System

Abstract

An object of a system according to an embodiment is to find a lost article with fun and ease and to promote cooperation in the entire community.SOLUTION: A system includes an identification part, a search part, and a cooperation part. The identification unit identifies a lost article. The search unit searches for the lost article identified by the identification unit through the AR game. The cooperation part promotes cooperation in the whole community on the basis of the information searched by the search part.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] Previous technology had the drawback of making the process of finding lost items cumbersome and making it difficult to promote cooperation across the community.

[0005] The system according to the embodiment aims to make finding lost items fun and easy, and to promote cooperation across the community. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a search unit, and a cooperation unit. The identification unit identifies a lost item. The search unit searches for the lost item identified by the identification unit through an AR game. The cooperation unit promotes cooperation across the entire community based on the information searched for by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can make finding lost items fun and easy, and can also promote cooperation across the community. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A lost item search system according to an embodiment of the present invention combines AI technology with AR games to make searching for lost items fun. This system uses AI to instantly identify lost items and makes the search fun through AR games. This makes the task of finding and returning lost items easy and fun, and allows the entire community to work together to solve the problem.

[0029] A lost item search system according to an embodiment includes an identification unit, a search unit, and a collaboration unit. The identification unit identifies lost items. For example, an AI analyzes the image and characteristics of a lost item to instantly identify it. The identification unit can also analyze the characteristics of an object and determine its location. For example, if a photo of an umbrella left behind in a cafe is input to the AI, the AI ​​analyzes the umbrella's characteristics and determines its location within the cafe. The search unit searches for lost items identified by the identification unit through an AR game. For example, a user uses a smartphone or tablet to search for the lost item by following the instructions in the AR game. The AR game can issue instructions such as, "Turn the next corner and look under the table on the left." The collaboration unit promotes cooperation across the entire community based on the information searched by the search unit. For example, if an item is lost in a school or office, everyone can cooperate through the AR game to find it. The generation AI integrates information from multiple users and proposes the optimal search route. This allows the entire community to work together to solve the problem. As a result, the lost item search system according to the embodiment integrates lost item identification, search, and collaboration, enabling efficient and enjoyable lost item search. For example, families and friends can share a fun time by searching for lost items while enjoying an AR game. It can also be used as a team-building activity in schools or offices.

[0030] The identification unit can perform identification taking into account the aging of an object and its usage conditions. For example, when the AI ​​analyzes the characteristics of a lost item, the identification unit takes into account the aging of the object. For example, by analyzing discoloration and the presence or absence of scratches and reflecting the usage conditions of the object, more accurate identification can be achieved. The identification unit also performs identification taking into account the usage conditions of the object. For example, it analyzes the frequency of use and the usage environment to identify the characteristics of the object. This improves the accuracy of identification by taking into account the aging of the object and its usage conditions.

[0031] The identification unit can perform identification by analyzing the behavioral patterns and movement history of the owner of the object. For example, the identification unit uses AI to analyze the behavioral patterns of the owner of the object and predict the location of the lost item. For example, it analyzes the places and routes the owner often visits and identifies the most likely location of the lost item. The identification unit also analyzes the movement history of the owner of the object to perform identification. For example, it analyzes GPS data and past movement records to identify the location of the object. In this way, by analyzing the behavioral patterns and movement history of the owner of the object, the accuracy of identification is improved.

[0032] The identification unit accepts voice input or text input and can analyze the features of an object from multiple angles. For example, when the AI ​​is identifying a lost item, the identification unit accepts voice input. For example, the owner describes the features of the object in voice, and the identification is performed based on that information. The identification unit also accepts text input. For example, the owner enters the features of the object in text, and the identification is performed based on that information. In this way, by accepting voice input or text input, the accuracy of identification is improved.

[0033] The identification unit can provide a platform for sharing the identification results with other users and conducting a joint search. The identification unit provides a platform for sharing, for example, the identification results of a lost item with other users. For example, the identification results can be shared in real time and a joint search can be conducted. The identification unit also provides a platform for conducting a joint search. For example, through a web app or a mobile app, users can share information and cooperate to search for lost items. This enables a joint search by sharing the identification results.

