providing a list.

[0058] The zero-waiting-time system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit inputs user conditions. The collection unit can input conditions such as distance, number of people, priority conditions, genre, mode of transportation, desired genre, and whether or not there are children. The collection unit can input conditions using, for example, a smartphone or personal computer. The collection unit can also input conditions using, for example, voice input or text input. The analysis unit searches for nearby available facilities in real time based on the conditions input by the collection unit. The analysis unit analyzes congestion and reservation status using, for example, people measurement sensors installed in facilities and information from reservation sites. The analysis unit can grasp the congestion status of facilities in real time based on data obtained from people measurement sensors. The analysis unit can grasp the reservation status of facilities in real time based on information from reservation sites. The provision unit provides the search results obtained by the analysis unit in a list. The provision unit can provide the search results to the user in a list. The provision unit can display the search results in a list on the screen of a smartphone or personal computer. The service provider can, for example, create a list of search results and send it to the user via email. This allows the zero-waiting system according to the embodiment to reduce waiting times and enable a comfortable outing by searching for and listing available nearby facilities in real time based on the user's criteria.

[0059] The data collection unit inputs user criteria. These criteria can include, for example, distance, number of people, priority conditions, genre, mode of transport, desired genre, and whether or not the user has children. Specifically, users access a dedicated application or website using their smartphone or personal computer and enter the necessary criteria into an input form. The data collection unit can also accept criteria using voice input or text input. With voice input, the user simply speaks the criteria into a microphone, and the system uses speech recognition technology to convert it into text and automatically analyze the input. With text input, the user enters the criteria using a keyboard or touchscreen. Furthermore, the data collection unit also has a function to assist with criterion input by referring to the user's past input history and behavioral history. For example, it can suggest criteria suitable for the user based on previously used facilities and criteria. This allows the data collection unit to enable users to input criteria easily and quickly, improving the overall usability of the system.

[0060] The analysis department searches for available nearby facilities in real time based on the conditions entered by the data collection department. The analysis department analyzes congestion and reservation status using, for example, people counting sensors installed in facilities and information from reservation websites. Specifically, people counting sensors are installed at the entrance and main areas of facilities to count the number of people entering and leaving. This allows for real-time tracking of the current number of people inside the facility. Information from reservation websites provides information on the reservation and availability status of facilities, and by integrating and analyzing this data, the congestion status of facilities can be accurately grasped. For example, the analysis department can grasp the congestion status of facilities in real time based on data obtained from people counting sensors. Furthermore, by utilizing AI-based data analysis technology, it can also predict future congestion based on past data and trends. This allows for the suggestion of the most suitable facility according to the time of day and day of the week the user visits. For example, the analysis department can grasp the reservation status of facilities in real time based on information from reservation websites. This allows for the quick discovery of facilities that match the user's desired conditions, minimizing waiting times.

[0061] The service provider provides the search results obtained by the analysis department in a list format. For example, the service provider can provide the user with a list of search results. Specifically, it can prioritize the search results based on the user's criteria and display the most suitable facilities at the top. For example, the service provider can display the search results in a list format on the screen of a smartphone or personal computer. The user can scroll through the list on the screen to check detailed information and congestion status for each facility. For example, the service provider can also send the search results in a list format to the user via email. This allows the user to check the search results even when on the go, improving convenience. Furthermore, the service provider also has a function to display the search results on a map, allowing the user to check the location of facilities and surrounding conditions while viewing the map. This allows the user to quickly find the most suitable facility and reduce waiting times. The service provider can also collect user feedback and continuously improve the accuracy and method of providing search results. For example, by collecting ratings and comments on facilities that users have actually visited and reflecting them in the next search results, the service provider can provide more accurate information. In this way, the service provider can always provide users with the latest and most suitable information, supporting a comfortable outing.

