System

Abstract

An object of the system according to the embodiment is to efficiently manage and find lost articles.SOLUTION: A system includes a position information acquisition part, an infrared signal analysis part, a lost article management part, and an advice provision part. The position information acquisition unit acquires position information. The infrared signal analysis section analyzes the infrared signal. The lost article management part manages lost articles. The advice providing unit provides advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have made it difficult to efficiently manage and find lost items.

[0005] The system according to the embodiment aims to efficiently manage and find lost items. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, an infrared signal analysis unit, a lost item management unit, and an advice provision unit. The location information acquisition unit acquires location information. The infrared signal analysis unit analyzes infrared signals. The lost item management unit manages lost items. The advice provision unit provides advice. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage and find lost items. [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 lost and found management system according to an embodiment of the present invention is a system for managing lost items targeted at people who are busy with childcare and daily life. This system is designed to solve the problem of unconsciously leaving something behind and then forgetting where you put it. Specifically, it uses location information from inside and outside the home, identifies the location of the item using infrared light, and provides subsequent advice. This allows the lost and found management system to efficiently manage items without losing them, even for people who are busy with childcare and daily life.

[0029] A lost item management system according to an embodiment includes a location information acquisition unit, an infrared signal analysis unit, a lost item management unit, and an advice provision unit. The location information acquisition unit acquires location information. For example, it identifies the location of an item using location information technology such as GPS or Wi-Fi. The location information acquisition unit can also identify the location of an item using Bluetooth signals. For example, the location information acquisition unit identifies the location of an item while out and about using GPS. It identifies the location of an item inside the home using Wi-Fi signals. It identifies the location of an item using Bluetooth signals. The infrared signal analysis unit analyzes infrared signals. For example, it identifies the location of an item using infrared signals. It identifies the location of an item by analyzing the strength and direction of the infrared signals. It triangulates the location by linking multiple infrared sensors. The lost item management unit manages lost items. For example, it registers items that a user often loses and constantly monitors the location of those items. It learns the user's behavior patterns and predicts and notifies the user of times and places where items are likely to be lost. It records the frequency of use and importance of items and prioritizes monitoring of important items. The advice provision unit provides advice. For example, the system provides advice to the user after finding an item. It learns the user's behavioral patterns and provides customized advice to prevent losing items. It analyzes past data to identify situations and times when items are likely to be lost and provides advice based on that. In this way, the lost item management system according to the embodiment reduces the burden on the user by locating and managing lost items and providing advice.

[0030] The location information acquisition unit can use Wi-Fi signals to identify the location of an object. For example, the generation AI analyzes a user's past behavioral patterns to identify places where the user is likely to leave things behind. For example, the location information acquisition unit learns where the user frequently leaves things behind and sends a notification when the user leaves an object in that location. The location information acquisition unit also predicts places where the user is likely to leave things behind based on the user's movement history. For example, if the user tends to leave things behind after staying in a specific location for a long time, the unit sends a notification when the user leaves an object in that location. The location information acquisition unit also learns the user's behavioral patterns and predicts places where the user is likely to leave things behind during certain times of the day. For example, if the user tends to leave things behind during busy morning hours, the unit sends a notification when the user leaves an object during that time of day. This makes it easier to identify the location of objects within the home by using Wi-Fi signals.

[0031] The infrared signal analysis unit can identify the location of an object using an infrared signal. For example, the infrared signal analysis unit records the movement history of an object and estimates its current location based on past location information. For example, it analyzes the movement pattern of the object from the last recorded location and estimates its current location. The infrared signal analysis unit also analyzes the movement history of the object to find a specific pattern. For example, if the object tends to take a specific route, it estimates its current location based on that route. The infrared signal analysis unit also identifies locations where objects are likely to be placed based on the object's movement history. For example, it learns locations where objects are frequently placed and increases the likelihood that the object is located in those locations. This makes it easier to identify the location of a device that emits infrared light by using infrared signals to identify the location of an object.

[0032] The lost item management unit can register items that a user often loses and constantly monitor the location of those items. The lost item management unit, for example, uses an emotion estimation function to evaluate the stress level of a user when they lose something in real time. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The lost item management unit also builds a system that quickly notifies users when their stress level is high. For example, it immediately notifies users when their stress level exceeds a certain threshold. The lost item management unit also continuously monitors the user's stress level based on the emotion estimation data and notifies users when stress increases. For example, it notifies users when their stress level rises. In this way, by registering items that a user often loses and constantly monitoring their location, lost items can be found quickly.

[0033] The advice providing unit can provide advice to the user after finding an item. For example, the advice providing unit constructs a system in which the generation AI learns the user's behavioral patterns and provides customized advice to prevent losing items. For example, the generation AI learns the user's behavioral patterns and provides customized advice to prevent losing items. For example, the generation AI advises the user to put items in a specific location. The advice providing unit also develops an algorithm that provides advice to prevent losing items based on the user's behavioral patterns. For example, the advice providing unit advises the user to avoid places where they are likely to leave items. The advice providing unit also operates a system in real time in which the generation AI analyzes the user's behavioral patterns and provides customized advice to prevent losing items. For example, the advice providing unit provides advice when the user puts down an item. As a result, advice can be provided to the user after they find an item, allowing them to take measures to prevent losing items.

[0034] The location information acquisition unit can determine the location of an object using a GPS signal. In the location information acquisition unit, for example, a generation AI determines the location of an object using a GPS signal. For example, a generation AI builds a system that determines the location of an object while out and about using a GPS signal. For example, the location of an object is determined based on the GPS signal. The location information acquisition unit also develops an algorithm that determines the location of an object based on the GPS signal. For example, the strength of the GPS signal is analyzed to determine the location of an object. The location information acquisition unit also operates a system in real time that determines the location of an object using a generation AI using a GPS signal. For example, the GPS signal is analyzed in real time to determine the location of an object. This makes it easier to determine the location of an object while out and about by using a GPS signal to determine the location of an object.

[0035] The location information acquisition unit can learn the user's behavioral patterns and predict places where items are likely to be left behind and issue a notification. For example, the location information acquisition unit uses a generation AI to learn the user's behavioral patterns and identify places where items are likely to be left behind. For example, the unit can learn places where the user frequently leaves things behind and issue a notification when the user leaves an item in that place. The location information acquisition unit also predicts places where items are likely to be left behind based on the user's movement history. For example, if the user tends to leave things behind after staying in a specific place for a long time, a notification will be issued when the user leaves an item in that place. The location information acquisition unit also learns the user's behavioral patterns and predicts places where items are likely to be left behind during specific times of the day. For example, if the user tends to leave things behind during busy morning hours, a notification will be issued when the user leaves an item during that time of day. In this way, by learning the user's behavioral patterns and predicting places where items are likely to be left behind and issuing a notification, it is possible to prevent items from being left behind.

[0036] The location information acquisition unit records the movement history of an object and can infer its current location from past location information. The location information acquisition unit, for example, records the movement history of an object and infers its current location based on past location information. For example, it analyzes the movement pattern of the object from the last recorded location and infers its current location. The location information acquisition unit also analyzes the movement history of the object to find a specific pattern. For example, if the object tends to take a specific route, it infers its current location based on that route. The location information acquisition unit also identifies locations where objects are likely to be placed based on the object's movement history. For example, it learns locations where objects are frequently placed and increases the likelihood that the object is located in those locations. This makes it easier to identify the location of an object by recording the movement history of the object and inferring its current location from past location information.

[0037] The location information acquisition unit can add a function to centrally manage the locations of all family members' objects and share the information among family members. The location information acquisition unit, for example, builds a system that centrally manages the locations of all family members' objects. For example, it shares the location information of all family members' smartphones and devices and centrally manages the locations of objects. The location information acquisition unit also adds a function to share the location information of objects among family members. For example, it develops an app that allows all family members to check the location of objects in real time. The location information acquisition unit also builds a system that centrally manages the locations of all family members' objects and allows for quick response when an object is lost. For example, it provides a dashboard that allows all family members to check the location of objects. This allows the locations of all family members' objects to be centrally managed and shared among family members, so that all family members can know the location of their objects.

[0038] The location information acquisition unit can display the location of an object on a map, allowing visual confirmation. The location information acquisition unit, for example, builds a system that displays the location of an object on a map. For example, the location of an object can be plotted on a map based on GPS data, allowing visual confirmation. The location information acquisition unit also develops an app that displays the location of an object on a map, allowing a user to easily confirm the location of an object. For example, the location of an object can be updated in real time and displayed on a map. The location information acquisition unit also displays the location of an object on a map, allowing a user to intuitively understand the location of an object. For example, the location of an object can be displayed with an icon or marker, allowing visual confirmation. This makes it easier to identify the location of an object by displaying the location of an object on a map and allowing visual confirmation.