[0034] The exploration unit can provide additional hints or rewards when the user reaches a specific location. For example, in an AR game, the exploration unit provides additional hints when the user reaches a specific location. For example, information about the next exploration point is provided. The exploration unit also provides rewards when the user reaches a specific location. For example, rewards such as points or badges are provided. In this way, by providing additional hints or rewards when the user reaches a specific location, the motivation for exploration is improved.

[0035] The search unit can expand the search range not only indoors but also outdoors, enabling the search for lost items over a wide area. For example, the search unit can expand the search range of an AR game to outdoors, enabling the search for lost items over a wide area. For example, it supports searches in parks and city streets. The search unit also expands the search range indoors and outdoors. For example, it can search not only inside a building or a room, but also an entire city or a specific area. In this way, by expanding the search range indoors and outdoors, the search for lost items over a wide area becomes possible.

[0036] The search unit can implement a multiplayer mode in which users compete or cooperate with other users in real time. The search unit implements a multiplayer mode in which users compete with other users in real time in an AR game, for example. For example, users compete to see who can find a lost item first. The search unit also implements a multiplayer mode in which users cooperate with other users in real time. For example, users can cooperate to search for a lost item while dividing up tasks and sharing information. This increases the enjoyment of the search by competing or cooperating with other users in real time.

[0037] The collaboration unit can analyze each user's areas of expertise and interests and propose optimal division of roles. For example, AI can analyze each user's areas of expertise and propose optimal division of roles. For example, a technically knowledgeable user could be assigned to technical support. The collaboration unit can also analyze each user's interests and propose optimal division of roles. For example, tasks could be assigned based on hobbies and interests. This improves the efficiency of collaboration by analyzing each user's areas of expertise and interests and proposing optimal division of roles.

[0038] The Collaboration Department can analyze the skills and experience of each member and propose the optimal team composition. For example, AI can analyze the skills of each member and propose the optimal team composition. For example, it can combine members with high technical skills with members with high management skills. The Collaboration Department can also analyze the experience of each member and propose the optimal team composition. For example, it can select members based on past project experience and work experience. This improves the efficiency of collaboration by analyzing the skills and experience of each member and proposing the optimal team composition.

[0039] The Cooperation Department can analyze past successes and failures and propose the optimal approach. For example, AI can analyze past successes and propose the optimal approach. For example, it can refer to the methods used in successful projects. The Cooperation Department can also analyze past failures and propose the optimal approach. For example, it can utilize the lessons learned from failed projects. In this way, the efficiency of cooperation can be improved by analyzing past successes and failures and proposing the optimal approach.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The identification unit can perform identification by analyzing the behavioral patterns and movement history of the owner of the object. For example, AI can analyze the behavioral patterns of the owner of the object and predict the location of the lost item. For example, it can analyze the places and routes the owner often visits and identify the most likely location of the lost item. The identification unit can also analyze the movement history of the owner of the object to perform identification. For example, it can analyze GPS data and past movement records to identify the location of the object. In this way, by analyzing the behavioral patterns and movement history of the owner of the object, the accuracy of identification can be improved.

[0042] The exploration unit can provide additional hints or rewards when the user reaches a specific location. For example, in an AR game, additional hints are provided when the user reaches a specific location. For example, information about the next exploration point is provided. The exploration unit also provides rewards when the user reaches a specific location. For example, rewards such as points or badges are provided. This increases the user's motivation to explore by providing additional hints or rewards when the user reaches a specific location.

[0043] The search unit may implement a multiplayer mode in which users compete or cooperate with other users in real time. For example, a multiplayer mode may be implemented in an AR game in which users compete with other users in real time. For example, users may compete to see who can find a lost item first. The search unit may also implement a multiplayer mode in which users cooperate with other users in real time. For example, users may cooperate to search for a lost item while dividing up tasks and sharing information. This allows users to compete or cooperate with other users in real time, making the search more enjoyable.

[0044] The collaboration department can analyze each user's areas of expertise and interests and propose optimal division of roles. For example, AI can analyze each user's areas of expertise and propose optimal division of roles. For example, a technically knowledgeable user could be assigned to technical support. The collaboration department can also analyze each user's interests and propose optimal division of roles. For example, tasks could be assigned based on hobbies and interests. This improves the efficiency of collaboration by analyzing each user's areas of expertise and interests and proposing optimal division of roles.

