System

Abstract

An object of a system according to an embodiment is to prevent the occurrence of a thing left behind in a hotel or a Minshuku and to reduce costs for storage and handling.SOLUTION: A system according to an embodiment includes a camera, a generation AI, a weight sensor, and a management system. A camera is installed in each room. The generation AI analyzes the image captured by the camera. The weight sensor is installed at a specific location in each room. The management system transmits the generated AI and that from the weight sensor to the front.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to prevent items from being left behind in hotels and guesthouses, and there was the issue of the high costs involved in storing and handling items.

[0005] The system according to the embodiment aims to prevent items from being left behind in hotels and guesthouses, and to reduce the costs of storage and handling. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a generating AI, a weight sensor, and a management system. The camera is installed in each room. The generating AI analyzes images captured by the camera. The weight sensor is installed in a specific location in each room. The management system transmits data from the generating AI and the weight sensor to the front desk. [Effects of the Invention]

[0007] The system according to the embodiment can prevent items from being left behind in hotels and guesthouses, and reduce storage and handling costs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The lost property prevention system according to an embodiment of the present invention is a system that prevents lost property in hotels and guesthouses and reduces storage and handling costs. This system uses image analysis technology and weight sensors to check whether or not an item has been left behind, allowing the front desk staff to grasp the status of each room. This prevents lost property from being left behind and reduces storage and handling costs.

[0029] An embodiment of the lost property prevention system includes a camera, a generation AI, a weight sensor, and a management system. The camera is installed in each room and captures the room's condition. For example, the camera can be installed on the ceiling or wall and capture the entire room from a specific angle. The generation AI analyzes the images captured by the camera to determine whether or not an item has been left behind. For example, the generation AI uses deep learning technology to detect chargers and clothes placed on the bed or table, or food in the refrigerator. The generation AI can also use image recognition technology to identify the shape and location of lost items. Weight sensors are installed in specific locations in each room and detect abnormal weights. For example, if a weight sensor is installed on a table or refrigerator shelf and detects a heavy object after checkout, it determines that an item is likely to have been left behind. The management system transmits data from the generation AI and the weight sensor to the front desk. For example, the management system can receive data in real time and notify front desk staff. This prevents lost items from occurring and reduces storage and handling costs. For example, if a lost item is detected, the front desk staff can immediately contact a cleaning staff member and instruct them to retrieve the item.

[0030] The generating AI can identify the location of lost items and display them on a 3D map of the room. For example, the generating AI can identify the location of lost items from image data of the room and display it on a 3D map. For example, it can detect items placed under the bed or in the closet and reflect their locations on the 3D map. This allows cleaning staff to quickly retrieve lost items.

[0031] The generation AI can automatically classify the type of lost item and set priorities according to their importance. For example, the generation AI can automatically classify the type of lost item and set priorities according to their importance. For example, it can classify important items such as chargers and passports as high priority. This allows important lost items to be detected first.

[0032] Generative AI can use image analysis technology to evaluate the cleaning status of a room and provide feedback to improve the quality of cleaning. Generative AI can, for example, use image analysis technology to evaluate the cleaning status of a room and provide feedback to improve the quality of cleaning. For example, it can detect dirt and trash on the floor and notify cleaning staff of areas for improvement. This allows it to provide feedback to improve the quality of cleaning.

[0033] Generative AI can suggest ways to optimize the interior and layout of a room, creating an environment where it is less likely that items will be left behind. Generative AI can suggest ways to optimize the interior and layout of a room, creating an environment where it is less likely that items will be left behind. For example, it can rearrange furniture so that items are placed in positions where they are easier to see. This can create an environment where it is less likely that items will be left behind.

[0034] The weight sensor can estimate the shape and material of the lost item, enabling highly accurate detection of lost items. For example, the weight sensor can estimate the shape and material of the lost item based on the weight sensor data, enabling more accurate detection of lost items. For example, it can identify a charger or clothes based on the weight and shape. This allows for highly accurate detection of lost items.

[0035] Weight sensors can analyze data in real time and immediately notify of abnormal weight fluctuations. Weight sensors can, for example, build a system that analyzes weight sensor data in real time and immediately notifies of abnormal weight fluctuations. For example, an alert can be issued if a heavy object is left behind after checkout. This allows for immediate notification of abnormal weight fluctuations.

[0036] The weight sensor can monitor the usage status of a room and improve the efficiency of maintenance. For example, the weight sensor can detect the movement and frequency of use of furniture and adjust the maintenance schedule. This can improve the efficiency of maintenance.

