system

Abstract

The system according to the embodiment aims to grasp the availability of facilities in an office in real time and to use them efficiently. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a reservation unit. The collection unit collects the availability status of each facility. The analysis unit analyzes the data collected by the collection unit and updates the availability status of each facility in real time. The provision unit provides the availability status obtained by the analysis unit to a user. The reservation unit allows a user to reserve a specific facility.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to grasp the availability of facilities within an office in real time, which made it difficult to use them efficiently.

[0005] The system according to the embodiment aims to grasp the availability of facilities in an office in real time and to use them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a reservation unit. The collection unit collects availability information for each facility. The analysis unit analyzes the data collected by the collection unit and updates the availability information for each facility in real time. The provision unit provides the availability information obtained by the analysis unit to a user. The reservation unit allows a user to reserve a specific facility. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the availability of facilities in an office in real time and use them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A location search system according to an embodiment of the present invention monitors the availability of various office facilities (e.g., seats, restrooms, and booths) in real time, enabling employees to use them efficiently. This location search system works in conjunction with Smile's vacancy information system, allowing users to easily check availability from their smartphones or computers. First, the system collects the availability of each facility in real time using sensors and cameras. For example, pressure sensors are installed on seats to detect whether they are in use. Door open / close sensors are installed on restrooms and booths to detect whether they are in use. The data obtained from these sensors is sent to Smile's vacancy information system. Smile's vacancy information system then analyzes the collected data and updates the availability of each facility in real time. Users can check the current availability status from their smartphones or computers using a dedicated application. For example, if a user is looking for a seat, the application can locate available seats and display the shortest route. Furthermore, the system also provides a function for users to reserve specific facilities. For example, if a user wants to use a conference room or booth, they can make a reservation on the application, preventing other users from using it at the same time. This improves facility utilization efficiency and increases employee satisfaction. This system makes it easy to check the availability of facilities within the office, allowing employees to work efficiently without wasting time.In addition, by utilizing the reservation function, it becomes easier to plan facility usage, improving overall work efficiency.As a result, the location search system can check the availability of facilities within the office in real time, allowing for efficient use.

[0029] A location search system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a reservation unit. The collection unit collects information on the availability of each facility. For example, the collection unit installs a pressure sensor in each seat to detect whether the seat is occupied. The collection unit also installs a door open / close sensor in each toilet or booth to detect whether the seat is occupied. For example, the pressure sensor detects a change in pressure when a seat is sat on to determine whether the seat is occupied. The door open / close sensor detects whether the toilet or booth door is open or closed to determine whether the seat is occupied. The analysis unit analyzes the data collected by the collection unit and updates the availability of each facility in real time. For example, the analysis unit analyzes the usage status of each facility based on the collected data and updates the availability status in real time. The provision unit provides the availability status obtained by the analysis unit to a user. For example, the provision unit allows a user to check the current availability status via a dedicated application on a smartphone or computer. The provision unit can also locate available seats and display the shortest route. The reservation unit provides a function for a user to reserve a specific facility. For example, the reservation unit allows a user to reserve a conference room or booth on the application, preventing other users from using the facility at the same time. This allows the location search system according to the embodiment to grasp the availability of facilities in an office in real time and use them efficiently. Furthermore, by utilizing the reservation function, it becomes easier to plan facility usage, improving overall business efficiency.

[0030] The collection unit can detect vacancy status by installing pressure sensors in seats and door open / close sensors in toilets and booths. For example, the collection unit installs pressure sensors in seats to detect whether the seats are in use. The pressure sensors detect pressure changes when a person sits in the seat and determine whether the seat is in use. The collection unit also installs door open / close sensors in toilets and booths to detect whether they are in use. The door open / close sensors detect the open / close state of the toilet or booth door and determine whether they are in use. This allows the vacancy status of each facility to be accurately detected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data obtained from pressure sensors and door open / close sensors into a generation AI and have the generation AI detect vacancy status.

[0031] The analysis unit can analyze the collected data and update the availability of each facility in real time. The analysis unit, for example, analyzes the usage status of each facility based on the collected data and updates the availability status in real time. The analysis unit analyzes the collected data in real time and updates the availability status of each facility. This allows the availability status of facilities to be updated in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform an analysis of the availability status.

[0032] The providing unit can enable a user to check current availability through an application on a smartphone or a personal computer. The providing unit can, for example, enable a user to check current availability through a dedicated application on a smartphone or a personal computer. The providing unit can also check the location of available seats and display the shortest route. This allows a user to easily check availability. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input availability data into a generation AI and cause the generation AI to display the availability.