[0045] The Cooperation Department can analyze past successes and failures and propose the optimal approach. For example, AI can analyze past successes and propose the optimal approach. For example, it can refer to the methods used in successful projects. The Cooperation Department can also analyze past failures and propose the optimal approach. For example, it can utilize the lessons learned from failed projects. This improves the efficiency of cooperation by analyzing past successes and failures and proposing the optimal approach.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The recognition unit identifies the lost item. For example, the AI ​​analyzes the image and characteristics of the lost item and immediately identifies it. The recognition unit can also analyze the characteristics of the object and determine its location. For example, if a photo of an umbrella left behind in a cafe is input into the AI, the AI ​​will analyze the characteristics of the umbrella and determine where it is in the cafe. Step 2: The search unit searches for the lost item identified by the identification unit through the AR game. For example, the user uses a smartphone or tablet to search for the lost item while following the instructions of the AR game. The AR game can give instructions such as, "Turn the next corner and look under the table on the left." Step 3: The Collaboration Unit promotes collaboration across the entire community based on the information discovered by the Search Unit. For example, if an item is lost at a school or office, everyone can work together through an AR game to find it. The Generation AI integrates information from multiple users and proposes the optimal search route. This allows the entire community to come together and solve the problem.

[0048] (Example 2) A lost item search system according to an embodiment of the present invention combines AI technology with AR games to make searching for lost items fun. This system uses AI to instantly identify lost items and makes the search fun through AR games. This makes the task of finding and returning lost items easy and fun, and allows the entire community to work together to solve the problem.

[0049] A lost item search system according to an embodiment includes an identification unit, a search unit, and a collaboration unit. The identification unit identifies lost items. For example, an AI analyzes the image and characteristics of a lost item to instantly identify it. The identification unit can also analyze the characteristics of an object and determine its location. For example, if a photo of an umbrella left behind in a cafe is input to the AI, the AI ​​analyzes the umbrella's characteristics and determines its location within the cafe. The search unit searches for lost items identified by the identification unit through an AR game. For example, a user uses a smartphone or tablet to search for the lost item by following the instructions in the AR game. The AR game can issue instructions such as, "Turn the next corner and look under the table on the left." The collaboration unit promotes cooperation across the entire community based on the information searched by the search unit. For example, if an item is lost in a school or office, everyone can cooperate through the AR game to find it. The generation AI integrates information from multiple users and proposes the optimal search route. This allows the entire community to work together to solve the problem. As a result, the lost item search system according to the embodiment integrates lost item identification, search, and collaboration, enabling efficient and enjoyable lost item search. For example, families and friends can share a fun time by searching for lost items while enjoying an AR game. It can also be used as a team-building activity in schools or offices.

[0050] The identification unit can perform identification taking into account the aging of an object and its usage conditions. For example, when the AI ​​analyzes the characteristics of a lost item, the identification unit takes into account the aging of the object. For example, by analyzing discoloration and the presence or absence of scratches and reflecting the usage conditions of the object, more accurate identification can be achieved. The identification unit also performs identification taking into account the usage conditions of the object. For example, it analyzes the frequency of use and the usage environment to identify the characteristics of the object. This improves the accuracy of identification by taking into account the aging of the object and its usage conditions.

[0051] The identification unit can perform identification by analyzing the behavioral patterns and movement history of the owner of the object. For example, the identification unit uses AI to analyze the behavioral patterns of the owner of the object and predict the location of the lost item. For example, it analyzes the places and routes the owner often visits and identifies the most likely location of the lost item. The identification unit also analyzes the movement history of the owner of the object to perform identification. For example, it analyzes GPS data and past movement records to identify the location of the object. In this way, by analyzing the behavioral patterns and movement history of the owner of the object, the accuracy of identification is improved.