[0037] Weight sensors can optimize energy consumption in a room and reduce the environmental impact. For example, weight sensors can automatically control the power supply to rooms that are not in use, based on data from the weight sensors. This can optimize energy consumption and reduce the environmental impact.

[0038] The front desk system can display the location information of lost items in real time, enabling a quick response. For example, the front desk system can display the location information of lost items in real time, enabling a quick response. For example, the location of lost items can be displayed on a 3D map of the room and instructions can be given to cleaning staff. This allows for a quick response.

[0039] The front system can add a function to suggest preventive measures by accumulating data on lost items and analyzing trends in lost items.The front system can add a function to suggest preventive measures by accumulating data on lost items and analyzing trends in lost items.For example, if there are many lost items on a particular day of the week or at a particular time of day, the system can warn people to be careful during those times.This allows the addition of a function to suggest preventive measures.

[0040] The front desk system simultaneously displays the cleaning status and maintenance status of the rooms, enabling comprehensive management.The front desk system simultaneously displays the cleaning status and maintenance status of the rooms, enabling comprehensive management.For example, it identifies rooms that have not been cleaned or that require maintenance.This enables comprehensive management.

[0041] The front desk system can display the check-in and check-out history of users and strengthen individualized support. For example, the front desk system can display the check-in and check-out history of users and strengthen individualized support. For example, regular customers or users with specific requests can be provided with individualized support. This makes it possible to strengthen individualized support.

[0042] Generative AI can develop dedicated detection algorithms for the top three most commonly lost items, improving detection accuracy. Generative AI can develop dedicated detection algorithms for the top three most commonly lost items, improving detection accuracy. For example, it can detect chargers, clothes, and food in the refrigerator with high accuracy. This can improve detection accuracy.

[0043] The generation AI can add a function to send reminders to users to prevent forgetting items based on the detection results for the top three most common lost items. For example, the generation AI can add a function to send reminders to users to prevent forgetting items based on the detection results for the top three most common lost items. For example, it can send a message urging users to check their chargers or clothes before checking out. This can add a function to send reminders to prevent forgetting items.

[0044] Based on the detection results for the top three items left behind, the generative AI can optimize the layout and interior of a room, creating an environment where items are less likely to be left behind. For example, based on the detection results for the top three items left behind, the generative AI can optimize the layout and interior of a room, creating an environment where items are less likely to be left behind. For example, it can rearrange furniture so that items are placed in positions where they are easier to see. This can create an environment where items are less likely to be left behind.

[0045] The generation AI can provide the user with advice on how to prevent forgetting things based on the detection results for the top three most common lost items. For example, the generation AI can send a message urging the user to check their charger or clothes before checking out. This allows the user to receive advice on how to prevent forgetting things.

[0046] Based on data on lost items, it is possible to propose the optimal layout of storage space and improve storage efficiency.Based on data on lost items, it is possible to propose the optimal layout of storage space and improve storage efficiency.For example, items that are frequently forgotten can be stored in a specific location.This can improve storage efficiency.

[0047] Based on data on lost items, disposal methods can be optimized and disposal costs can be reduced. Based on data on lost items, for example, disposal methods can be optimized and disposal costs can be reduced. For example, recyclable items can be separated and handed over to a recycling company. This can reduce disposal costs.

[0048] Based on data on lost items, the usage status of storage space can be monitored in real time, enabling efficient management. Based on data on lost items, for example, the usage status of storage space can be monitored in real time, enabling efficient management. For example, the availability status of storage space can be grasped in real time. This allows efficient management.

[0049] Based on data on lost items, methods of recycling and reuse can be suggested, reducing the burden on the environment. Based on data on lost items, methods of recycling and reuse can be suggested, reducing the burden on the environment. For example, recyclable items can be separated and handed over to a recycling company. This can reduce the burden on the environment.

[0050] Based on data on lost items, the usage status of storage space can be monitored in real time, enabling efficient management. Based on data on lost items, for example, the usage status of storage space can be monitored in real time, enabling efficient management. For example, the availability status of storage space can be grasped in real time. This allows efficient management.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The lost property prevention system can further include a voice recognition unit. The voice recognition unit analyzes the voice uttered by the user when leaving the room and detects any comments related to forgotten items. For example, if the user says, "I think I forgot my cell phone," the voice recognition unit can detect this comment and notify the front desk. This allows for a prompt response even if the user realizes they have left something behind.