[0033] The providing unit can confirm the location of an available seat and display the shortest route. For example, the providing unit confirms the location of an available seat and displays the shortest route. By confirming the location of an available seat and displaying the shortest route, the providing unit can enable a user to efficiently find an available seat. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input location data of an available seat into the generating AI and cause the generating AI to display the shortest route.

[0034] The reservation unit allows a user to reserve a specific facility on the application and prevent other users from using it at the same time. The reservation unit, for example, allows a user to reserve a conference room or booth on the application and prevent other users from using it at the same time. The reservation unit also enables reservations to be canceled or changed. This allows facility reservations to be made efficiently. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may input reservation data into a generation AI and have the generation AI manage the reservations.

[0035] The reservation unit can also enable cancellation or change of reservations. For example, the reservation unit allows a user to cancel or change a reservation on an application. The reservation unit enables flexible cancellation or change of reservations. This allows flexible cancellation or change of reservations. Some or all of the above-described processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input cancellation or change data into the generation AI and have the generation AI manage the cancellation or change.

[0036] The collection unit can analyze the usage history of each facility and determine the optimal sensor placement. For example, the collection unit installs additional pressure sensors in areas where seats are used frequently. The collection unit adjusts the sensitivity of door open / close sensors during times when toilets are used frequently. The collection unit optimizes sensor placement based on the usage history of the boxes and improves data accuracy. In this way, optimizing sensor placement improves data accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input usage history data into a generation AI and have the generation AI determine the optimal sensor placement.

[0037] When collecting facility vacancy information, the collection unit can correct data based on environmental conditions. For example, when the temperature is high, the collection unit adjusts the sensitivity of the seat pressure sensor to prevent false detection. When the humidity is high, the collection unit corrects data from the toilet door open / close sensor to accurately grasp usage status. When the illuminance is low, the collection unit corrects data from the box sensor to accurately collect vacancy information. This improves data accuracy by taking environmental conditions into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input environmental condition data into the generation AI and have the generation AI correct the data.

[0038] When collecting facility availability information, the collection unit can prioritize collecting highly relevant facilities by taking into account the user's location information. For example, the collection unit prioritizes collecting availability information of seats close to the user. The collection unit prioritizes collecting availability information of toilets close to the user. The collection unit prioritizes collecting availability information of booths close to the user. This makes it possible to provide highly relevant information by taking the user's location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant facilities.

[0039] When collecting facility availability information, the collection unit can determine the optimal collection timing by referring to the user's schedule information. For example, the collection unit collects conference room availability information in accordance with the end time of the user's meeting. The collection unit collects seat availability information in accordance with the user's break time. The collection unit collects restroom availability information in accordance with the user's schedule. This allows information to be collected at the optimal timing by referring to the user's schedule information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's schedule information data into the generation AI and have the generation AI determine the optimal collection timing.

[0040] When analyzing collected data, the analysis unit can detect abnormal values ​​by comparing it with past data. For example, the analysis unit detects an abnormal value when the frequency of seat use is abnormally high compared to past data. The analysis unit detects an abnormal value when the frequency of toilet use is significantly different from past data. The analysis unit detects an abnormal value when the frequency of box use is abnormally low compared to past data. In this way, abnormal values ​​can be detected by comparing it with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI detect abnormal values.

[0041] When analyzing the collected data, the analysis unit can apply different analysis algorithms for each type of facility. For example, when analyzing seat usage, the analysis unit applies an algorithm based on pressure sensor data. When analyzing toilet usage, the analysis unit applies an algorithm based on door open / close sensor data. When analyzing box usage, the analysis unit applies an algorithm that integrates data from multiple sensors. This allows appropriate analysis to be performed for each type of facility. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sensor data into a generation AI and have the generation AI perform different analyses for each type of facility.