[0052] The identification unit can use the emotion estimation function to prioritize identifying emotionally significant items. The identification unit, for example, uses the emotion estimation function to analyze the emotional state of the owner of the lost item. For example, it prioritizes identifying items that the owner feels are emotionally significant. The identification unit also uses the emotion estimation function to identify emotionally significant items. For example, it prioritizes identifying personal keepsakes, gifts, etc. This prioritizes identifying emotionally significant items, thereby improving user satisfaction.

[0053] The identification unit accepts voice input or text input and can analyze the features of an object from multiple angles. For example, when the AI ​​is identifying a lost item, the identification unit accepts voice input. For example, the owner describes the features of the object in voice, and the identification is performed based on that information. The identification unit also accepts text input. For example, the owner enters the features of the object in text, and the identification is performed based on that information. In this way, by accepting voice input or text input, the accuracy of identification is improved.

[0054] The identification unit can provide a platform for sharing the identification results with other users and conducting a joint search. The identification unit provides a platform for sharing, for example, the identification results of a lost item with other users. For example, the identification results can be shared in real time and a joint search can be conducted. The identification unit also provides a platform for conducting a joint search. For example, through a web app or a mobile app, users can share information and cooperate to search for lost items. This enables a joint search by sharing the identification results.

[0055] The identification unit can use the emotion estimation function to prioritize identifying items that are emotionally valuable and share that information. For example, the identification unit uses the emotion estimation function to identify items that the owner of the lost item finds most emotionally valuable and share that information. For example, the identification unit prioritizes identifying items that the owner feels are most emotionally valuable. The identification unit also identifies items that are emotionally valuable and shares that information. For example, the identification unit prioritizes identifying family photos, souvenirs, etc. and shares that information with other users. In this way, by prioritizing identifying items that are emotionally valuable and sharing that information, user satisfaction is improved.

[0056] The exploration unit can provide additional hints or rewards when the user reaches a specific location. For example, in an AR game, the exploration unit provides additional hints when the user reaches a specific location. For example, information about the next exploration point is provided. The exploration unit also provides rewards when the user reaches a specific location. For example, rewards such as points or badges are provided. In this way, by providing additional hints or rewards when the user reaches a specific location, the motivation for exploration is improved.

[0057] The search unit can use the emotion estimation function to analyze the user's emotional state and provide a gaming experience that elicits positive emotions. The search unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide a gaming experience that elicits positive emotions. For example, it provides additional rewards when the user is having fun. The search unit also analyzes the user's emotional state and provides a gaming experience that elicits positive emotions. For example, it adds elements that allow the user to relax when they are feeling stressed. This improves the gaming experience by analyzing the user's emotional state and eliciting positive emotions.

[0058] The search unit can expand the search range not only indoors but also outdoors, enabling the search for lost items over a wide area. For example, the search unit can expand the search range of an AR game to outdoors, enabling the search for lost items over a wide area. For example, it supports searches in parks and city streets. The search unit also expands the search range indoors and outdoors. For example, it can search not only inside a building or a room, but also an entire city or a specific area. In this way, by expanding the search range indoors and outdoors, the search for lost items over a wide area becomes possible.

[0059] The search unit can implement a multiplayer mode in which users compete or cooperate with other users in real time. The search unit implements a multiplayer mode in which users compete with other users in real time in an AR game, for example. For example, users compete to see who can find a lost item first. The search unit also implements a multiplayer mode in which users cooperate with other users in real time. For example, users can cooperate to search for a lost item while dividing up tasks and sharing information. This increases the enjoyment of the search by competing or cooperating with other users in real time.

[0060] The search unit can use the emotion estimation function to identify the game element that the user enjoys most and enhance that element. For example, the search unit uses the emotion estimation function to identify the game element that the user enjoys most and enhance that element. For example, it adds an element that excites the user. The search unit also identifies the game element that the user enjoys most and enhances that element. For example, it enhances elements such as characters, items, and quests. In this way, the game experience is improved by identifying the game element that the user enjoys most and enhancing that element.

[0061] The collaboration unit can analyze each user's areas of expertise and interests and propose optimal division of roles. For example, AI can analyze each user's areas of expertise and propose optimal division of roles. For example, a technically knowledgeable user could be assigned to technical support. The collaboration unit can also analyze each user's interests and propose optimal division of roles. For example, tasks could be assigned based on hobbies and interests. This improves the efficiency of collaboration by analyzing each user's areas of expertise and interests and proposing optimal division of roles.