[0053] The lost property prevention system can also be equipped with a temperature sensor. The temperature sensor monitors the temperature in specific locations in the room and detects abnormal temperature fluctuations. For example, if the temperature in the refrigerator rises, it can determine that food may have been left unattended and notify the front desk. This can prevent food from being left behind.

[0054] The loss prevention system can further include a light sensor. The light sensor monitors the lighting conditions in the room and detects abnormal light fluctuations. For example, if the room lights are on after checkout, the system can determine that there is a high possibility that an item has been left behind and notify the front desk. This can further prevent items from being left behind.

[0055] The lost property prevention system can also be equipped with a humidity sensor. The humidity sensor monitors the humidity in the room and detects abnormal humidity fluctuations. For example, if the humidity in the bathroom remains high, it can determine that towels or clothes may have been left behind and notify the front desk. This can prevent items from being left behind due to humidity.

[0056] The loss prevention system can further include a vibration sensor. The vibration sensor monitors vibrations in specific locations in the room and detects abnormal vibrations. For example, if a table or bed is moved after checkout, it can determine that there is a high possibility that an item has been left behind and notify the front desk. This can prevent items from being lost due to vibrations.

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

[0058] Step 1: Cameras are installed in each room to capture the state of the room. For example, cameras can be installed on the ceiling or walls to capture the entire room from a specific angle. Step 2: The generating AI analyzes the images captured by the camera and determines whether any items have been left behind. For example, the generating AI uses deep learning technology to detect chargers and clothes placed on the bed or table, or food in the refrigerator. The generating AI can also use image recognition technology to identify the shape and location of lost items. Step 3: Weight sensors are installed in specific locations in each room to detect abnormal weight. For example, weight sensors are installed on tables or refrigerator shelves, and if a heavy object is detected after checkout, it is determined that there is a high possibility that an item has been left behind. Step 4: The management system sends the data from the generated AI and weight sensor to the front desk. For example, the management system can receive the data in real time and notify the front desk staff. This allows the lost property prevention system to prevent lost items from occurring and reduce storage and handling costs. For example, if a lost item is detected, the front desk staff can immediately contact the cleaning staff and instruct them to retrieve the lost item.

[0059] (Example 2) The lost property prevention system according to an embodiment of the present invention is a system that prevents lost property in hotels and guesthouses and reduces storage and handling costs. This system uses image analysis technology and weight sensors to check whether or not an item has been left behind, allowing the front desk staff to grasp the status of each room. This prevents lost property from being left behind and reduces storage and handling costs.

[0060] An embodiment of the lost property prevention system includes a camera, a generation AI, a weight sensor, and a management system. The camera is installed in each room and captures the room's condition. For example, the camera can be installed on the ceiling or wall and capture the entire room from a specific angle. The generation AI analyzes the images captured by the camera to determine whether or not an item has been left behind. For example, the generation AI uses deep learning technology to detect chargers and clothes placed on the bed or table, or food in the refrigerator. The generation AI can also use image recognition technology to identify the shape and location of lost items. Weight sensors are installed in specific locations in each room and detect abnormal weights. For example, if a weight sensor is installed on a table or refrigerator shelf and detects a heavy object after checkout, it determines that an item is likely to have been left behind. The management system transmits data from the generation AI and the weight sensor to the front desk. For example, the management system can receive data in real time and notify front desk staff. This prevents lost items from occurring and reduces storage and handling costs. For example, if a lost item is detected, the front desk staff can immediately contact a cleaning staff member and instruct them to retrieve the item.

[0061] The generating AI can identify the location of lost items and display them on a 3D map of the room. For example, the generating AI can identify the location of lost items from image data of the room and display it on a 3D map. For example, it can detect items placed under the bed or in the closet and reflect their locations on the 3D map. This allows cleaning staff to quickly retrieve lost items.

[0062] The generation AI can automatically classify the type of lost item and set priorities according to their importance. For example, the generation AI can automatically classify the type of lost item and set priorities according to their importance. For example, it can classify important items such as chargers and passports as high priority. This allows important lost items to be detected first.

[0063] The generation AI uses the emotion estimation function to estimate the emotion a user felt when they left something behind, and can prioritize the detection of particularly important lost items. For example, the generation AI uses the emotion estimation function to estimate the emotion a user felt when they left something behind, and can prioritize the detection of particularly important lost items. For example, it classifies items with high emotional value (such as photographs and letters) as having a high priority. This allows it to prioritize the detection of lost items with high emotional value.