[0042] When analyzing collected data, the analysis unit can improve the accuracy of the analysis by referring to the user's usage history. For example, the analysis unit improves the accuracy of the analysis based on data on seats that the user has frequently used in the past. The analysis unit improves the accuracy of the analysis based on data on toilets that the user has used in the past. The analysis unit improves the accuracy of the analysis based on data on lockers that the user has used in the past. In this way, the accuracy of the analysis is improved by referring to the user's usage history. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's usage history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] When analyzing collected data, the analysis unit can improve the accuracy of the analysis by referring to related external data. For example, the analysis unit refers to weather information to analyze seat usage during rainy weather. The analysis unit refers to traffic information to analyze toilet usage during rush hour. The analysis unit refers to external event information to analyze box usage during a specific event. In this way, by referring to external data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input external data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] When displaying availability, the providing unit can select the optimal display method by referring to the user's past usage history. For example, the providing unit prioritizes displaying the availability of seats that the user has frequently used in the past. The providing unit prioritizes displaying the availability of toilets that the user has used in the past. The providing unit prioritizes displaying the availability of booths that the user has used in the past. In this way, by referring to the user's past usage history, the optimal display method can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's usage history data into the generating AI and cause the generating AI to select the optimal display method.

[0045] The providing unit can apply different display formats for each facility type when displaying availability. For example, when displaying seat availability, the providing unit visually displays the seat location and usage status. When displaying toilet availability, the providing unit visually displays the toilet location and usage status. When displaying booth availability, the providing unit visually displays the booth location and usage status. This makes it possible to provide an appropriate display format for each facility type. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data for each facility type into a generating AI and cause the generating AI to apply the display format.

[0046] When displaying availability, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. If the user is using a tablet, the providing unit provides a display method optimized for a large screen. If the user is using a personal computer, the providing unit provides a display method that includes detailed information. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data into the generating AI and cause the generating AI to select the optimal display method.

[0047] When displaying availability, the providing unit can refer to the user's location information and prioritize displaying highly relevant facilities. For example, the providing unit prioritizes displaying availability of seats close to the user. The providing unit prioritizes displaying availability of toilets close to the user. The providing unit prioritizes displaying availability of booths close to the user. This makes it possible to provide highly relevant information by referring to the user's location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's location information data into the generating AI and cause the generating AI to display highly relevant facilities.

[0048] When accepting a reservation, the reservation unit can select the optimal reservation method by referring to the user's past reservation history. For example, the reservation unit prioritizes suggesting seats that the user has frequently reserved in the past. The reservation unit suggests the optimal reservation method based on toilet reservation methods that the user has used in the past. The reservation unit suggests the optimal reservation method based on data on boxes that the user has reserved in the past. In this way, the optimal reservation method can be provided by referring to the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's reservation history data into a generation AI and have the generation AI select the optimal reservation method.

[0049] When accepting a reservation, the reservation unit can apply different reservation algorithms for each type of facility. For example, when accepting a seat reservation, the reservation unit applies an algorithm based on the frequency of seat use. When accepting a toilet reservation, the reservation unit applies an algorithm based on the frequency of toilet use. When accepting a booth reservation, the reservation unit applies an algorithm based on the frequency of booth use. This allows appropriate reservations to be made for each type of facility. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input data for each type of facility into a generation AI and have the generation AI apply the reservation algorithm.

[0050] When accepting a reservation, the reservation unit can suggest an optimal reservation time by referring to the user's schedule information. The reservation unit, for example, refers to the user's calendar information and suggests reserving a seat during an available time slot. The reservation unit suggests a toilet reservation time based on the user's schedule information. The reservation unit suggests an optimal booth reservation time according to the user's schedule. In this way, the optimal reservation time can be suggested by referring to the user's schedule information. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule information data into a generation AI and have the generation AI suggest an optimal reservation time.

[0051] When accepting a reservation, the reservation unit can prioritize reserving highly relevant facilities by taking into account the user's location information. For example, the reservation unit prioritizes reserving a seat that the user is close to. The reservation unit prioritizes reserving a toilet that the user is close to. The reservation unit prioritizes reserving a booth that the user is close to. In this way, by taking the user's location information into consideration, highly relevant facilities can be reserved preferentially. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's location information data into the generation AI and cause the generation AI to reserve highly relevant facilities.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can analyze the usage history of each facility and determine the optimal sensor placement. For example, additional pressure sensors can be installed in areas where seats are used frequently. The sensitivity of door open / close sensors can be adjusted during times when toilets are used frequently. Sensor placement can be optimized based on the usage history of the boxes, improving data accuracy. In this way, optimizing sensor placement improves data accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input usage history data into a generation AI and have the generation AI determine the optimal sensor placement.

[0054] When collecting facility vacancy information, the collection unit can correct the data based on environmental conditions. For example, if the temperature is high, the sensitivity of the seat pressure sensor is adjusted to prevent false detection. If the humidity is high, the data from the toilet door open / close sensor is corrected to accurately grasp the usage status. If the illuminance is low, the data from the box sensor is corrected to accurately collect vacancy information. This improves data accuracy by taking environmental conditions into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input environmental condition data into the generation AI and have the generation AI correct the data.