[0062] The data collection unit allows users to input conditions such as distance, number of people, priority criteria, genre, mode of transport, desired genre, and whether or not there are children. The data collection unit allows users to input conditions using, for example, a smartphone or personal computer. The data collection unit also allows users to input conditions using, for example, voice input or text input. The data collection unit allows users to input distance in kilometers or miles. The data collection unit allows users to input the number of people as a specific numerical value. The data collection unit allows users to select priority criteria from a list of options. This allows users to search for more suitable facilities by inputting detailed conditions.

[0063] The analysis unit can analyze congestion and reservation status using people-counting sensors installed at facilities and information from reservation websites. For example, the analysis unit can grasp the congestion status of a facility in real time based on data obtained from people-counting sensors. For example, the analysis unit can grasp the reservation status of a facility in real time based on information from reservation websites. For example, the analysis unit can measure the number of people using camera-based sensors or infrared sensors. For example, the analysis unit can analyze the congestion status of a facility using reservation status and cancellation information from reservation websites. This allows for accurate analysis of facility congestion and reservation status, enabling the provision of appropriate facilities to users.

[0064] The service provider can provide search results to users in a list format. For example, the service provider can display the search results in a list format on the screen of a smartphone or personal computer. For example, the service provider can send the search results in a list format to users via email. For example, the service provider can provide the search results in a list format that is easy for users to select from. For example, the service provider can customize the order of the list and the displayed items to the user's preferences. This makes it easier for users to select results by providing them in a list format.

[0065] The service provider can provide directions that avoid crowded areas. For example, the service provider can display directions that avoid crowded areas on the screen of a smartphone or personal computer. For example, the service provider can send directions that avoid crowded areas to the user via email. For example, the service provider can provide directions that avoid crowded areas based on a data source of congestion conditions. For example, the service provider can provide directions that avoid crowded areas using a method for calculating alternative routes. By providing directions that avoid crowded areas, users can travel more comfortably.

[0066] The data collection unit can estimate the user's emotions and adjust the condition input interface based on the estimated emotions. For example, if the user is stressed, the data collection unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the data collection unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the data collection unit can prioritize voice input to allow for quick condition input. This makes condition input more comfortable by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The data collection unit can analyze past condition input history and automatically suggest conditions that are easy for the user to input. For example, the data collection unit can automatically display conditions that the user has frequently entered in the past as suggestions. For example, the data collection unit can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the data collection unit can predict and suggest conditions that the user will use during a specific time period based on their past input history. In this way, by analyzing past condition input history, it is possible to automatically suggest conditions that are easy for the user to input.

[0068] The data collection unit can automatically complete the optimal conditions when conditions are entered, taking into account the user's current location and past activity history. For example, when a user opens the app, the data collection unit can automatically acquire the user's current location and set it as a condition. For example, when a user enters conditions, the data collection unit can suggest the best options by considering the distance from the user's current location. For example, when a user uses the app while on the move, the data collection unit can update the user's current location in real time and reflect it as a condition. This allows the system to automatically complete the optimal conditions by taking into account the user's current location and past activity history.

[0069] The data collection unit can estimate the user's emotions and determine the priority of condition inputs based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize inputting important conditions. For example, if the user is relaxed, the data collection unit can allow the user to input detailed conditions. For example, if the user is in a hurry, the data collection unit can allow the user to input only the most important conditions. This allows for more appropriate condition input by determining the priority of condition inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The data collection unit can analyze the user's social media activity when conditions are entered and automatically suggest relevant conditions. For example, the data collection unit can suggest relevant conditions based on the locations the user has checked into on social media. For example, the data collection unit can analyze the content of posts the user has shared on social media and suggest relevant conditions. For example, the data collection unit can suggest relevant conditions based on information about accounts the user follows on social media. In this way, relevant conditions can be automatically suggested by analyzing the user's social media activity.