[0039] The infrared signal analysis unit analyzes the strength and direction of infrared signals to determine a more accurate location. The infrared signal analysis unit, for example, analyzes the strength and direction of infrared signals to build a system to determine the location of an object. For example, it measures the distance to an object based on the strength of the infrared signal and analyzes its direction to determine its location. The infrared signal analysis unit also develops an algorithm that uses a generative AI to analyze the strength and direction of infrared signals to determine the location of an object. For example, it uses multiple infrared sensors to analyze the strength and direction of the signal to determine the location. The infrared signal analysis unit also builds a system that analyzes the strength and direction of infrared signals in real time to determine the location of an object. For example, it analyzes fluctuations in infrared signals to track the movement of an object. This improves the accuracy of determining the location of an object by analyzing the strength and direction of the infrared signal to determine a more accurate location.

[0040] The infrared signal analysis unit can triangulate a position by coordinating multiple infrared sensors. For example, the infrared signal analysis unit constructs a system that coordinates multiple infrared sensors to triangulate a position. For example, the principle of triangulation is used to determine the position of an object with high accuracy. The infrared signal analysis unit also develops an algorithm that deploys multiple infrared sensors and analyzes the difference in signal arrival time to determine the position of an object. For example, the position is calculated based on the distance between the sensors and the difference in signal arrival time. The infrared signal analysis unit also operates a system that coordinates multiple infrared sensors to triangulate the position of an object in real time. For example, data from the sensors is analyzed in real time to determine the position of an object. As a result, by coordinating multiple infrared sensors to triangulate a position, the accuracy of determining the position of an object is improved.

[0041] The infrared signal analysis unit can determine the location of an object in combination with wireless communication technology. For example, the infrared signal analysis unit builds a system that determines the location of an object by combining infrared signals and Bluetooth signals. For example, it analyzes both signals to improve the accuracy of location determination. The infrared signal analysis unit also develops an algorithm that determines the location of an object by combining infrared signals and Zigbee signals. For example, it analyzes the strength and direction of both signals to determine the location with high accuracy. The infrared signal analysis unit also operates a system that determines the location of an object in real time by combining infrared signals with other wireless communication technologies. For example, it analyzes multiple signals simultaneously to improve the accuracy of location determination. As a result, the accuracy of location determination is improved by determining the location of an object in combination with other wireless communication technologies.

[0042] The infrared signal analysis unit can analyze signals from tags attached to objects to determine their location. The infrared signal analysis unit, for example, analyzes signals from infrared tags attached to objects and builds a system to determine the location of the objects. For example, the location of an object is determined based on the signal strength from the tag. The infrared signal analysis unit also analyzes signals from infrared tags and develops algorithms to determine the location of objects with high accuracy. For example, the unit analyzes the arrival time and direction of signals from the tag to determine the location. The infrared signal analysis unit also analyzes signals from infrared tags attached to objects in real time and builds a system to determine the location of an object. For example, the unit continuously monitors signals from the tag to determine the location of the object. This makes it easier to determine the location of an object by analyzing signals from tags attached to objects.

[0043] The lost item management unit learns the user's behavioral patterns and can predict and notify the user of times and places where items are likely to be lost. For example, the lost item management unit uses a generation AI to learn the user's behavioral patterns and identify times and places where items are likely to be lost. For example, if a user tends to lose items during a certain time of day, a notification will be sent during that time. The lost item management unit also builds a system that predicts places where items are likely to be lost based on the user's behavioral patterns. For example, if a user tends to lose items in a certain place, a notification will be sent when the user places an item in that place. The lost item management unit also uses a generation AI to analyze the user's behavioral patterns and develop an algorithm that predicts and notifies the user of times and places where items are likely to be lost. For example, the prediction is made based on the user's past behavioral data. This allows the system to learn the user's behavioral patterns and predict and notify the user of times and places where items are likely to be lost, thereby preventing item loss.

[0044] The lost item management unit records the frequency of use and importance of items, and can prioritize monitoring of important items. The lost item management unit, for example, records the frequency of use and importance of items and builds a system that prioritizes monitoring of important items. For example, items that a user uses frequently or important items are registered in a database and monitored as a priority. The lost item management unit also develops an algorithm that prioritizes monitoring of important items based on the frequency of use and importance of items. For example, it prioritizes monitoring of items that are used frequently or that are important. The lost item management unit also records the frequency of use and importance of items and operates a system that prioritizes monitoring of important items in real time. For example, it immediately notifies users when an important item is lost. In this way, by recording the frequency of use and importance of items and monitoring important items as a priority, it is possible to prevent the loss of important items.

[0045] The lost and found management unit can add a function to register the belongings of all family members and share them among family members. For example, the lost and found management unit could register the belongings of all family members in a database and build a system to share them among family members. For example, an app could be developed that allows all family members to check the location of their belongings in real time. The lost and found management unit could also add a function to share the location information of items among family members. For example, it could provide a dashboard that allows all family members to check the location of their items. The lost and found management unit could also register the belongings of all family members in a database and build a system that allows for quick response when an item is lost. For example, it could provide a notification function that allows all family members to check the location of their items. In this way, by registering all family members' belongings and sharing them among family members, all family members can know the location of their items.

[0046] The lost item management unit can register photos and detailed information about items, allowing them to be visually confirmed. For example, the lost item management unit registers photos and detailed information about items in a database, and builds a system that allows them to be visually confirmed. For example, a photo of the item is uploaded and detailed information is entered. The lost item management unit also develops an app that allows the location of an item to be visually confirmed based on the photo and detailed information about the item. For example, the app can display a photo of the item and check the detailed information. The lost item management unit also registers photos and detailed information about the item in a database, and builds a system that allows items to be visually confirmed when they are lost. For example, it provides a notification function that displays a photo of the item and allows the detailed information to be checked. This makes it easier to identify items by registering photos and detailed information about the item and allowing them to be visually confirmed.

[0047] The advice providing unit can learn the user's behavioral patterns and provide customized advice to prevent losing items. For example, the generation AI learns the user's behavioral patterns and provides customized advice to prevent losing items. For example, the generation AI learns the user's behavioral patterns and builds a system that provides customized advice to prevent losing items. For example, the generation AI advises the user to put items in a specific location. The advice providing unit also develops an algorithm that provides advice to prevent losing items based on the user's behavioral patterns. For example, the advice providing unit advises the user to avoid places where they are likely to leave items. The advice providing unit also operates a system in real time in which the generation AI analyzes the user's behavioral patterns and provides customized advice to prevent losing items. For example, the system provides advice when the user puts something down. In this way, the loss of items can be prevented by learning the user's behavioral patterns and providing customized advice to prevent losing items.

[0048] The advice providing unit can analyze past data, identify situations and time periods when items are likely to be lost, and provide advice based on that. For example, the advice providing unit constructs a system in which the generation AI analyzes past data and identifies situations and time periods when items are likely to be lost. For example, if a user tends to lose items during a certain time period, advice is provided for that time period. The advice providing unit also develops an algorithm based on past data to identify situations and time periods when items are likely to be lost. For example, if a user tends to lose items during a certain situation, advice is provided for that situation. The advice providing unit also operates a system in real time in which the generation AI analyzes past data, identifies situations and time periods when items are likely to be lost, and provides advice based on that. For example, advice is provided when a user puts something down. In this way, by analyzing past data, identifying situations and time periods when items are likely to be lost, and providing advice based on that, it is possible to prevent items from being lost.

[0049] The advice providing unit can learn the user's lifestyle habits and provide lifestyle improvement advice to prevent losing things. For example, the advice providing unit uses a generation AI to learn the user's lifestyle habits and provide lifestyle improvement advice to prevent losing things. For example, the generation AI builds a system that learns the user's lifestyle habits and provides lifestyle improvement advice to prevent losing things. For example, the generation AI advises the user to decide where to put things. The advice providing unit also develops an algorithm that provides advice to prevent losing things based on the user's lifestyle habits. For example, the advice providing unit advises the user to avoid places where they are likely to forget things. The advice providing unit also operates a system in real time in which the generation AI analyzes the user's lifestyle habits and provides lifestyle improvement advice to prevent losing things. For example, the advice providing unit provides advice when the user puts something down. In this way, the system learns the user's lifestyle habits and provides lifestyle improvement advice to prevent losing things, thereby preventing the loss of things.