[0055] When analyzing collected data, the analysis unit can detect outliers by comparing it with past data. For example, an outlier is detected when the frequency of seat use is abnormally high compared to past data. An outlier is detected when the frequency of toilet use is significantly different from past data. An outlier is detected when the frequency of box use is abnormally low compared to past data. In this way, by comparing it with past data, outliers can be detected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI detect outliers.

[0056] When displaying availability, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size is provided. If the user is using a tablet, a display method optimized for a large screen is provided. If the user is using a PC, a display method that includes detailed information is provided. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data into the generating AI and have the generating AI select the optimal display method.

[0057] When accepting a reservation, the reservation unit can suggest the optimal reservation time by referring to the user's schedule information. For example, by referring to the user's calendar information, it can suggest reserving a seat during an available time slot. It can suggest a toilet reservation time based on the user's schedule information. It can suggest the optimal booth reservation time based on the user's schedule. In this way, the optimal reservation time can be suggested by referring to the user's schedule information. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's schedule information data into a generation AI and have the generation AI suggest the optimal reservation time.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects the availability status of each facility. For example, pressure sensors are installed in seats to detect whether the seats are in use. Door open / close sensors are also installed in toilets and booths to detect whether they are in use. The pressure sensors detect pressure changes when a seat is sat on and determine whether the seat is in use. The door open / close sensors detect whether the toilet or booth door is open or closed and determine whether it is in use. Step 2: The analysis unit analyzes the data collected by the collection unit and updates the availability status of each facility in real time. For example, based on the collected data, the analysis unit analyzes the usage status of each facility and updates the availability status in real time. Step 3: The provider provides the availability information obtained by the analysis unit to the user. For example, the user can check the current availability information through a dedicated application on their smartphone or computer. The provider can also check the location of available seats and display the shortest route. Step 4: The reservation section allows users to reserve specific facilities, for example, a conference room or a booth on the application, preventing other users from using it at the same time.

[0060] (Example 2) A location search system according to an embodiment of the present invention monitors the availability of various office facilities (e.g., seats, restrooms, and booths) in real time, enabling employees to use them efficiently. This location search system works in conjunction with Smile's vacancy information system, allowing users to easily check availability from their smartphones or computers. First, the system collects the availability of each facility in real time using sensors and cameras. For example, pressure sensors are installed on seats to detect whether they are in use. Door open / close sensors are installed on restrooms and booths to detect whether they are in use. The data obtained from these sensors is sent to Smile's vacancy information system. Smile's vacancy information system then analyzes the collected data and updates the availability of each facility in real time. Users can check the current availability status from their smartphones or computers using a dedicated application. For example, if a user is looking for a seat, the application can locate available seats and display the shortest route. Furthermore, the system also provides a function for users to reserve specific facilities. For example, if a user wants to use a conference room or booth, they can make a reservation on the application, preventing other users from using it at the same time. This improves facility utilization efficiency and increases employee satisfaction. This system makes it easy to check the availability of facilities within the office, allowing employees to work efficiently without wasting time.In addition, by utilizing the reservation function, it becomes easier to plan facility usage, improving overall work efficiency.As a result, the location search system can check the availability of facilities within the office in real time, allowing for efficient use.

[0061] A location search system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a reservation unit. The collection unit collects information on the availability of each facility. For example, the collection unit installs a pressure sensor in each seat to detect whether the seat is occupied. The collection unit also installs a door open / close sensor in each toilet or booth to detect whether the seat is occupied. For example, the pressure sensor detects a change in pressure when a seat is sat on to determine whether the seat is occupied. The door open / close sensor detects whether the toilet or booth door is open or closed to determine whether the seat is occupied. The analysis unit analyzes the data collected by the collection unit and updates the availability of each facility in real time. For example, the analysis unit analyzes the usage status of each facility based on the collected data and updates the availability status in real time. The provision unit provides the availability status obtained by the analysis unit to a user. For example, the provision unit allows a user to check the current availability status via a dedicated application on a smartphone or computer. The provision unit can also locate available seats and display the shortest route. The reservation unit provides a function for a user to reserve a specific facility. For example, the reservation unit allows a user to reserve a conference room or booth on the application, preventing other users from using the facility at the same time. This allows the location search system according to the embodiment to grasp the availability of facilities in an office in real time and use them efficiently. Furthermore, by utilizing the reservation function, it becomes easier to plan facility usage, improving overall business efficiency.