[0062] The Collaboration Department can analyze the skills and experience of each member and propose the optimal team composition. For example, AI can analyze the skills of each member and propose the optimal team composition. For example, it can combine members with high technical skills with members with high management skills. The Collaboration Department can also analyze the experience of each member and propose the optimal team composition. For example, it can select members based on past project experience and work experience. This improves the efficiency of collaboration by analyzing the skills and experience of each member and proposing the optimal team composition.

[0063] The Cooperation Department can analyze past successes and failures and propose the optimal approach. For example, AI can analyze past successes and propose the optimal approach. For example, it can refer to the methods used in successful projects. The Cooperation Department can also analyze past failures and propose the optimal approach. For example, it can utilize the lessons learned from failed projects. In this way, the efficiency of cooperation can be improved by analyzing past successes and failures and proposing the optimal approach.

[0064] The cooperation unit can use the emotion estimation function to identify a cooperation method that is most emotionally satisfying for members in the community and recommend that method. For example, the cooperation unit uses the emotion estimation function to identify a cooperation method that is most emotionally satisfying for members in the community and recommends that method. For example, the cooperation unit preferentially suggests tasks that members enjoy. The cooperation unit also uses the emotion estimation function to identify a cooperation method that is most emotionally satisfying for members and recommends that method. For example, the cooperation unit provides an environment in which members can relax. In this way, by using the emotion estimation function to identify a cooperation method that is most emotionally satisfying for members in the community and recommending that method, the efficiency of cooperation is improved.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The identification unit can perform identification by analyzing the behavioral patterns and movement history of the owner of the object. For example, AI can analyze the behavioral patterns of the owner of the object and predict the location of the lost item. For example, it can analyze the places and routes the owner often visits and identify the most likely location of the lost item. The identification unit can also analyze the movement history of the owner of the object to perform identification. For example, it can analyze GPS data and past movement records to identify the location of the object. In this way, by analyzing the behavioral patterns and movement history of the owner of the object, the accuracy of identification can be improved.

[0067] The exploration unit can provide additional hints or rewards when the user reaches a specific location. For example, in an AR game, additional hints are provided when the user reaches a specific location. For example, information about the next exploration point is provided. The exploration unit also provides rewards when the user reaches a specific location. For example, rewards such as points or badges are provided. This increases the user's motivation to explore by providing additional hints or rewards when the user reaches a specific location.

[0068] The search unit may implement a multiplayer mode in which users compete or cooperate with other users in real time. For example, a multiplayer mode may be implemented in an AR game in which users compete with other users in real time. For example, users may compete to see who can find a lost item first. The search unit may also implement a multiplayer mode in which users cooperate with other users in real time. For example, users may cooperate to search for a lost item while dividing up tasks and sharing information. This allows users to compete or cooperate with other users in real time, making the search more enjoyable.

[0069] The collaboration department can analyze each user's areas of expertise and interests and propose optimal division of roles. For example, AI can analyze each user's areas of expertise and propose optimal division of roles. For example, a technically knowledgeable user could be assigned to technical support. The collaboration department can also analyze each user's interests and propose optimal division of roles. For example, tasks could be assigned based on hobbies and interests. This improves the efficiency of collaboration by analyzing each user's areas of expertise and interests and proposing optimal division of roles.

[0070] The Cooperation Department can analyze past successes and failures and propose the optimal approach. For example, AI can analyze past successes and propose the optimal approach. For example, it can refer to the methods used in successful projects. The Cooperation Department can also analyze past failures and propose the optimal approach. For example, it can utilize the lessons learned from failed projects. This improves the efficiency of cooperation by analyzing past successes and failures and proposing the optimal approach.

[0071] The identification unit can use the emotion estimation function to prioritize identifying emotionally significant items. For example, the emotion estimation function is used to analyze the emotional state of the owner of the lost item. For example, items that the owner finds emotionally significant are prioritized for identification. The identification unit also uses the emotion estimation function to identify emotionally significant items. For example, personal keepsakes and gifts are prioritized for identification. This prioritizes identifying emotionally significant items, thereby improving user satisfaction.