[0050] The advice providing unit can analyze the user's item usage history and provide advice on storage methods and storage locations to prevent items from being lost. For example, the advice providing unit uses a generation AI to analyze the user's item usage history and provide advice on storage methods and storage locations to prevent items from being lost. For example, the generation AI analyzes the user's item usage history and builds a system that provides advice on storage methods and storage locations to prevent items from being lost. For example, the generation AI advises the user to store items that they use frequently in a specific location. The advice providing unit also develops an algorithm that provides advice on storage methods and storage locations to prevent items from being lost based on the user's item usage history. For example, the advice providing unit advises the user to avoid places where they are likely to forget items. The advice providing unit also operates a system in real time that uses a generation AI to analyze the user's item usage history and provide advice on storage methods and storage locations to prevent items from being lost. For example, the advice providing unit provides advice when the user puts down an item. In this way, the loss of items can be prevented by analyzing the user's item usage history and providing advice on storage methods and storage locations to prevent items from being lost.

[0051] The location information acquisition unit can learn the layout of a house, identify places where items are likely to be lost, and issue a notification. For example, the location information acquisition unit constructs a system in which the generation AI learns the layout of a house and identifies places where items are likely to be lost. For example, the system learns places where users frequently lose items and issues a notification when an item is left in that place. The location information acquisition unit also develops an algorithm to identify places where items are likely to be lost based on the layout of a house. For example, if a user tends to lose items in a particular room or area, the system will issue a notification when an item is left in that place. The location information acquisition unit also operates a system in real time in which the generation AI analyzes the layout of a house, identifies places where items are likely to be lost, and issues a notification. For example, the system will issue an alert when a user leaves an item behind. This allows the system to learn the layout of a house, identify places where items are likely to be lost, and issue a notification, preventing item loss.

[0052] The location information acquisition unit can learn the behavioral patterns of all family members and provide advice on how to prevent losing things. For example, the location information acquisition unit uses a generation AI to learn the behavioral patterns of all family members and build a system that provides advice on how to prevent losing things. For example, it identifies places where all family members are likely to put things and advises them to put things in those places. The location information acquisition unit also develops an algorithm that provides advice on how to prevent losing things based on the behavioral patterns of all family members. For example, it advises all family members to avoid places where they are likely to forget things. The location information acquisition unit also operates a system in real time that uses a generation AI to analyze the behavioral patterns of all family members and provide advice on how to prevent losing things. For example, it provides advice when all family members have put something down. In this way, the system can learn the behavioral patterns of all family members and provide advice on how to prevent losing things, thereby preventing loss.

[0053] The location information acquisition unit can identify the location of an object in cooperation with sensors inside the house. For example, the generation AI in the location information acquisition unit builds a system to identify the location of an object in cooperation with temperature sensors and sound sensors inside the house. For example, it identifies the location of an object based on temperature changes and sound generation. The location information acquisition unit also develops an algorithm to identify the location of an object based on data from sensors inside the house. For example, it analyzes data from temperature sensors and sound sensors to identify the location of an object. The location information acquisition unit also operates a system in real time to identify the location of an object in cooperation with sensors inside the house. For example, it analyzes data from sensors in real time to identify the location of an object. This makes it easier to identify the location of an object by linking with sensors inside the house.

[0054] The location information acquisition unit can identify the location of an object in cooperation with smart devices in the home. For example, the location information acquisition unit constructs a system in which the generation AI links with smart speakers and smart lights in the home to identify the location of an object. For example, the location of an object is identified based on voice commands from the smart speaker. The location information acquisition unit also develops an algorithm to identify the location of an object based on data from smart devices in the home. For example, it analyzes the lighting status of smart lights to identify the location of an object. The location information acquisition unit also operates a system in real time in which the generation AI links with smart devices in the home to identify the location of an object. For example, it analyzes data from smart devices in real time to identify the location of an object. This makes it easier to identify the location of an object by linking with smart devices in the home.

[0055] The location information acquisition unit can learn the user's movement history, identify places where items are likely to be lost, and send notifications. For example, the generation AI in the location information acquisition unit learns the user's movement history and builds a system that identifies places where items are likely to be lost. For example, it learns places where the user frequently loses items and sends a notification when the user leaves an item in that place. The location information acquisition unit also develops an algorithm that identifies places where items are likely to be lost based on the user's movement history. For example, if the user tends to lose items in a specific place, it sends a notification when the user leaves an item in that place. The location information acquisition unit also operates a system in real time where the generation AI analyzes the user's movement history and identifies and notifies places where items are likely to be lost. For example, it issues an alert when the user leaves an item. This makes it possible to prevent items from being lost by learning the user's movement history and identifying places where items are likely to be lost and sending a notification.

[0056] The location information acquisition unit can learn the user's behavioral patterns and provide advice to prevent losing items. For example, the location information acquisition unit constructs a system in which a generation AI learns the user's behavioral patterns and provides advice to prevent losing items. For example, it identifies places where the user is likely to put things and advises the user to put things in those places. The location information acquisition unit also develops an algorithm that provides advice to prevent losing items based on the user's behavioral patterns. For example, it advises the user to avoid places where they are likely to forget things. The location information acquisition unit also operates a system in real time in which a generation AI analyzes the user's behavioral patterns and provides advice to prevent losing items. For example, it provides advice when the user puts something down. In this way, the system can learn the user's behavioral patterns and provide advice to prevent losing items, thereby preventing the loss of items.

[0057] The location information acquisition unit can determine the location of an object using public Wi-Fi or Bluetooth signals. For example, the location information acquisition unit constructs a system in which a generation AI determines the location of an object using public Wi-Fi signals. For example, the location of an object is determined based on the location of a public Wi-Fi access point. The location information acquisition unit also develops an algorithm to determine the location of an object based on public Bluetooth signals. For example, the signal strength of a Bluetooth beacon is analyzed to determine the location of an object. The location information acquisition unit also operates a system in real time in which a generation AI determines the location of an object using public Wi-Fi or Bluetooth signals. For example, the system analyzes public Wi-Fi or Bluetooth signals in real time to determine the location of an object. This makes it easier to determine the location of an object while on the go by determining the location of an object using public Wi-Fi or Bluetooth signals.

[0058] The location information acquisition unit can identify the location of an object in cooperation with other users' devices. For example, the location information acquisition unit constructs a system in which the generation AI collaborates with other users' devices to identify the location of an object. For example, the location of an object is identified using signals from other users' smartphones or devices. The location information acquisition unit also develops an algorithm to identify the location of an object based on data from other users' devices. For example, the location information acquisition unit analyzes the signal strength from other users' devices to identify the location of an object. The location information acquisition unit also operates a system in real time in which the generation AI collaborates with other users' devices to identify the location of an object. For example, the system analyzes data from other users' devices in real time to identify the location of an object. This makes it easier to identify the location of an object by collaborating with other users' devices.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The location information acquisition unit can learn the user's behavioral patterns and predict places where the user is likely to leave things behind and issue a notification. For example, it can learn places where the user frequently leaves things behind and issue a notification when the user leaves an item in that place. The location information acquisition unit also predicts places where the user is likely to leave things behind based on the user's movement history. For example, if the user tends to leave things behind after staying in a specific place for a long time, it can issue a notification when the user leaves an item in that place. The location information acquisition unit also learns the user's behavioral patterns and predicts places where the user is likely to leave things behind during specific time periods. For example, if the user tends to leave things behind during busy morning hours, it can issue a notification when the user leaves an item during that time period. In this way, the user's behavioral patterns can be learned, and the user can be predicted and notified when the user leaves things behind, thereby preventing the user from leaving things behind.

[0061] The location information acquisition unit records the movement history of an object and can infer its current location from past location information. For example, it analyzes the movement pattern of the object from the last recorded location to infer its current location. The location information acquisition unit also analyzes the object's movement history to find specific patterns. For example, if an object tends to take a specific route, it infers its current location based on that route. The location information acquisition unit also identifies locations where an object is likely to be placed based on the object's movement history. For example, it learns locations where objects are frequently placed, increasing the likelihood that the object will be found in those locations. This makes it easier to identify the object's location by recording the object's movement history and inferring its current location from past location information.

[0062] The location information acquisition unit can add a function to centrally manage the locations of all family members' objects and share the information among family members. For example, the location information of all family members' smartphones and devices can be shared, and the locations of objects can be centrally managed. The location information acquisition unit also adds a function to share the location information of objects among family members. For example, an app can be developed that allows all family members to check the location of objects in real time. The location information acquisition unit can also centrally manage the locations of all family members' objects, building a system that can respond quickly when an object is lost. For example, a dashboard can be provided that allows all family members to check the location of objects. This allows the locations of all family members' objects to be centrally managed and shared among family members, so that all family members can know the location of their objects.