[0071] The data collection unit can provide the optimal input method by considering the user's device information when conditions are entered. For example, if the user is using a smartphone, the data collection unit can provide an input method that matches the screen size. For example, if the user is using a tablet, the data collection unit can provide an input method optimized for a large screen. For example, if the user is using a smartwatch, the data collection unit can provide a concise and highly visible input method. In this way, the optimal input method can be provided by considering the user's device information.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. For example, if the user is in a hurry, the analysis unit can apply an algorithm that performs a rapid analysis. For example, if the user is excited, the analysis unit can apply an algorithm that provides visually stimulating results. By adjusting the analysis algorithm according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The analysis unit can predict current congestion levels by referring to past congestion data during analysis. For example, the analysis unit can predict current congestion levels based on past congestion data. For example, the analysis unit can predict congestion levels by combining past congestion data with current weather information. For example, the analysis unit can predict congestion levels by combining past congestion data with current event information. This allows for the prediction of current congestion levels by referring to past congestion data.

[0074] The analysis department can apply different analytical methods to each facility category during analysis. For example, in the case of a restaurant, the analysis department can analyze based on reservation status and current number of customers. For example, in the case of a shopping mall, the analysis department can comprehensively analyze the congestion status of each store. For example, in the case of a public facility, the analysis department can analyze based on user inflow and outflow data. By applying different analytical methods to each facility category, more accurate analysis becomes possible.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can select the optimal facility by considering the geographical distribution of facilities during analysis. For example, the analysis unit can prioritize selecting the facility closest to the user's current location. For example, the analysis unit can select a facility along the user's travel route. For example, the analysis unit can select a facility close to the user's destination. In this way, the optimal facility can be selected by considering the geographical distribution of facilities.

[0077] The analysis department can improve the accuracy of its analysis by referring to relevant literature and reviews of the facility during the analysis process. For example, the analysis department can predict congestion levels based on facility reviews. For example, the analysis department can analyze the causes of congestion by referring to relevant literature of the facility. For example, the analysis department can improve the accuracy of its analysis by combining facility reviews and relevant literature. This means that the accuracy of the analysis can be improved by referring to relevant literature and reviews of the facility.

[0078] The service provider can estimate the user's emotions and adjust how the list is displayed based on those emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible list. If the user is relaxed, the service provider can provide a list containing detailed information. If the user is in a hurry, the service provider can provide a concise list. By adjusting how the list is displayed according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The service provider can generate an optimal list by referring to the user's past selection history at the time of service provision. For example, the service provider can generate an optimal list based on the facilities the user has previously selected. For example, the service provider can list facilities to avoid congestion from the user's past selection history. For example, the service provider can analyze the user's past selection history and generate the most efficient list. In this way, the optimal list can be generated by referring to the user's past selection history.

[0080] The service provider can list the most suitable facilities at the time of service provision, taking into account the user's current location information. For example, the service provider can list the facilities closest to the user's current location. For example, the service provider can list facilities along the user's travel route. For example, the service provider can list facilities close to the user's destination. In this way, the service provider can list the most suitable facilities by taking into account the user's current location information.

[0081] The service provider can estimate the user's emotions and prioritize the list based on those emotions. For example, if the user is stressed, the service provider can prioritize listing important facilities. If the user is relaxed, the service provider can list facilities with detailed information. If the user is in a hurry, the service provider can list only the most important facilities. This allows for a more appropriate list to be provided by prioritizing the list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The service provider can analyze the user's social media activity and list relevant facilities at the time of service provision. For example, the service provider can list relevant facilities based on places the user has checked in to on social media. For example, the service provider can list relevant facilities based on posts the user has shared on social media. For example, the service provider can list relevant facilities based on information about accounts the user follows on social media. In this way, relevant facilities can be listed by analyzing the user's social media activity.

[0083] The service provider can provide the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by considering the user's device information.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The zero-waiting system can further monitor the user's health status and suggest facilities tailored to that status. For example, if the user is tired, it can prioritize suggesting facilities where they can relax. If the user is active, it can suggest facilities offering exercise or activities. Furthermore, if the user has a specific health problem, it can suggest facilities that address that problem. By suggesting the optimal facility according to the user's health status, it can make outings more comfortable.