[0072] The search unit can use the emotion estimation function to analyze the user's emotional state and provide a gaming experience that elicits positive emotions. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and provide a gaming experience that elicits positive emotions. For example, additional rewards can be provided when the user is having fun. The search unit can also analyze the user's emotional state and provide a gaming experience that elicits positive emotions. For example, an element that allows the user to relax when they are feeling stressed can be added. This improves the gaming experience by analyzing the user's emotional state and eliciting positive emotions.

[0073] The search unit can use the emotion estimation function to identify the game elements that the user enjoys most and enhance those elements. For example, the emotion estimation function can be used to identify the game elements that the user enjoys most and enhance those elements. For example, an element that excites the user can be added. The search unit can also identify the game elements that the user enjoys most and enhance those elements. For example, elements such as characters, items, and quests can be enhanced. In this way, the game experience can be improved by identifying the game elements that the user enjoys most and enhancing those elements.

[0074] The cooperation unit can use the emotion estimation function to identify the cooperation method that is most emotionally satisfying for members in the community and recommend that method. For example, the emotion estimation function can be used to identify the cooperation method that is most emotionally satisfying for members in the community and recommend that method. For example, tasks that members enjoy can be preferentially suggested. The cooperation unit can also use the emotion estimation function to identify the cooperation method that is most emotionally satisfying for members and recommend that method. For example, providing an environment where members can relax. In this way, by using the emotion estimation function to identify the cooperation method that is most emotionally satisfying for members in the community and recommending that method, the efficiency of cooperation is improved.

[0075] The identification unit can use the emotion estimation function to prioritize identifying items that are emotionally valuable and share that information. For example, the emotion estimation function can be used to identify items that the owner of a lost item finds most emotionally valuable and share that information. For example, the identification unit can prioritize identifying items that the owner feels are most emotionally valuable. The identification unit can also identify items that are emotionally valuable and share that information. For example, the identification unit can prioritize identifying family photos, souvenirs, etc. and share that information with other users. In this way, by prioritizing identifying items that are emotionally valuable and sharing that information, user satisfaction can be improved.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The recognition unit identifies the lost item. For example, the AI ​​analyzes the image and characteristics of the lost item and immediately identifies it. The recognition unit can also analyze the characteristics of the object and determine its location. For example, if a photo of an umbrella left behind in a cafe is input into the AI, the AI ​​will analyze the characteristics of the umbrella and determine where it is in the cafe. Step 2: The search unit searches for the lost item identified by the identification unit through the AR game. For example, the user uses a smartphone or tablet to search for the lost item while following the instructions of the AR game. The AR game can give instructions such as, "Turn the next corner and look under the table on the left." Step 3: The Collaboration Unit promotes collaboration across the entire community based on the information discovered by the Search Unit. For example, if an item is lost at a school or office, everyone can work together through an AR game to find it. The Generation AI integrates information from multiple users and proposes the optimal search route. This allows the entire community to come together and solve the problem.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] 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.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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. an identification unit that identifies lost items; a search unit that searches for the lost item identified by the identification unit through an AR game; a cooperation unit that promotes cooperation throughout the community based on the information searched by the search unit. A system characterized by:

2. The identification unit Identifying an object by analyzing its owner's behavioral patterns and movement history 2. The system of claim 1.

3. an identification unit that identifies lost items; a search unit that searches for the lost item identified by the identification unit through an AR game; a cooperation unit that promotes cooperation throughout the community based on the information searched by the search unit. A system characterized by:

4. The Cooperation Department: Analyze each user's areas of expertise and interests and propose optimal role allocation 2. The system of claim 1.

5. The identification unit Prioritize identification of emotionally significant objects 2. The system of claim 1.

6. The search unit Analyze the user's emotional state and provide a gaming experience that elicits positive emotions 2. The system of claim 1.

7. The Cooperation Department: Identify and recommend ways of collaborating that are most emotionally satisfying for members of the community 2. The system of claim 1.

8. The identification unit Prioritize identifying and sharing emotionally valuable information 2. The system of claim 1.

Images