[0062] The collection unit can detect vacancy status by installing pressure sensors in seats and door open / close sensors in toilets and booths. For example, the collection unit installs pressure sensors in seats to detect whether the seats are in use. The pressure sensors detect pressure changes when a person sits in the seat and determine whether the seat is in use. The collection unit also installs door open / close sensors in toilets and booths to detect whether they are in use. The door open / close sensors detect the open / close state of the toilet or booth door and determine whether they are in use. This allows the vacancy status of each facility to be accurately detected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data obtained from pressure sensors and door open / close sensors into a generation AI and have the generation AI detect vacancy status.

[0063] The analysis unit can analyze the collected data and update the availability of each facility in real time. The analysis unit, for example, analyzes the usage status of each facility based on the collected data and updates the availability status in real time. The analysis unit analyzes the collected data in real time and updates the availability status of each facility. This allows the availability status of facilities to be updated in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform an analysis of the availability status.

[0064] The providing unit can enable a user to check current availability through an application on a smartphone or a personal computer. The providing unit can, for example, enable a user to check current availability through a dedicated application on a smartphone or a personal computer. The providing unit can also check the location of available seats and display the shortest route. This allows a user to easily check availability. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input availability data into a generation AI and cause the generation AI to display the availability.

[0065] The providing unit can confirm the location of an available seat and display the shortest route. For example, the providing unit confirms the location of an available seat and displays the shortest route. By confirming the location of an available seat and displaying the shortest route, the providing unit can enable a user to efficiently find an available seat. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input location data of an available seat into the generating AI and cause the generating AI to display the shortest route.

[0066] The reservation unit allows a user to reserve a specific facility on the application and prevent other users from using it at the same time. The reservation unit, for example, allows a user to reserve a conference room or booth on the application and prevent other users from using it at the same time. The reservation unit also enables reservations to be canceled or changed. This allows facility reservations to be made efficiently. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may input reservation data into a generation AI and have the generation AI manage the reservations.

[0067] The reservation unit can also enable cancellation or change of reservations. For example, the reservation unit allows a user to cancel or change a reservation on an application. The reservation unit enables flexible cancellation or change of reservations. This allows flexible cancellation or change of reservations. Some or all of the above-described processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input cancellation or change data into the generation AI and have the generation AI manage the cancellation or change.

[0068] The collection unit can estimate the user's emotions and adjust the frequency of facility availability information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit increases the collection frequency to provide near-real-time information. If the user is relaxed, the collection unit decreases the collection frequency to reduce the load on the system. If the user is in a hurry, the collection unit maximizes the collection frequency to provide the most up-to-date information. This allows for adjusting the collection frequency according to the user's emotions to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection frequency.

[0069] The collection unit can analyze the usage history of each facility and determine the optimal sensor placement. For example, the collection unit installs additional pressure sensors in areas where seats are used frequently. The collection unit adjusts the sensitivity of door open / close sensors during times when toilets are used frequently. The collection unit optimizes sensor placement based on the usage history of the boxes and improves data accuracy. In this way, optimizing sensor placement improves data accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input usage history data into a generation AI and have the generation AI determine the optimal sensor placement.

[0070] When collecting facility vacancy information, the collection unit can correct data based on environmental conditions. For example, when the temperature is high, the collection unit adjusts the sensitivity of the seat pressure sensor to prevent false detection. When the humidity is high, the collection unit corrects data from the toilet door open / close sensor to accurately grasp usage status. When the illuminance is low, the collection unit corrects data from the box sensor to accurately collect vacancy information. This improves data accuracy by taking environmental conditions into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input environmental condition data into the generation AI and have the generation AI correct the data.

[0071] The collection unit can estimate the user's emotions and determine the priority of facilities to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting information about available seats. If the user is relaxed, the collection unit prioritizes collecting information about available toilets. If the user is in a hurry, the collection unit prioritizes collecting information about available lockers. This allows for more appropriate information to be provided by determining the priority of facilities to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of facilities to be collected.

[0072] When collecting facility availability information, the collection unit can prioritize collecting highly relevant facilities by taking into account the user's location information. For example, the collection unit prioritizes collecting availability information of seats close to the user. The collection unit prioritizes collecting availability information of toilets close to the user. The collection unit prioritizes collecting availability information of booths close to the user. This makes it possible to provide highly relevant information by taking the user's location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant facilities.