[0064] Generative AI can use image analysis technology to evaluate the cleaning status of a room and provide feedback to improve the quality of cleaning. Generative AI can, for example, use image analysis technology to evaluate the cleaning status of a room and provide feedback to improve the quality of cleaning. For example, it can detect dirt and trash on the floor and notify cleaning staff of areas for improvement. This allows it to provide feedback to improve the quality of cleaning.

[0065] Generative AI can suggest ways to optimize the interior and layout of a room, creating an environment where it is less likely that items will be left behind. Generative AI can suggest ways to optimize the interior and layout of a room, creating an environment where it is less likely that items will be left behind. For example, it can rearrange furniture so that items are placed in positions where they are easier to see. This can create an environment where it is less likely that items will be left behind.

[0066] The generation AI can use the emotion estimation function to analyze the user's emotions at checkout and improve the service to provide a positive experience. The generation AI can, for example, use the emotion estimation function to analyze the user's emotions at checkout and improve the service to provide a positive experience. For example, if the user is satisfied, a discount on the next stay can be offered. This makes it possible to improve the service to provide a positive experience.

[0067] The weight sensor can estimate the shape and material of the lost item, enabling highly accurate detection of lost items. For example, the weight sensor can estimate the shape and material of the lost item based on the weight sensor data, enabling more accurate detection of lost items. For example, it can identify a charger or clothes based on the weight and shape. This allows for highly accurate detection of lost items.

[0068] Weight sensors can analyze data in real time and immediately notify of abnormal weight fluctuations. Weight sensors can, for example, build a system that analyzes weight sensor data in real time and immediately notifies of abnormal weight fluctuations. For example, an alert can be issued if a heavy object is left behind after checkout. This allows for immediate notification of abnormal weight fluctuations.

[0069] The weight sensor uses an emotion estimation function to estimate the emotion a user feels when they leave something behind, and can prioritize detecting particularly important lost items. The weight sensor, for example, uses an emotion estimation function to estimate the emotion a user feels when they leave something behind, and can prioritize detecting particularly important lost items. For example, items with high emotional value (such as photographs or letters) can be classified as having a high priority. This allows lost items with high emotional value to be prioritized.

[0070] The weight sensor can monitor the usage status of a room and improve the efficiency of maintenance. For example, the weight sensor can detect the movement and frequency of use of furniture and adjust the maintenance schedule. This can improve the efficiency of maintenance.

[0071] Weight sensors can optimize energy consumption in a room and reduce the environmental impact. For example, weight sensors can automatically control the power supply to rooms that are not in use, based on data from the weight sensors. This can optimize energy consumption and reduce the environmental impact.

[0072] The weight sensor can use its emotion estimation function to analyze the user's emotion at checkout and improve the service to provide a positive experience. The weight sensor can use its emotion estimation function to analyze the user's emotion at checkout and improve the service to provide a positive experience. For example, if the user is satisfied, a discount on the next stay can be offered. This makes it possible to improve the service to provide a positive experience.

[0073] The front desk system can display the location information of lost items in real time, enabling a quick response. For example, the front desk system can display the location information of lost items in real time, enabling a quick response. For example, the location of lost items can be displayed on a 3D map of the room and instructions can be given to cleaning staff. This allows for a quick response.

[0074] The front system can add a function to suggest preventive measures by accumulating data on lost items and analyzing trends in lost items.The front system can add a function to suggest preventive measures by accumulating data on lost items and analyzing trends in lost items.For example, if there are many lost items on a particular day of the week or at a particular time of day, the system can warn people to be careful during those times.This allows the addition of a function to suggest preventive measures.

[0075] The front system uses an emotion estimation function to estimate the emotion a user may have when they have left something behind, and can prioritize detecting particularly important lost items. For example, the front system uses an emotion estimation function to estimate the emotion a user may have when they have left something behind, and can prioritize detecting particularly important lost items. For example, items with high emotional value (such as photographs and letters) can be classified as having a high priority. This allows for prioritized detection of lost items with high emotional value.

[0076] The front desk system simultaneously displays the cleaning status and maintenance status of the rooms, enabling comprehensive management.The front desk system simultaneously displays the cleaning status and maintenance status of the rooms, enabling comprehensive management.For example, it identifies rooms that have not been cleaned or that require maintenance.This enables comprehensive management.

[0077] The front desk system can display the check-in and check-out history of users and strengthen individualized support. For example, the front desk system can display the check-in and check-out history of users and strengthen individualized support. For example, regular customers or users with specific requests can be provided with individualized support. This makes it possible to strengthen individualized support.