[0063] The location information acquisition unit can display the location of an object on a map, allowing it to be visually confirmed. For example, the location of an object can be plotted on a map based on GPS data, allowing it to be visually confirmed. The location information acquisition unit can also develop an app that displays the location of an object on a map, allowing the user to easily check the location of an object. For example, the location of an object can be updated in real time and displayed on a map. The location information acquisition unit can also display the location of an object on a map, allowing the user to intuitively understand the location of the object. For example, the location of an object can be displayed with an icon or marker, allowing it to be visually confirmed. This makes it easier to identify the location of an object by displaying the location of the object on a map and allowing it to be visually confirmed.

[0064] The infrared signal analysis unit analyzes the strength and direction of infrared signals to determine a more accurate location. For example, it measures the distance to an object based on the strength of the infrared signal and analyzes its direction to determine its location. The infrared signal analysis unit also develops an algorithm that uses a generative AI to analyze the strength and direction of infrared signals to determine the object's location. For example, it uses multiple infrared sensors to analyze the signal's strength and direction to determine its location. The infrared signal analysis unit also builds a system that analyzes the strength and direction of infrared signals in real time to determine the object's location. For example, it analyzes fluctuations in infrared signals to track the movement of an object. This improves the accuracy of object location by analyzing the strength and direction of infrared signals to determine a more accurate location.

[0065] The infrared signal analysis unit can triangulate the position of multiple infrared sensors by linking them together. For example, it uses the principle of triangulation to pinpoint the position of an object with high accuracy. The infrared signal analysis unit also develops an algorithm that places multiple infrared sensors and analyzes the difference in signal arrival time to pinpoint the position of an object. For example, it calculates the position based on the distance between sensors and the difference in signal arrival time. The infrared signal analysis unit also links multiple infrared sensors to operate a system in real time that triangulates the position of an object. For example, it analyzes data from the sensors in real time to pinpoint the position of an object. As a result, by linking multiple infrared sensors to triangulate the position, the accuracy of pinpointing the position of an object is improved.

[0066] The infrared signal analysis unit can determine the location of an object in combination with wireless communication technology. For example, a system is built that determines the location of an object by combining infrared signals and Bluetooth signals. For example, both signals are analyzed to improve the accuracy of location determination. The infrared signal analysis unit also develops an algorithm that determines the location of an object by combining infrared signals and Zigbee signals. For example, the strength and direction of both signals are analyzed to determine the location with high accuracy. The infrared signal analysis unit also operates a system that determines the location of an object in real time by combining infrared signals with other wireless communication technologies. For example, multiple signals are analyzed simultaneously to improve the accuracy of location determination. As a result, the accuracy of location determination is improved by determining the location of an object in combination with other wireless communication technologies.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The location information acquisition unit acquires location information. For example, the location of an object is determined using location information technologies such as GPS, Wi-Fi, or Bluetooth signals. Specifically, the location of an object is determined using GPS when the object is out, using Wi-Fi signals to determine the location of an object inside the home, and using Bluetooth signals to determine the location of an object. Step 2: The infrared signal analysis unit analyzes the infrared signal. For example, it uses the infrared signal to identify the location of an object, and analyzes the strength and direction of the infrared signal to identify the object's location. It also coordinates multiple infrared sensors to triangulate the location. Step 3: The lost item management unit manages lost items. For example, the user can register items that they often lose and constantly monitor their location. It also learns the user's behavioral patterns, predicts times and places where items are likely to be lost, and notifies the user. It also records the frequency of use and importance of items, and prioritizes monitoring of important items. Step 4: The advice provider provides advice. For example, it provides advice to the user after finding an item. It learns the user's behavioral patterns and provides customized advice to prevent losing items. It analyzes past data to identify situations and times when items are more likely to be lost, and provides advice based on that.

[0069] (Example 2) A lost and found management system according to an embodiment of the present invention is a system for managing lost items targeted at people who are busy with childcare and daily life. This system is designed to solve the problem of unconsciously leaving something behind and then forgetting where you put it. Specifically, it uses location information from inside and outside the home, identifies the location of the item using infrared light, and provides subsequent advice. This allows the lost and found management system to efficiently manage items without losing them, even for people who are busy with childcare and daily life.

[0070] A lost item management system according to an embodiment includes a location information acquisition unit, an infrared signal analysis unit, a lost item management unit, and an advice provision unit. The location information acquisition unit acquires location information. For example, it identifies the location of an item using location information technology such as GPS or Wi-Fi. The location information acquisition unit can also identify the location of an item using Bluetooth signals. For example, the location information acquisition unit identifies the location of an item while out and about using GPS. It identifies the location of an item inside the home using Wi-Fi signals. It identifies the location of an item using Bluetooth signals. The infrared signal analysis unit analyzes infrared signals. For example, it identifies the location of an item using infrared signals. It identifies the location of an item by analyzing the strength and direction of the infrared signals. It triangulates the location by linking multiple infrared sensors. The lost item management unit manages lost items. For example, it registers items that a user often loses and constantly monitors the location of those items. It learns the user's behavior patterns and predicts and notifies the user of times and places where items are likely to be lost. It records the frequency of use and importance of items and prioritizes monitoring of important items. The advice provision unit provides advice. For example, the system provides advice to the user after finding an item. It learns the user's behavioral patterns and provides customized advice to prevent losing items. It analyzes past data to identify situations and times when items are likely to be lost and provides advice based on that. In this way, the lost item management system according to the embodiment reduces the burden on the user by locating and managing lost items and providing advice.

[0071] The location information acquisition unit can use Wi-Fi signals to identify the location of an object. For example, the generation AI analyzes a user's past behavioral patterns to identify places where the user is likely to leave things behind. For example, the location information acquisition unit learns where the user frequently leaves things behind and sends a notification when the user leaves an object in that location. The location information acquisition unit also predicts places where the user is likely to leave things behind based on the user's movement history. For example, if the user tends to leave things behind after staying in a specific location for a long time, the unit sends a notification when the user leaves an object in that location. The location information acquisition unit also learns the user's behavioral patterns and predicts places where the user is likely to leave things behind during certain times of the day. For example, if the user tends to leave things behind during busy morning hours, the unit sends a notification when the user leaves an object during that time of day. This makes it easier to identify the location of objects within the home by using Wi-Fi signals.

[0072] The infrared signal analysis unit can identify the location of an object using an infrared signal. For example, the infrared signal analysis unit records the movement history of an object and estimates its current location based on past location information. For example, it analyzes the movement pattern of the object from the last recorded location and estimates its current location. The infrared signal analysis unit also analyzes the movement history of the object to find a specific pattern. For example, if the object tends to take a specific route, it estimates its current location based on that route. The infrared signal analysis unit also identifies locations where objects are likely to be placed based on the object's movement history. For example, it learns locations where objects are frequently placed and increases the likelihood that the object is located in those locations. This makes it easier to identify the location of a device that emits infrared light by using infrared signals to identify the location of an object.

[0073] The lost item management unit can register items that a user often loses and constantly monitor the location of those items. The lost item management unit, for example, uses an emotion estimation function to evaluate the stress level of a user when they lose something in real time. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The lost item management unit also builds a system that quickly notifies users when their stress level is high. For example, it immediately notifies users when their stress level exceeds a certain threshold. The lost item management unit also continuously monitors the user's stress level based on the emotion estimation data and notifies users when stress increases. For example, it notifies users when their stress level rises. In this way, by registering items that a user often loses and constantly monitoring their location, lost items can be found quickly.

[0074] The advice providing unit can provide advice to the user after finding an item. For example, the advice providing unit constructs a system in which the generation AI learns the user's behavioral patterns and provides customized advice to prevent losing items. For example, the generation AI learns the user's behavioral patterns and provides customized advice to prevent losing items. For example, the generation AI advises the user to put items in a specific location. The advice providing unit also develops an algorithm that provides advice to prevent losing items based on the user's behavioral patterns. For example, the advice providing unit advises the user to avoid places where they are likely to leave items. The advice providing unit also operates a system in real time in which the generation AI analyzes the user's behavioral patterns and provides customized advice to prevent losing items. For example, the advice providing unit provides advice when the user puts down an item. As a result, advice can be provided to the user after they find an item, allowing them to take measures to prevent losing items.

[0075] The location information acquisition unit can determine the location of an object using a GPS signal. In the location information acquisition unit, for example, a generation AI determines the location of an object using a GPS signal. For example, a generation AI builds a system that determines the location of an object while out and about using a GPS signal. For example, the location of an object is determined based on the GPS signal. The location information acquisition unit also develops an algorithm that determines the location of an object based on the GPS signal. For example, the strength of the GPS signal is analyzed to determine the location of an object. The location information acquisition unit also operates a system in real time that determines the location of an object using a generation AI using a GPS signal. For example, the GPS signal is analyzed in real time to determine the location of an object. This makes it easier to determine the location of an object while out and about by using a GPS signal to determine the location of an object.