[0073] When collecting facility availability information, the collection unit can determine the optimal collection timing by referring to the user's schedule information. For example, the collection unit collects conference room availability information in accordance with the end time of the user's meeting. The collection unit collects seat availability information in accordance with the user's break time. The collection unit collects restroom availability information in accordance with the user's schedule. This allows information to be collected at the optimal timing by referring to the user's schedule information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's schedule information data into the generation AI and have the generation AI determine the optimal collection timing.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit provides a display method including detailed information. If the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows for adjusting the display method according to the user's emotions to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0075] When analyzing collected data, the analysis unit can detect abnormal values ​​by comparing it with past data. For example, the analysis unit detects an abnormal value when the frequency of seat use is abnormally high compared to past data. The analysis unit detects an abnormal value when the frequency of toilet use is significantly different from past data. The analysis unit detects an abnormal value when the frequency of box use is abnormally low compared to past data. In this way, abnormal values ​​can be detected by comparing it with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI detect abnormal values.

[0076] When analyzing the collected data, the analysis unit can apply different analysis algorithms for each type of facility. For example, when analyzing seat usage, the analysis unit applies an algorithm based on pressure sensor data. When analyzing toilet usage, the analysis unit applies an algorithm based on door open / close sensor data. When analyzing box usage, the analysis unit applies an algorithm that integrates data from multiple sensors. This allows appropriate analysis to be performed for each type of facility. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sensor data into a generation AI and have the generation AI perform different analyses for each type of facility.

[0077] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing the availability of seats. If the user is relaxed, the analysis unit prioritizes analyzing the availability of restrooms. If the user is in a hurry, the analysis unit prioritizes analyzing the availability of lockers. This allows for more appropriate information to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0078] When analyzing collected data, the analysis unit can improve the accuracy of the analysis by referring to the user's usage history. For example, the analysis unit improves the accuracy of the analysis based on data on seats that the user has frequently used in the past. The analysis unit improves the accuracy of the analysis based on data on toilets that the user has used in the past. The analysis unit improves the accuracy of the analysis based on data on lockers that the user has used in the past. In this way, the accuracy of the analysis is improved by referring to the user's usage history. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's usage history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0079] When analyzing collected data, the analysis unit can improve the accuracy of the analysis by referring to related external data. For example, the analysis unit refers to weather information to analyze seat usage during rainy weather. The analysis unit refers to traffic information to analyze toilet usage during rush hour. The analysis unit refers to external event information to analyze box usage during a specific event. In this way, by referring to external data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input external data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The providing unit can estimate the user's emotions and adjust the availability display method based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. If the user is relaxed, the providing unit provides a display method including detailed information. If the user is in a hurry, the providing unit provides a display method that focuses on the main points. This allows the display method to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0081] When displaying availability, the providing unit can select the optimal display method by referring to the user's past usage history. For example, the providing unit prioritizes displaying the availability of seats that the user has frequently used in the past. The providing unit prioritizes displaying the availability of toilets that the user has used in the past. The providing unit prioritizes displaying the availability of booths that the user has used in the past. In this way, by referring to the user's past usage history, the optimal display method can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's usage history data into the generating AI and cause the generating AI to select the optimal display method.

[0082] The providing unit can apply different display formats for each facility type when displaying availability. For example, when displaying seat availability, the providing unit visually displays the seat location and usage status. When displaying toilet availability, the providing unit visually displays the toilet location and usage status. When displaying booth availability, the providing unit visually displays the booth location and usage status. This makes it possible to provide an appropriate display format for each facility type. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data for each facility type into a generating AI and cause the generating AI to apply the display format.

[0083] The providing unit can estimate the user's emotions and adjust the display order of availability information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit prioritizes displaying seat availability. If the user is relaxed, the providing unit prioritizes displaying restroom availability. If the user is in a hurry, the providing unit prioritizes displaying booth availability. This allows for adjusting the display order according to the user's emotions to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the display order.

[0084] When displaying availability, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. If the user is using a tablet, the providing unit provides a display method optimized for a large screen. If the user is using a personal computer, the providing unit provides a display method that includes detailed information. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data into the generating AI and cause the generating AI to select the optimal display method.