[0086] The zero-waiting system can further analyze users' past behavior patterns and suggest facilities based on those patterns. For example, it can suggest similar facilities based on data from facilities the user has visited in the past. It can also suggest facilities suitable for a particular time slot if the user tends to visit specific facilities at that time. Furthermore, if a user tends to visit specific facilities during certain events or seasons, it can suggest facilities suitable for those events or seasons. This allows for a more comfortable outing by suggesting optimal facilities based on the user's behavior patterns.

[0087] The zero-waiting system can further learn user preferences and tastes, and suggest facilities based on those preferences. For example, if a user prefers a particular type of restaurant, it can prioritize suggesting restaurants of that type. Similarly, if a user prefers a particular activity, it can suggest facilities offering that activity. Furthermore, if a user prefers a specific brand or chain store, it can suggest facilities of that brand or chain store. This allows for a more comfortable outing by suggesting the optimal facility based on the user's preferences and tastes.

[0088] The zero-waiting system can further estimate the user's current mood and emotions and suggest facilities based on those moods and emotions. For example, if the user is in the mood to relax, it can suggest facilities where they can relax. If the user is in the mood to be active, it can suggest facilities that offer exercise or activities. Furthermore, if the user is feeling stressed, it can suggest facilities that can help relieve stress. In this way, by suggesting the most suitable facilities based on the user's mood and emotions, it can make outings more comfortable.

[0089] The zero-waiting system can further analyze the user's social network and suggest places visited by friends and family. For example, it can suggest restaurants and cafes visited by the user's friends. It can also suggest tourist spots and activities visited by the user's family. Furthermore, it can suggest places that are popular on the user's social network. In this way, by suggesting the most suitable places based on the user's social network, it can make outings more comfortable.

[0090] The zero-waiting system can further analyze users' past reviews and ratings to suggest facilities based on those ratings. For example, it can suggest restaurants and cafes that users have previously given high ratings to, and avoid facilities that users have previously given low ratings to. Furthermore, if a user tends to give high ratings to a particular genre or category, the system can prioritize suggesting facilities in that genre or category. In this way, by suggesting the most suitable facilities based on the user's past reviews and ratings, it can make outings more comfortable.

[0091] The zero-waiting system can also suggest the most suitable facilities by considering the user's current weather and climate conditions. For example, it can prioritize indoor facilities on rainy days, and suggest outdoor facilities and activities on sunny days. Furthermore, it can suggest warm facilities on cold days and cool facilities on hot days. This allows for a more comfortable outing by suggesting the most suitable facilities based on the user's current weather and climate conditions.

[0092] The zero-waiting system can also estimate the user's emotions and notify them in real time about the congestion status of facilities based on those emotions. For example, if a user is feeling stressed, it can prioritize notifying them of facilities that are not crowded. If a user is relaxed, it can notify them of facilities that are okay with some crowding. Furthermore, if a user is in a hurry, it can quickly notify them of the least crowded facilities. In this way, by providing optimal congestion status notifications based on the user's emotions, it can make outings more comfortable.

[0093] The zero-waiting system can also estimate the user's emotions and notify them in real time about the availability of facilities based on those emotions. For example, if a user is feeling stressed, it can prioritize notifying them of facilities where reservations are easier to obtain. If a user is relaxed, it can notify them of facilities where a short wait is acceptable. Furthermore, if a user is in a hurry, it can quickly notify them of facilities where reservations are immediately available. By providing optimal reservation status notifications based on the user's emotions, this system can make outings more comfortable.