[0076] The location information acquisition unit can learn the user's behavioral patterns and predict places where items are likely to be left behind and issue a notification. For example, the location information acquisition unit uses a generation AI to learn the user's behavioral patterns and identify places where items are likely to be left behind. For example, the unit can learn places where the user frequently leaves things behind and issue a notification when the user leaves an item in that place. The location information acquisition unit also predicts places where items are likely to be left behind based on the user's movement history. For example, if the user tends to leave things behind after staying in a specific place for a long time, a notification will be issued when the user leaves an item in that place. The location information acquisition unit also learns the user's behavioral patterns and predicts places where items are likely to be left behind during specific times of the day. For example, if the user tends to leave things behind during busy morning hours, a notification will be issued when the user leaves an item during that time of day. In this way, by learning the user's behavioral patterns and predicting places where items are likely to be left behind and issuing a notification, it is possible to prevent items from being left behind.

[0077] The location information acquisition unit records the movement history of an object and can infer its current location from past location information. The location information acquisition unit, for example, records the movement history of an object and infers its current location based on past location information. For example, it analyzes the movement pattern of the object from the last recorded location and infers its current location. The location information acquisition unit also analyzes the movement history of the object to find a specific pattern. For example, if the object tends to take a specific route, it infers its current location based on that route. The location information acquisition unit also identifies locations where objects are likely to be placed based on the object's movement history. For example, it learns locations where objects are frequently placed and increases the likelihood that the object is located in those locations. This makes it easier to identify the location of an object by recording the movement history of the object and inferring its current location from past location information.

[0078] The location information acquisition unit uses the emotion estimation function to evaluate the stress level of the user when they lose something, and can quickly notify them if the stress level is high. The location information acquisition unit, for example, uses the emotion estimation function to evaluate the stress level of the user when they lose something in real time. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The location information acquisition unit also builds a system that quickly notifies the user when the user's stress level is high. For example, it immediately notifies the user when the stress level exceeds a certain threshold. The location information acquisition unit also continuously monitors the user's stress level based on the emotion estimation data and notifies the user when stress increases. For example, it notifies the user when the user's stress level increases. In this way, the user's stress can be reduced by evaluating the user's stress level when they lose something and quickly notifying the user when stress is high.

[0079] The location information acquisition unit can add a function to centrally manage the locations of all family members' objects and share the information among family members. The location information acquisition unit, for example, builds a system that centrally manages the locations of all family members' objects. For example, it shares the location information of all family members' smartphones and devices and centrally manages the locations of objects. The location information acquisition unit also adds a function to share the location information of objects among family members. For example, it develops an app that allows all family members to check the location of objects in real time. The location information acquisition unit also builds a system that centrally manages the locations of all family members' objects and allows for quick response when an object is lost. For example, it provides a dashboard that allows all family members to check the location of objects. This allows the locations of all family members' objects to be centrally managed and shared among family members, so that all family members can know the location of their objects.

[0080] The location information acquisition unit can display the location of an object on a map, allowing visual confirmation. The location information acquisition unit, for example, builds a system that displays the location of an object on a map. For example, the location of an object can be plotted on a map based on GPS data, allowing visual confirmation. The location information acquisition unit also develops an app that displays the location of an object on a map, allowing a user to easily confirm the location of an object. For example, the location of an object can be updated in real time and displayed on a map. The location information acquisition unit also displays the location of an object on a map, allowing a user to intuitively understand the location of an object. For example, the location of an object can be displayed with an icon or marker, allowing visual confirmation. This makes it easier to identify the location of an object by displaying the location of an object on a map and allowing visual confirmation.

[0081] The location information acquisition unit can add a function to use the emotion estimation function to evaluate the emotion of joy felt by a user when they find an object and share that emotion. The location information acquisition unit, for example, uses the emotion estimation function to evaluate the emotion of joy felt by a user when they find an object in real time. For example, it analyzes the user's facial expression and voice and calculates a joy emotion score. The location information acquisition unit also adds a function to share the emotion of joy felt by a user when they find an object. For example, it develops an app that allows users to share the joy emotion score with family and friends. The location information acquisition unit also builds a system that evaluates the emotion of joy felt by a user when they find an object based on the emotion estimation data and shares that emotion. For example, it provides a function that allows users to post the joy emotion score to a social networking site. This allows users to evaluate the emotion of joy felt when they find an object and share that emotion, thereby improving user satisfaction.

[0082] The infrared signal analysis unit analyzes the strength and direction of infrared signals to determine a more accurate location. The infrared signal analysis unit, for example, analyzes the strength and direction of infrared signals to build a system to determine the location of an object. For example, it measures the distance to an object based on the strength of the infrared signal and analyzes its direction to determine its location. The infrared signal analysis unit also develops an algorithm that uses a generative AI to analyze the strength and direction of infrared signals to determine the location of an object. For example, it uses multiple infrared sensors to analyze the strength and direction of the signal to determine the location. The infrared signal analysis unit also builds a system that analyzes the strength and direction of infrared signals in real time to determine the location of an object. For example, it analyzes fluctuations in infrared signals to track the movement of an object. This improves the accuracy of determining the location of an object by analyzing the strength and direction of the infrared signal to determine a more accurate location.

[0083] The infrared signal analysis unit can triangulate a position by coordinating multiple infrared sensors. For example, the infrared signal analysis unit constructs a system that coordinates multiple infrared sensors to triangulate a position. For example, the principle of triangulation is used to determine the position of an object with high accuracy. The infrared signal analysis unit also develops an algorithm that deploys multiple infrared sensors and analyzes the difference in signal arrival time to determine the position of an object. For example, the position is calculated based on the distance between the sensors and the difference in signal arrival time. The infrared signal analysis unit also operates a system that coordinates multiple infrared sensors to triangulate the position of an object in real time. For example, data from the sensors is analyzed in real time to determine the position of an object. As a result, by coordinating multiple infrared sensors to triangulate a position, the accuracy of determining the position of an object is improved.

[0084] The infrared signal analysis unit uses the emotion estimation function to evaluate the user's sense of anxiety when they lose something, and can quickly notify them if the anxiety is high. The infrared signal analysis unit, for example, uses the emotion estimation function to evaluate the user's sense of anxiety when they lose something in real time. For example, it analyzes the user's facial expressions and voice to calculate an anxiety emotion score. The infrared signal analysis unit also builds a system that quickly notifies the user if the user's sense of anxiety is high. For example, it immediately notifies the user if the anxiety emotion score exceeds a certain threshold. The infrared signal analysis unit also continuously monitors the user's sense of anxiety based on the emotion estimation data, and notifies the user if the anxiety increases. For example, it notifies the user if the user's sense of anxiety increases. In this way, the user's sense of anxiety can be reduced by evaluating the user's sense of anxiety when they lose something and quickly notifying the user if the anxiety is high.

[0085] The infrared signal analysis unit can determine the location of an object in combination with wireless communication technology. For example, the infrared signal analysis unit builds a system that determines the location of an object by combining infrared signals and Bluetooth signals. For example, it analyzes both signals to improve the accuracy of location determination. The infrared signal analysis unit also develops an algorithm that determines the location of an object by combining infrared signals and Zigbee signals. For example, it analyzes the strength and direction of both signals to determine the location with high accuracy. The infrared signal analysis unit also operates a system that determines the location of an object in real time by combining infrared signals with other wireless communication technologies. For example, it analyzes multiple signals simultaneously to improve the accuracy of location determination. As a result, the accuracy of location determination is improved by determining the location of an object in combination with other wireless communication technologies.

[0086] The infrared signal analysis unit can analyze signals from tags attached to objects to determine their location. The infrared signal analysis unit, for example, analyzes signals from infrared tags attached to objects and builds a system to determine the location of the objects. For example, the location of an object is determined based on the signal strength from the tag. The infrared signal analysis unit also analyzes signals from infrared tags and develops algorithms to determine the location of objects with high accuracy. For example, the unit analyzes the arrival time and direction of signals from the tag to determine the location. The infrared signal analysis unit also analyzes signals from infrared tags attached to objects in real time and builds a system to determine the location of an object. For example, the unit continuously monitors signals from the tag to determine the location of the object. This makes it easier to determine the location of an object by analyzing signals from tags attached to objects.

[0087] The infrared signal analysis unit can use the emotion estimation function to evaluate the sense of relief felt by a user when they find an object and add a function for sharing that feeling. The infrared signal analysis unit, for example, uses the emotion estimation function to evaluate the sense of relief felt by a user when they find an object in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score for the sense of relief. The infrared signal analysis unit also adds a function for sharing the sense of relief felt by a user when they find an object. For example, it develops an app that allows the sense of relief to be shared with family and friends. The infrared signal analysis unit also builds a system that evaluates the sense of relief felt by a user when they find an object based on the emotion estimation data and shares that emotion. For example, it provides a function that allows the sense of relief to be posted on a social networking site. This allows the user to evaluate the sense of relief felt when they find an object and share that emotion, thereby improving user satisfaction.