[0085] When displaying availability, the providing unit can refer to the user's location information and prioritize displaying highly relevant facilities. For example, the providing unit prioritizes displaying availability of seats close to the user. The providing unit prioritizes displaying availability of toilets close to the user. The providing unit prioritizes displaying availability of booths close to the user. This makes it possible to provide highly relevant information by referring to the user's location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's location information data into the generating AI and cause the generating AI to display highly relevant facilities.

[0086] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated user emotions. For example, if the user is feeling stressed, the reservation unit prioritizes reserving seats. If the user is relaxed, the reservation unit prioritizes reserving restrooms. If the user is in a hurry, the reservation unit prioritizes reserving booths. This allows for more appropriate reservations by determining reservation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of reservations.

[0087] When accepting a reservation, the reservation unit can select the optimal reservation method by referring to the user's past reservation history. For example, the reservation unit prioritizes suggesting seats that the user has frequently reserved in the past. The reservation unit suggests the optimal reservation method based on toilet reservation methods that the user has used in the past. The reservation unit suggests the optimal reservation method based on data on boxes that the user has reserved in the past. In this way, the optimal reservation method can be provided by referring to the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's reservation history data into a generation AI and have the generation AI select the optimal reservation method.

[0088] When accepting a reservation, the reservation unit can apply different reservation algorithms for each type of facility. For example, when accepting a seat reservation, the reservation unit applies an algorithm based on the frequency of seat use. When accepting a toilet reservation, the reservation unit applies an algorithm based on the frequency of toilet use. When accepting a booth reservation, the reservation unit applies an algorithm based on the frequency of booth use. This allows appropriate reservations to be made for each type of facility. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input data for each type of facility into a generation AI and have the generation AI apply the reservation algorithm.

[0089] The reservation unit can estimate a user's emotions and adjust the reservation cancellation or change procedure based on the estimated user emotions. For example, if the user is stressed, the reservation unit allows the user to cancel the reservation with a simple procedure. If the user is relaxed, the reservation unit provides detailed procedures and allows the user to change the reservation. If the user is in a hurry, the reservation unit allows the user to quickly cancel or change the reservation. This allows for more appropriate responses by adjusting the reservation cancellation or change procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI execute the cancellation or change procedure.

[0090] When accepting a reservation, the reservation unit can suggest an optimal reservation time by referring to the user's schedule information. The reservation unit, for example, refers to the user's calendar information and suggests reserving a seat during an available time slot. The reservation unit suggests a toilet reservation time based on the user's schedule information. The reservation unit suggests an optimal booth reservation time according to the user's schedule. In this way, the optimal reservation time can be suggested by referring to the user's schedule information. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule information data into a generation AI and have the generation AI suggest an optimal reservation time.

[0091] When accepting a reservation, the reservation unit can prioritize reserving highly relevant facilities by taking into account the user's location information. For example, the reservation unit prioritizes reserving a seat that the user is close to. The reservation unit prioritizes reserving a toilet that the user is close to. The reservation unit prioritizes reserving a booth that the user is close to. In this way, by taking the user's location information into consideration, highly relevant facilities can be reserved preferentially. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's location information data into the generation AI and cause the generation AI to reserve highly relevant facilities. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and reservation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the availability status of each facility using the camera 42 or pressure sensor of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the availability status to the user via the control unit 46A of the smart device 14, and the reservation unit realizes the reservation function through the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and reservation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the availability status of each facility using the camera 42 or pressure sensor of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the availability status to the user via the control unit 46A of the smart glasses 214, and the reservation unit realizes the reservation function through the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and reservation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the availability status of each facility using the camera 42 or pressure sensor of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the availability status to the user via the control unit 46A of the headset type terminal 314, and the reservation unit realizes the reservation function through the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and reservation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the availability status of each facility using the camera 42 and pressure sensor of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the availability status to the user via the control unit 46A of the robot 414, and the reservation unit realizes the reservation function through the control unit 46A of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that focuses on the main points is provided. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0094] The providing unit can estimate the user's emotions and adjust the display order of availability information based on the estimated user's emotions. For example, if the user is feeling stressed, priority is given to displaying seat availability. If the user is relaxed, priority is given to displaying restroom availability. If the user is in a hurry, priority is given to displaying booth availability. This allows for more appropriate information to be provided by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order.

[0095] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated user emotions. For example, if the user is feeling stressed, seat reservations are prioritized. If the user is relaxed, restroom reservations are prioritized. If the user is in a hurry, booth reservations are prioritized. This allows for more appropriate reservations by determining reservation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit may be performed using AI, or may be performed without AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI determine the reservation priorities.