[0094] The zero-waiting system can also estimate the user's emotions and notify them in real time of facility ratings and reviews based on those emotions. For example, if a user is feeling stressed, it can prioritize notifying them of highly-rated facilities. If a user is relaxed, it can notify them of facilities that are acceptable even if they have some lower ratings. Furthermore, if a user is in a hurry, it can quickly notify them of facilities with few ratings or reviews. In this way, by providing optimal ratings and reviews based on the user's emotions, it can make outings more comfortable.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit inputs the user's criteria. The data collection unit can input criteria such as distance, number of people, priority conditions, genre, mode of transportation, desired genre, and whether or not there are children. Users can input criteria using a smartphone or personal computer, and can also use voice input or text input. Step 2: The analysis unit searches for available nearby facilities in real time based on the conditions entered by the data collection unit. The analysis unit analyzes congestion and reservation status using people counting sensors installed at facilities and information from reservation websites. Based on the data obtained from people counting sensors and information from reservation websites, the congestion and reservation status of facilities can be grasped in real time. Step 3: The service provider compiles and provides the search results obtained by the analysis service provider. The service provider can provide the search results to the user in a list format, which can be displayed on a smartphone or personal computer screen or sent via email.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can input user conditions using the reception device 38 of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes congestion and reservation status using people measurement sensors and information from reservation sites. The provision unit can, for example, list the search results and provide them to the user using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can input user conditions by voice using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes congestion and reservation status using people measurement sensors and information from reservation sites. The provision unit can provide search results to the user by voice using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can input user conditions by voice using the microphone 238 of the headset terminal 314. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes congestion and reservation status using people counting sensors and information from the reservation site. The provision unit can, for example, list the search results and provide them to the user using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can input user conditions by voice using the microphone 238 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes congestion and reservation status using people measurement sensors and information from reservation sites. The provision unit can provide search results to the user by voice using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A data collection unit for inputting user conditions, Based on the conditions entered by the aforementioned collection unit, the analysis unit searches for nearby available facilities in real time, A providing unit that lists and provides the search results obtained by the analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Enter the following conditions: distance, number of people, priority conditions, genre, mode of transportation, desired genre, and whether or not there are children. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The facility uses people-counting sensors installed in the facility and information from the reservation website to analyze congestion levels and reservation status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, List the search results and provide them to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides directions that avoid crowded areas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is By analyzing past input history of conditions, the system automatically suggests conditions that are easy for the user to input. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When users enter conditions, the system automatically completes the optimal conditions by considering the user's current location and past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and determines the priority of conditional inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When users enter criteria, the system analyzes their social media activity and automatically suggests relevant criteria. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When inputting conditions, the system takes the user's device information into consideration and provides the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, past congestion data is referenced to predict current congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each facility category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, the geographical distribution of facilities is taken into consideration when selecting the most suitable facility. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we refer to relevant literature and reviews on the facility to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's sentiment and adjusts how the list is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the system references the user's past selection history to generate the most suitable list. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the system takes the user's current location into consideration to list the most suitable facilities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the list based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the system analyzes the user's social media activity and lists relevant facilities. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we will consider the user's device information to provide the optimal display method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit for inputting user conditions, Based on the conditions entered by the aforementioned collection unit, the analysis unit searches for nearby available facilities in real time, A providing unit that lists and provides the search results obtained by the analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Enter the following conditions: distance, number of people, priority conditions, genre, mode of transportation, desired genre, and whether or not there are children. The system according to feature 1.

3. The aforementioned analysis unit is The facility uses people-counting sensors installed in the facility and information from the reservation website to analyze congestion levels and reservation status. The system according to feature 1.

4. The aforementioned supply unit is, List the search results and provide them to the user. The system according to feature 1.

5. The aforementioned supply unit is, Provides directions that avoid crowded areas. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is By analyzing past input history of conditions, the system automatically suggests conditions that are easy for the user to input. The system according to feature 1.

8. The aforementioned collection unit is When users enter conditions, the system automatically completes the optimal conditions by considering the user's current location and past activity history. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the user's emotions and determines the priority of conditional inputs based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When users enter criteria, the system analyzes their social media activity and automatically suggests relevant criteria. The system according to feature 1.

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