[0088] The lost item management unit learns the user's behavioral patterns and can predict and notify the user of times and places where items are likely to be lost. For example, the lost item management unit uses a generation AI to learn the user's behavioral patterns and identify times and places where items are likely to be lost. For example, if a user tends to lose items during a certain time of day, a notification will be sent during that time. The lost item management unit also builds a system that predicts places where items are likely to be lost based on the user's behavioral patterns. For example, if a user tends to lose items in a certain place, a notification will be sent when the user places an item in that place. The lost item management unit also uses a generation AI to analyze the user's behavioral patterns and develop an algorithm that predicts and notifies the user of times and places where items are likely to be lost. For example, the prediction is made based on the user's past behavioral data. This allows the system to learn the user's behavioral patterns and predict and notify the user of times and places where items are likely to be lost, thereby preventing item loss.

[0089] The lost item management unit records the frequency of use and importance of items, and can prioritize monitoring of important items. The lost item management unit, for example, records the frequency of use and importance of items and builds a system that prioritizes monitoring of important items. For example, items that a user uses frequently or important items are registered in a database and monitored as a priority. The lost item management unit also develops an algorithm that prioritizes monitoring of important items based on the frequency of use and importance of items. For example, it prioritizes monitoring of items that are used frequently or that are important. The lost item management unit also records the frequency of use and importance of items and operates a system that prioritizes monitoring of important items in real time. For example, it immediately notifies users when an important item is lost. In this way, by recording the frequency of use and importance of items and monitoring important items as a priority, it is possible to prevent the loss of important items.

[0090] The lost item management unit uses the emotion estimation function to evaluate the emotion a user feels when they lose something and can respond according to that emotion. The lost item management unit, for example, uses the emotion estimation function to evaluate the emotion a user feels when they lose something in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The lost item management unit also builds a system that responds according to the user's emotion. For example, it quickly notifies the user if the user's emotion score is high. The lost item management unit also develops an algorithm that evaluates the emotion a user feels when they lose something and responds according to that emotion based on the emotion estimation data. For example, it adjusts the priority of notifications according to the user's emotion score. In this way, the user's stress can be reduced by evaluating the emotion a user feels when they lose something and responding according to that emotion.

[0091] The lost and found management unit can add a function to register the belongings of all family members and share them among family members. For example, the lost and found management unit could register the belongings of all family members in a database and build a system to share them among family members. For example, an app could be developed that allows all family members to check the location of their belongings in real time. The lost and found management unit could also add a function to share the location information of items among family members. For example, it could provide a dashboard that allows all family members to check the location of their items. The lost and found management unit could also register the belongings of all family members in a database and build a system that allows for quick response when an item is lost. For example, it could provide a notification function that allows all family members to check the location of their items. In this way, by registering all family members' belongings and sharing them among family members, all family members can know the location of their items.

[0092] The lost item management unit can register photos and detailed information about items, allowing them to be visually confirmed. For example, the lost item management unit registers photos and detailed information about items in a database, and builds a system that allows them to be visually confirmed. For example, a photo of the item is uploaded and detailed information is entered. The lost item management unit also develops an app that allows the location of an item to be visually confirmed based on the photo and detailed information about the item. For example, the app can display a photo of the item and check the detailed information. The lost item management unit also registers photos and detailed information about the item in a database, and builds a system that allows items to be visually confirmed when they are lost. For example, it provides a notification function that displays a photo of the item and allows the detailed information to be checked. This makes it easier to identify items by registering photos and detailed information about the item and allowing them to be visually confirmed.

[0093] The lost item management unit can add a function that uses the emotion estimation function to evaluate the emotion a user feels when they find an item and share that emotion. For example, the lost item management unit uses the emotion estimation function to evaluate the emotion a user feels when they find an item in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The lost item management unit also adds a function that allows the user to share the emotion they feel when they find an item. For example, it develops an app that allows the emotion score to be shared with family and friends. The lost item management unit also builds a system that evaluates the emotion a user feels when they find an item based on the emotion estimation data and shares that emotion. For example, it provides a function that allows the emotion score to be posted to a social networking site. This allows the user to evaluate the emotion they feel when they find an item and share that emotion, thereby improving user satisfaction.

[0094] The advice providing unit can learn the user's behavioral patterns and provide customized advice to prevent losing items. For example, the generation AI learns the user's behavioral patterns and provides customized advice to prevent losing items. For example, the generation AI learns the user's behavioral patterns and builds a system that provides customized advice to prevent losing items. For example, the generation AI advises the user to put items in a specific location. The advice providing unit also develops an algorithm that provides advice to prevent losing items based on the user's behavioral patterns. For example, the advice providing unit advises the user to avoid places where they are likely to leave items. The advice providing unit also operates a system in real time in which the generation AI analyzes the user's behavioral patterns and provides customized advice to prevent losing items. For example, the system provides advice when the user puts something down. In this way, the loss of items can be prevented by learning the user's behavioral patterns and providing customized advice to prevent losing items.

[0095] The advice providing unit can analyze past data, identify situations and time periods when items are likely to be lost, and provide advice based on that. For example, the advice providing unit constructs a system in which the generation AI analyzes past data and identifies situations and time periods when items are likely to be lost. For example, if a user tends to lose items during a certain time period, advice is provided for that time period. The advice providing unit also develops an algorithm based on past data to identify situations and time periods when items are likely to be lost. For example, if a user tends to lose items during a certain situation, advice is provided for that situation. The advice providing unit also operates a system in real time in which the generation AI analyzes past data, identifies situations and time periods when items are likely to be lost, and provides advice based on that. For example, advice is provided when a user puts something down. In this way, by analyzing past data, identifying situations and time periods when items are likely to be lost, and providing advice based on that, it is possible to prevent items from being lost.

[0096] The advice providing unit uses the emotion estimation function to provide advice according to the user's emotional state, thereby reducing stress. The advice providing unit, for example, uses the emotion estimation function to build a system that provides advice according to the user's emotional state. For example, if the user is feeling stressed, the advice providing unit provides advice to relax. The advice providing unit also analyzes the user's emotional state in real time and develops an algorithm that provides advice according to the emotion. For example, if the user is feeling anxious, the advice providing unit provides advice to stay calm. The advice providing unit also operates a system in real time that provides advice according to the user's emotional state based on the emotion estimation data. For example, advice to reduce stress is provided when the user loses something. In this way, advice according to the user's emotional state is provided and stress is reduced, thereby reducing the user's psychological burden.

[0097] The advice providing unit can learn the user's lifestyle habits and provide lifestyle improvement advice to prevent losing things. For example, the advice providing unit uses a generation AI to learn the user's lifestyle habits and provide lifestyle improvement advice to prevent losing things. For example, the generation AI builds a system that learns the user's lifestyle habits and provides lifestyle improvement advice to prevent losing things. For example, the generation AI advises the user to decide where to put things. The advice providing unit also develops an algorithm that provides advice to prevent losing things based on the user's lifestyle habits. For example, the advice providing unit advises the user to avoid places where they are likely to forget things. The advice providing unit also operates a system in real time in which the generation AI analyzes the user's lifestyle habits and provides lifestyle improvement advice to prevent losing things. For example, the advice providing unit provides advice when the user puts something down. In this way, the system learns the user's lifestyle habits and provides lifestyle improvement advice to prevent losing things, thereby preventing the loss of things.

[0098] The advice providing unit can analyze the user's item usage history and provide advice on storage methods and storage locations to prevent items from being lost. For example, the advice providing unit uses a generation AI to analyze the user's item usage history and provide advice on storage methods and storage locations to prevent items from being lost. For example, the generation AI analyzes the user's item usage history and builds a system that provides advice on storage methods and storage locations to prevent items from being lost. For example, the generation AI advises the user to store items that they use frequently in a specific location. The advice providing unit also develops an algorithm that provides advice on storage methods and storage locations to prevent items from being lost based on the user's item usage history. For example, the advice providing unit advises the user to avoid places where they are likely to forget items. The advice providing unit also operates a system in real time that uses a generation AI to analyze the user's item usage history and provide advice on storage methods and storage locations to prevent items from being lost. For example, the advice providing unit provides advice when the user puts down an item. In this way, the loss of items can be prevented by analyzing the user's item usage history and providing advice on storage methods and storage locations to prevent items from being lost.