[0096] The collection unit can estimate the user's emotions and adjust the frequency of facility availability information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection frequency can be increased to provide near-real-time information. If the user is relaxed, the collection frequency can be decreased to reduce the load on the system. If the user is in a hurry, the collection frequency can be maximized to provide the most up-to-date information. This allows for more appropriate information to be provided by adjusting the collection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection frequency.

[0097] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis of available seats is prioritized. If the user is relaxed, the analysis of available restrooms is prioritized. If the user is in a hurry, the analysis of available lockers is prioritized. This allows for more appropriate information to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0098] The collection unit can analyze the usage history of each facility and determine the optimal sensor placement. For example, additional pressure sensors can be installed in areas where seats are used frequently. The sensitivity of door open / close sensors can be adjusted during times when toilets are used frequently. Sensor placement can be optimized based on the usage history of the boxes, improving data accuracy. In this way, optimizing sensor placement improves data accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input usage history data into a generation AI and have the generation AI determine the optimal sensor placement.

[0099] When collecting facility vacancy information, the collection unit can correct the data based on environmental conditions. For example, if the temperature is high, the sensitivity of the seat pressure sensor is adjusted to prevent false detection. If the humidity is high, the data from the toilet door open / close sensor is corrected to accurately grasp the usage status. If the illuminance is low, the data from the box sensor is corrected to accurately collect vacancy information. This improves data accuracy by taking environmental conditions into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input environmental condition data into the generation AI and have the generation AI correct the data.

[0100] When analyzing collected data, the analysis unit can detect outliers by comparing it with past data. For example, an outlier is detected when the frequency of seat use is abnormally high compared to past data. An outlier is detected when the frequency of toilet use is significantly different from past data. An outlier is detected when the frequency of box use is abnormally low compared to past data. In this way, by comparing it with past data, outliers can be detected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI detect outliers.

[0101] When displaying availability, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size is provided. If the user is using a tablet, a display method optimized for a large screen is provided. If the user is using a PC, a display method that includes detailed information is provided. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information data into the generating AI and have the generating AI select the optimal display method.

[0102] When accepting a reservation, the reservation unit can suggest the optimal reservation time by referring to the user's schedule information. For example, by referring to the user's calendar information, it can suggest reserving a seat during an available time slot. It can suggest a toilet reservation time based on the user's schedule information. It can suggest the optimal booth reservation time based on the user's schedule. In this way, the optimal reservation time can be suggested by referring to the user's schedule information. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's schedule information data into a generation AI and have the generation AI suggest the optimal reservation time.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects the availability status of each facility. For example, pressure sensors are installed in seats to detect whether the seats are in use. Door open / close sensors are also installed in toilets and booths to detect whether they are in use. The pressure sensors detect pressure changes when a seat is sat on and determine whether the seat is in use. The door open / close sensors detect whether the toilet or booth door is open or closed and determine whether it is in use. Step 2: The analysis unit analyzes the data collected by the collection unit and updates the availability status of each facility in real time. For example, based on the collected data, the usage status of each facility is analyzed and the availability status is updated in real time. Step 3: The provider provides the availability information obtained by the analysis unit to the user. For example, the user can check the current availability information through a dedicated application on their smartphone or computer. The provider can also check the location of available seats and display the shortest route. Step 4: The reservation section allows users to reserve specific facilities, for example, a conference room or a booth on the application, preventing other users from using it at the same time.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on the availability of each facility; an analysis unit that analyzes the data collected by the collection unit and updates the availability of each facility in real time; a providing unit that provides the availability information obtained by the analyzing unit to a user; a reservation unit for a user to reserve a specific facility; A system characterized by:

2. The collecting unit Pressure sensors are installed in the seats, and door opening / closing sensors are installed in the toilets and booths to detect vacancy.

2. The system of claim 1.

3. The analysis unit Analyze the collected data and update the availability of each facility in real time.

2. The system of claim 1.

4. The providing unit Allow users to check current availability through an application on their smartphone or computer 2. The system of claim 1.

5. The providing unit Find available seats and find the shortest route 2. The system of claim 1.

6. The reservation unit Allows users to reserve specific facilities on the application and prevent other users from using them at the same time 2. The system of claim 1.

7. The reservation unit Allows you to cancel or change your reservation 2. The system of claim 1.

8. The collecting unit To estimate a user's emotion and adjust the frequency of collection of facility availability information based on the estimated user's emotion.

2. The system of claim 1.

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