[0078] The front desk system can use the emotion estimation function to analyze the user's emotions at check-out and improve the service to provide a positive experience. The front desk system can, for example, use the emotion estimation function to analyze the user's emotions at check-out and improve the service to provide a positive experience. For example, if the user is satisfied, the front desk system can offer a discount on the user's next stay. This makes it possible to improve the service to provide a positive experience.

[0079] Generative AI can develop dedicated detection algorithms for the top three most commonly lost items, improving detection accuracy. Generative AI can develop dedicated detection algorithms for the top three most commonly lost items, improving detection accuracy. For example, it can detect chargers, clothes, and food in the refrigerator with high accuracy. This can improve detection accuracy.

[0080] The generation AI can add a function to send reminders to users to prevent forgetting items based on the detection results for the top three most common lost items. For example, the generation AI can add a function to send reminders to users to prevent forgetting items based on the detection results for the top three most common lost items. For example, it can send a message urging users to check their chargers or clothes before checking out. This can add a function to send reminders to prevent forgetting items.

[0081] The generation AI uses the emotion estimation function to estimate the emotion a user felt when they left something behind, and can prioritize the detection of particularly important lost items. For example, the generation AI uses the emotion estimation function to estimate the emotion a user felt when they left something behind, and can prioritize the detection of particularly important lost items. For example, it classifies items with high emotional value (such as photographs and letters) as having a high priority. This allows it to prioritize the detection of lost items with high emotional value.

[0082] Based on the detection results for the top three items left behind, the generative AI can optimize the layout and interior of a room, creating an environment where items are less likely to be left behind. For example, based on the detection results for the top three items left behind, the generative AI can optimize the layout and interior of a room, creating an environment where items are less likely to be left behind. For example, it can rearrange furniture so that items are placed in positions where they are easier to see. This can create an environment where items are less likely to be left behind.

[0083] The generation AI can provide the user with advice on how to prevent forgetting things based on the detection results for the top three most common lost items. For example, the generation AI can send a message urging the user to check their charger or clothes before checking out. This allows the user to receive advice on how to prevent forgetting things.

[0084] The generation AI can use the emotion estimation function to analyze the user's emotions at checkout and improve the service to provide a positive experience. The generation AI can, for example, use the emotion estimation function to analyze the user's emotions at checkout and improve the service to provide a positive experience. For example, if the user is satisfied, a discount on the next stay can be offered. This makes it possible to improve the service to provide a positive experience.

[0085] Based on data on lost items, it is possible to propose the optimal layout of storage space and improve storage efficiency.Based on data on lost items, it is possible to propose the optimal layout of storage space and improve storage efficiency.For example, items that are frequently forgotten can be stored in a specific location.This can improve storage efficiency.

[0086] Based on data on lost items, disposal methods can be optimized and disposal costs can be reduced. Based on data on lost items, for example, disposal methods can be optimized and disposal costs can be reduced. For example, recyclable items can be separated and handed over to a recycling company. This can reduce disposal costs.

[0087] Based on data on lost items, the usage status of storage space can be monitored in real time, enabling efficient management. Based on data on lost items, for example, the usage status of storage space can be monitored in real time, enabling efficient management. For example, the availability status of storage space can be grasped in real time. This allows efficient management.

[0088] Based on data on lost items, methods of recycling and reuse can be suggested, reducing the burden on the environment. Based on data on lost items, methods of recycling and reuse can be suggested, reducing the burden on the environment. For example, recyclable items can be separated and handed over to a recycling company. This can reduce the burden on the environment.

[0089] The emotion estimation function can analyze the user's emotions at checkout and improve the service to provide a positive experience. The emotion estimation function can, for example, analyze the user's emotions at checkout and improve the service to provide a positive experience. For example, if the user is satisfied, a discount on the next stay can be offered. This makes it possible to improve the service to provide a positive experience.

[0090] Based on data on lost items, the usage status of storage space can be monitored in real time, enabling efficient management. Based on data on lost items, for example, the usage status of storage space can be monitored in real time, enabling efficient management. For example, the availability status of storage space can be grasped in real time. This allows efficient management.

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

[0092] The lost property prevention system can further include a voice recognition unit. The voice recognition unit analyzes the voice uttered by the user when leaving the room and detects any comments related to forgotten items. For example, if the user says, "I think I forgot my cell phone," the voice recognition unit can detect this comment and notify the front desk. This allows for a prompt response even if the user realizes they have left something behind.