[0099] The advice providing unit can add a function to use the emotion estimation function to evaluate the emotion a user feels when they find an object and share that emotion. The advice providing unit, for example, uses the emotion estimation function to evaluate the emotion a user feels when they find an object in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The advice providing unit also adds a function to share the emotion a user feels when they find an object. For example, it develops an app that allows the emotion score to be shared with family and friends. The advice providing unit also builds a system that evaluates the emotion a user feels when they find an object based on the emotion estimation data and shares that emotion. For example, it provides a function that allows the emotion score to be posted on a social networking site. This allows the user to evaluate the emotion they feel when they find an object and share that emotion, thereby improving user satisfaction.

[0100] The location information acquisition unit can learn the layout of a house, identify places where items are likely to be lost, and issue a notification. For example, the location information acquisition unit constructs a system in which the generation AI learns the layout of a house and identifies places where items are likely to be lost. For example, the system learns places where users frequently lose items and issues a notification when an item is left in that place. The location information acquisition unit also develops an algorithm to identify places where items are likely to be lost based on the layout of a house. For example, if a user tends to lose items in a particular room or area, the system will issue a notification when an item is left in that place. The location information acquisition unit also operates a system in real time in which the generation AI analyzes the layout of a house, identifies places where items are likely to be lost, and issues a notification. For example, the system will issue an alert when a user leaves an item behind. This allows the system to learn the layout of a house, identify places where items are likely to be lost, and issue a notification, preventing item loss.

[0101] The location information acquisition unit can learn the behavioral patterns of all family members and provide advice on how to prevent losing things. For example, the location information acquisition unit uses a generation AI to learn the behavioral patterns of all family members and build a system that provides advice on how to prevent losing things. For example, it identifies places where all family members are likely to put things and advises them to put things in those places. The location information acquisition unit also develops an algorithm that provides advice on how to prevent losing things based on the behavioral patterns of all family members. For example, it advises all family members to avoid places where they are likely to forget things. The location information acquisition unit also operates a system in real time that uses a generation AI to analyze the behavioral patterns of all family members and provide advice on how to prevent losing things. For example, it provides advice when all family members have put something down. In this way, the system can learn the behavioral patterns of all family members and provide advice on how to prevent losing things, thereby preventing loss.

[0102] The location information acquisition unit uses the emotion estimation function to evaluate the emotion when an item is lost at home and can take action according to the emotion. The location information acquisition unit, for example, uses the emotion estimation function to evaluate the emotion when an item is lost at home in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The location information acquisition unit also builds a system that takes action according to the user's emotion. For example, if the user's emotion score is high, it quickly notifies the user. The location information acquisition unit also develops an algorithm that evaluates the emotion when an item is lost at home based on the emotion estimation data and takes action according to the emotion. For example, it adjusts the priority of notifications according to the user's emotion score. In this way, it is possible to reduce the user's stress by evaluating the emotion when an item is lost at home and taking action according to the emotion.

[0103] The location information acquisition unit can identify the location of an object in cooperation with sensors inside the house. For example, the generation AI in the location information acquisition unit builds a system to identify the location of an object in cooperation with temperature sensors and sound sensors inside the house. For example, it identifies the location of an object based on temperature changes and sound generation. The location information acquisition unit also develops an algorithm to identify the location of an object based on data from sensors inside the house. For example, it analyzes data from temperature sensors and sound sensors to identify the location of an object. The location information acquisition unit also operates a system in real time to identify the location of an object in cooperation with sensors inside the house. For example, it analyzes data from sensors in real time to identify the location of an object. This makes it easier to identify the location of an object by linking with sensors inside the house.

[0104] The location information acquisition unit can identify the location of an object in cooperation with smart devices in the home. For example, the location information acquisition unit constructs a system in which the generation AI links with smart speakers and smart lights in the home to identify the location of an object. For example, the location of an object is identified based on voice commands from the smart speaker. The location information acquisition unit also develops an algorithm to identify the location of an object based on data from smart devices in the home. For example, it analyzes the lighting status of smart lights to identify the location of an object. The location information acquisition unit also operates a system in real time in which the generation AI links with smart devices in the home to identify the location of an object. For example, it analyzes data from smart devices in real time to identify the location of an object. This makes it easier to identify the location of an object by linking with smart devices in the home.

[0105] The location information acquisition unit can add a function to use the emotion estimation function to evaluate emotions felt when a user finds an object in the home and share the emotions. The location information acquisition unit, for example, uses the emotion estimation function to evaluate emotions felt when a user finds an object in the home in real time. For example, the location information acquisition unit analyzes the user's facial expressions and voice and calculates an emotion score. The location information acquisition unit also adds a function to share emotions felt when a user finds an object in the home. For example, an app is developed that allows users to share emotion scores with family and friends. The location information acquisition unit also builds a system that evaluates emotions felt when a user finds an object in the home based on emotion estimation data and shares the emotions. For example, a function is provided that allows users to post emotion scores to social media. This allows users to evaluate emotions felt when a user finds an object in the home and share the emotions, thereby improving user satisfaction.

[0106] The location information acquisition unit can learn the user's movement history, identify places where items are likely to be lost, and send notifications. For example, the generation AI in the location information acquisition unit learns the user's movement history and builds a system that identifies places where items are likely to be lost. For example, it learns places where the user frequently loses items and sends a notification when the user leaves an item in that place. The location information acquisition unit also develops an algorithm that identifies places where items are likely to be lost based on the user's movement history. For example, if the user tends to lose items in a specific place, it sends a notification when the user leaves an item in that place. The location information acquisition unit also operates a system in real time where the generation AI analyzes the user's movement history and identifies and notifies places where items are likely to be lost. For example, it issues an alert when the user leaves an item. This makes it possible to prevent items from being lost by learning the user's movement history and identifying places where items are likely to be lost and sending a notification.

[0107] The location information acquisition unit can learn the user's behavioral patterns and provide advice to prevent losing items. For example, the location information acquisition unit constructs a system in which a generation AI learns the user's behavioral patterns and provides advice to prevent losing items. For example, it identifies places where the user is likely to put things and advises the user to put things in those places. The location information acquisition unit also develops an algorithm that provides advice to prevent losing items based on the user's behavioral patterns. For example, it advises the user to avoid places where they are likely to forget things. The location information acquisition unit also operates a system in real time in which a generation AI analyzes the user's behavioral patterns and provides advice to prevent losing items. For example, it provides advice when the user puts something down. In this way, the system can learn the user's behavioral patterns and provide advice to prevent losing items, thereby preventing the loss of items.

[0108] The location information acquisition unit uses the emotion estimation function to evaluate the emotion when an item is lost while out and can take action according to the emotion. The location information acquisition unit, for example, uses the emotion estimation function to evaluate the emotion when an item is lost while out in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The location information acquisition unit also builds a system that takes action according to the user's emotion. For example, if the user's emotion score is high, it quickly notifies the user. The location information acquisition unit also develops an algorithm that evaluates the emotion when an item is lost while out and takes action according to the emotion based on the emotion estimation data. For example, it adjusts the priority of notifications according to the user's emotion score. In this way, it is possible to reduce the user's stress by evaluating the emotion when an item is lost while out and taking action according to the emotion.

[0109] The location information acquisition unit can determine the location of an object using public Wi-Fi or Bluetooth signals. For example, the location information acquisition unit constructs a system in which a generation AI determines the location of an object using public Wi-Fi signals. For example, the location of an object is determined based on the location of a public Wi-Fi access point. The location information acquisition unit also develops an algorithm to determine the location of an object based on public Bluetooth signals. For example, the signal strength of a Bluetooth beacon is analyzed to determine the location of an object. The location information acquisition unit also operates a system in real time in which a generation AI determines the location of an object using public Wi-Fi or Bluetooth signals. For example, the system analyzes public Wi-Fi or Bluetooth signals in real time to determine the location of an object. This makes it easier to determine the location of an object while on the go by determining the location of an object using public Wi-Fi or Bluetooth signals.

[0110] The location information acquisition unit can identify the location of an object in cooperation with other users' devices. For example, the location information acquisition unit constructs a system in which the generation AI collaborates with other users' devices to identify the location of an object. For example, the location of an object is identified using signals from other users' smartphones or devices. The location information acquisition unit also develops an algorithm to identify the location of an object based on data from other users' devices. For example, the location information acquisition unit analyzes the signal strength from other users' devices to identify the location of an object. The location information acquisition unit also operates a system in real time in which the generation AI collaborates with other users' devices to identify the location of an object. For example, the system analyzes data from other users' devices in real time to identify the location of an object. This makes it easier to identify the location of an object by collaborating with other users' devices.

[0111] The location information acquisition unit can add a function to use the emotion estimation function to evaluate emotions when a user finds an object while out and about and share the emotions. The location information acquisition unit, for example, uses the emotion estimation function to evaluate emotions when a user finds an object while out and about in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The location information acquisition unit also adds a function to share emotions when a user finds an object while out and about. For example, it develops an app that allows users to share emotion scores with family and friends. The location information acquisition unit also builds a system that evaluates emotions when a user finds an object while out and about based on emotion estimation data and shares the emotions. For example, it provides a function that allows users to post emotion scores to SNS. This allows users to evaluate emotions when they find an object while out and share the emotions, thereby improving user satisfaction.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The location information acquisition unit can learn the user's behavioral patterns and predict places where the user is likely to leave things behind and issue a notification. For example, it can learn places where the user frequently leaves things behind and issue a notification when the user leaves an item in that place. The location information acquisition unit also predicts places where the user is likely to leave things behind based on the user's movement history. For example, if the user tends to leave things behind after staying in a specific place for a long time, it can issue a notification when the user leaves an item in that place. The location information acquisition unit also learns the user's behavioral patterns and predicts places where the user is likely to leave things behind during specific time periods. For example, if the user tends to leave things behind during busy morning hours, it can issue a notification when the user leaves an item during that time period. In this way, the user's behavioral patterns can be learned, and the user can be predicted and notified when the user leaves things behind, thereby preventing the user from leaving things behind.

[0114] The location information acquisition unit records the movement history of an object and can infer its current location from past location information. For example, it analyzes the movement pattern of the object from the last recorded location to infer its current location. The location information acquisition unit also analyzes the object's movement history to find specific patterns. For example, if an object tends to take a specific route, it infers its current location based on that route. The location information acquisition unit also identifies locations where an object is likely to be placed based on the object's movement history. For example, it learns locations where objects are frequently placed, increasing the likelihood that the object will be found in those locations. This makes it easier to identify the object's location by recording the object's movement history and inferring its current location from past location information.

[0115] The location information acquisition unit uses the emotion estimation function to evaluate the stress level of a user when they lose something, and can quickly notify them if the stress level is high. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The location information acquisition unit also builds a system that quickly notifies the user if the user's stress level is high. For example, it immediately notifies the user if the stress level exceeds a certain threshold. The location information acquisition unit also continuously monitors the user's stress level based on the emotion estimation data, and notifies the user if the stress level increases. For example, it notifies the user if the user's stress level increases. In this way, the user's stress can be reduced by evaluating the user's stress level when they lose something and quickly notifying the user if the stress level is high.

[0116] The location information acquisition unit can add a function to centrally manage the locations of all family members' objects and share the information among family members. For example, the location information of all family members' smartphones and devices can be shared, and the locations of objects can be centrally managed. The location information acquisition unit also adds a function to share the location information of objects among family members. For example, an app can be developed that allows all family members to check the location of objects in real time. The location information acquisition unit can also centrally manage the locations of all family members' objects, building a system that can respond quickly when an object is lost. For example, a dashboard can be provided that allows all family members to check the location of objects. This allows the locations of all family members' objects to be centrally managed and shared among family members, so that all family members can know the location of their objects.

[0117] The location information acquisition unit can display the location of an object on a map, allowing it to be visually confirmed. For example, the location of an object can be plotted on a map based on GPS data, allowing it to be visually confirmed. The location information acquisition unit can also develop an app that displays the location of an object on a map, allowing the user to easily check the location of an object. For example, the location of an object can be updated in real time and displayed on a map. The location information acquisition unit can also display the location of an object on a map, allowing the user to intuitively understand the location of the object. For example, the location of an object can be displayed with an icon or marker, allowing it to be visually confirmed. This makes it easier to identify the location of an object by displaying the location of the object on a map and allowing it to be visually confirmed.

[0118] The location information acquisition unit can use the emotion estimation function to evaluate the joyful emotion felt by the user when they find an object and add a function to share that emotion. For example, the location information acquisition unit can analyze the user's facial expressions and voice to calculate a joyful emotion score. The location information acquisition unit can also add a function to share the joyful emotion felt by the user when they find an object. For example, an app can be developed that allows the joyful emotion score to be shared with family and friends. The location information acquisition unit can also build a system that evaluates the joyful emotion felt by the user when they find an object based on the emotion estimation data and shares that emotion. For example, a function can be provided that allows the joyful emotion score to be posted on a social networking site. This allows the user to evaluate the joyful emotion felt by the user when they find an object and share that emotion, thereby improving user satisfaction.

[0119] The infrared signal analysis unit analyzes the strength and direction of infrared signals to determine a more accurate location. For example, it measures the distance to an object based on the strength of the infrared signal and analyzes its direction to determine its location. The infrared signal analysis unit also develops an algorithm that uses a generative AI to analyze the strength and direction of infrared signals to determine the object's location. For example, it uses multiple infrared sensors to analyze the signal's strength and direction to determine its location. The infrared signal analysis unit also builds a system that analyzes the strength and direction of infrared signals in real time to determine the object's location. For example, it analyzes fluctuations in infrared signals to track the movement of an object. This improves the accuracy of object location by analyzing the strength and direction of infrared signals to determine a more accurate location.

[0120] The infrared signal analysis unit can triangulate the position of multiple infrared sensors by linking them together. For example, it uses the principle of triangulation to pinpoint the position of an object with high accuracy. The infrared signal analysis unit also develops an algorithm that places multiple infrared sensors and analyzes the difference in signal arrival time to pinpoint the position of an object. For example, it calculates the position based on the distance between sensors and the difference in signal arrival time. The infrared signal analysis unit also links multiple infrared sensors to operate a system in real time that triangulates the position of an object. For example, it analyzes data from the sensors in real time to pinpoint the position of an object. As a result, by linking multiple infrared sensors to triangulate the position, the accuracy of pinpointing the position of an object is improved.

[0121] The infrared signal analysis unit uses the emotion estimation function to evaluate the user's feelings of anxiety when they lose something, and can quickly notify them if their anxiety is high. For example, it analyzes the user's facial expressions and voice to calculate an anxiety emotion score. The infrared signal analysis unit also builds a system that quickly notifies the user if their anxiety is high. For example, it immediately notifies the user if the anxiety emotion score exceeds a certain threshold. The infrared signal analysis unit also continuously monitors the user's feelings of anxiety based on the emotion estimation data, and notifies the user if their anxiety increases. For example, it notifies the user if their anxiety increases. In this way, the user's anxiety can be reduced by evaluating the user's feelings of anxiety when they lose something and quickly notifying the user if their anxiety is high.

[0122] The infrared signal analysis unit can determine the location of an object in combination with wireless communication technology. For example, a system is built that determines the location of an object by combining infrared signals and Bluetooth signals. For example, both signals are analyzed to improve the accuracy of location determination. The infrared signal analysis unit also develops an algorithm that determines the location of an object by combining infrared signals and Zigbee signals. For example, the strength and direction of both signals are analyzed to determine the location with high accuracy. The infrared signal analysis unit also operates a system that determines the location of an object in real time by combining infrared signals with other wireless communication technologies. For example, multiple signals are analyzed simultaneously to improve the accuracy of location determination. As a result, the accuracy of location determination is improved by determining the location of an object in combination with other wireless communication technologies.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The location information acquisition unit acquires location information. For example, the location of an object is determined using location information technologies such as GPS, Wi-Fi, or Bluetooth signals. Specifically, the location of an object is determined using GPS when the object is out, using Wi-Fi signals to determine the location of an object inside the home, and using Bluetooth signals to determine the location of an object. Step 2: The infrared signal analysis unit analyzes the infrared signal. For example, it uses the infrared signal to identify the location of an object, and analyzes the strength and direction of the infrared signal to identify the object's location. It also coordinates multiple infrared sensors to triangulate the location. Step 3: The lost item management unit manages lost items. For example, the user can register items that they often lose and constantly monitor their location. It also learns the user's behavioral patterns, predicts times and places where items are likely to be lost, and notifies the user. It also records the frequency of use and importance of items, and prioritizes monitoring of important items. Step 4: The advice provider provides advice. For example, it provides advice to the user after finding an item. It learns the user's behavioral patterns and provides customized advice to prevent losing items. It analyzes past data to identify situations and times when items are more likely to be lost, and provides advice based on that.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 AI 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.

[0142] 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.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 AI 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.

[0157] 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.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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 AI 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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]

[0192] 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 location information acquisition unit that acquires location information; an infrared signal analysis unit that analyzes an infrared signal; The Lost and Found Management Department manages lost and found items, an advice providing unit that provides advice system.

2. The location information acquisition unit Use Wi-Fi signals to locate the object 2. The system of claim 1.

3. The infrared signal analysis unit Use infrared signals to identify the location of the object 2. The system of claim 1.

4. The lost item management unit Register items that users often lose and constantly monitor their location 2. The system of claim 1.

5. The advice providing unit Providing advice to users after finding an object 2. The system of claim 1.

Images