[0093] The lost property prevention system can also be equipped with a temperature sensor. The temperature sensor monitors the temperature in specific locations in the room and detects abnormal temperature fluctuations. For example, if the temperature in the refrigerator rises, it can determine that food may have been left unattended and notify the front desk. This can prevent food from being left behind.

[0094] The loss prevention system can further include a light sensor. The light sensor monitors the lighting conditions in the room and detects abnormal light fluctuations. For example, if the room lights are on after checkout, the system can determine that there is a high possibility that an item has been left behind and notify the front desk. This can further prevent items from being left behind.

[0095] The lost property prevention system can also be equipped with a humidity sensor. The humidity sensor monitors the humidity in the room and detects abnormal humidity fluctuations. For example, if the humidity in the bathroom remains high, it can determine that towels or clothes may have been left behind and notify the front desk. This can prevent items from being left behind due to humidity.

[0096] The loss prevention system can further include a vibration sensor. The vibration sensor monitors vibrations in specific locations in the room and detects abnormal vibrations. For example, if a table or bed is moved after checkout, it can determine that there is a high possibility that an item has been left behind and notify the front desk. This can prevent items from being lost due to vibrations.

[0097] The loss prevention system can also use its emotion estimation function to estimate a user's stress level and urge them to be extra careful if they are highly stressed. For example, if a user's stress level is high at checkout, the system can determine that they are likely to forget something and send a warning to the front desk staff. This allows special attention to be paid to users who are highly stressed.

[0098] The lost property prevention system can also use emotion estimation to estimate the user's satisfaction level and provide special services if the level is low. For example, if the user's satisfaction is low at checkout, the system can send a notification offering discounts on the next stay or special services. This can improve the user's satisfaction.

[0099] The lost property prevention system can also use an emotion estimation function to analyze the user's emotions and provide music and lighting that matches their emotions. For example, if the user is feeling relaxed, it can provide relaxing music and lighting, making the user's stay more comfortable.

[0100] The lost property prevention system can also use an emotion estimation function to analyze the user's emotions and send a message that corresponds to the emotion. For example, if the user is feeling anxious, a reassuring message can be sent. This allows the system to respond in accordance with the user's emotions.

[0101] The loss prevention system can also use emotion estimation to analyze the user's emotions and suggest activities based on their emotions. For example, if the user is bored, it can suggest nearby tourist spots and activities. This can make the user's stay more fulfilling.

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

[0103] Step 1: Cameras are installed in each room to capture the state of the room. For example, cameras can be installed on the ceiling or walls to capture the entire room from a specific angle. Step 2: The generating AI analyzes the images captured by the camera and determines whether any items have been left behind. For example, the generating AI uses deep learning technology to detect chargers and clothes placed on the bed or table, or food in the refrigerator. The generating AI can also use image recognition technology to identify the shape and location of lost items. Step 3: Weight sensors are installed in specific locations in each room to detect abnormal weight. For example, weight sensors are installed on tables or refrigerator shelves, and if a heavy object is detected after checkout, it is determined that there is a high possibility that an item has been left behind. Step 4: The management system sends the data from the generated AI and weight sensor to the front desk. For example, the management system can receive the data in real time and notify the front desk staff. This allows the lost property prevention system to prevent lost items from occurring and reduce storage and handling costs. For example, if a lost item is detected, the front desk staff can immediately contact the cleaning staff and instruct them to retrieve the lost item.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] 7, 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

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

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

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

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

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

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

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

Claims

1. Cameras installed in each room, A generation AI that analyzes images captured by the camera; Weight sensors installed in specific locations in each room, a management system that transmits data from the generating AI and the weight sensor to the front. A system characterized by:

2. The generated AI is Identify the location of the lost item and display it on a 3D map of the room 2. The system of claim 1.

3. The generated AI is Image analysis technology is used to assess the cleaning status of the room and provide feedback to improve the quality of the cleaning.

2. The system of claim 1.

4. The weight sensor The shape or material of the lost item is estimated to achieve highly accurate lost item detection.

2. The system of claim 1.

5. The front system is Displaying the location of the lost item in real time enables quick response 2. The system of claim 1.

6. The generated AI is The emotion of the user when they left the item is estimated, and particularly important items are preferentially detected.

2. The system of claim 1.

7. The weight sensor The emotion of the user when they left the item is estimated, and particularly important items are preferentially detected.

2. The system of claim 1.

8. The front system is The emotion of the user when they left the item is estimated, and particularly important items are preferentially detected.

2. The system of claim 1.

Images