System

Abstract

An object of a system according to an embodiment is to enable a user to easily ask for a little trouble.SOLUTION: A system includes a request reception unit, a content interpretation unit, a notification unit, and a remittance unit. The request reception unit receives a request for each trouble from a user. A content interpretation part interprets the content of each trouble received by the request reception part by the generation AI. The notification unit transmits a notification to an appropriate user on the basis of the content interpreted by the content interpretation unit. When the completion of the help is reported, the remittance part performs remittance from the user to the user.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 technology has had the problem that it is difficult for users to casually ask for help with minor problems, making it difficult for them to receive appropriate support.

[0005] The system according to the embodiment aims to enable users to easily ask for help with minor problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a request receiving unit, a content interpretation unit, a notification unit, and a remittance unit. The request receiving unit receives a request for a problem from a user. The content interpretation unit interprets the content of the problem received by the request receiving unit using a generation AI. The notification unit sends a notification to the appropriate user based on the content interpreted by the content interpretation unit. The remittance unit remits money from one user to another when it is reported that the help has been completed. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily ask for help with minor problems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The problem-solving service system according to an embodiment of the present invention is a system in which when a user presses a button through an electronic payment service, a notification is sent to nearby suitable users, and once the recipient of the notification completes the task, the system transfers money. This allows the user to easily ask for help with minor problems, and nearby suitable users can quickly respond.

[0029] A problem-solving service system according to an embodiment includes a request receiving unit, a content interpretation unit, a notification unit, and a remittance unit. The request receiving unit receives a request for a problem from a user. For example, the user presses a dedicated button in an electronic payment service application to display a screen for inputting the problem details. The content interpretation unit uses a generation AI to interpret the problem details received by the request receiving unit. For example, the generation AI understands the text entered by the user and identifies a user who can respond appropriately based on the text. The generation AI analyzes the request details using a text generation AI (e.g., LLM). The generation AI can also analyze the request details using a multimodal generation AI. The notification unit sends a notification to an appropriate user based on the content interpreted by the content interpretation unit. For example, a notification is sent to a user who is good at shopping or a user who is nearby. The remittance unit transfers money from one user to another when a user reports that a task has been completed. For example, the remittance is automatically performed via an electronic payment service. This allows the problem-solving service system to allow users to easily request help with their problems, and the appropriate user will respond quickly, allowing for smooth remittance.

[0030] The request receiving unit can allow the user to easily input the details of the request using voice input when inputting the problem they are having. For example, when the user presses a voice input button in the application, the request receiving unit automatically converts the details of the request into text using voice recognition technology. For example, by simply speaking, "I need help with shopping," the user can eliminate the need for text input. This allows the user to easily input the details of the request using voice input.

[0031] The request receiving unit can create templates for requests for problems, allowing users to complete the request simply by selecting from options. For example, the request receiving unit can prepare templates for common problems within the application, allowing users to complete the request simply by selecting from options. For example, categories such as "shopping," "moving furniture," and "cleaning" can be selected. This allows users to easily complete requests using the templates.

[0032] The request receiving unit can also enable a user to request a solution to a problem from an IoT device such as a smart watch or a smart speaker. The request receiving unit allows a user to request a solution to a problem using, for example, a smart watch. For example, the user can use the voice assistant function of the smart watch to make a request such as "Please help me with shopping." This allows the user to request a solution to a problem using the IoT device.

[0033] The request receiving unit can add a function to automatically suggest similar requests by referring to past request history when the user inputs the request content. For example, the request receiving unit adds a function to automatically suggest similar requests based on the past request history when the user inputs the request content. For example, the request receiving unit may suggest "Please help me with the same shopping as last time." This allows the user to easily input similar requests based on the past request history.

[0034] When the generation AI interprets the content of a problem, the content interpretation unit refers to the user's past request history and ratings, allowing for more accurate interpretation. For example, when the generation AI interprets the content of a problem, the content interpretation unit refers to the user's past request history and interprets based on similar requests. For example, if a user who previously requested "help with shopping" makes the same request again, the unit can quickly interpret it. This allows for more accurate interpretation by referring to the user's past request history and ratings.

[0035] When interpreting the content of a problem, the content interpretation unit can analyze related images and videos to enable a more specific response. For example, when a user inputs the content of a problem request, the content interpretation unit allows the user to upload related images and videos. For example, in response to a request such as "Please help me move some furniture," a photo of the furniture can be attached. This allows the analysis of related images and videos to enable a more specific response.

[0036] When interpreting the content of a problem, the content interpretation unit can automatically translate request content entered in different languages, thereby achieving multilingual support. For example, the content interpretation unit automatically translates request content entered in different languages ​​by the generation AI and interprets the content of the problem. For example, it translates requests entered in English or Chinese into Japanese. This allows request content entered in different languages ​​to be automatically translated, enabling multilingual support.

[0037] When the generation AI interprets the content of a problem, the content interpretation unit can refer to related external databases (for example, local service information) and suggest a more appropriate response.When the generation AI interprets the content of a problem, the content interpretation unit can refer to local service information and suggest an appropriate response.For example, when the generation AI interprets the content of a problem, the content interpretation unit can refer to local service information and suggest an appropriate response.For example, it can suggest shopping help based on information about nearby supermarkets and pharmacies.This allows it to refer to related external databases and suggest a more appropriate response.

[0038] When the generation AI selects a suitable user, the notification unit takes into consideration the user's past ratings and trustworthiness, and can prioritize the selection of highly reliable users. For example, when the generation AI selects a suitable user, the notification unit refers to the user's past ratings and prioritizes the selection of users who have received high ratings. For example, the notification unit sends notifications to users who have received high ratings in the past. This allows the generation AI to prioritize the selection of highly reliable users.

[0039] The notification unit can send notifications to the appropriate user at the optimal timing, taking into account the user's schedule and current activity status. For example, the generation AI references the user's schedule and sends notifications at the appropriate timing. For example, it sends notifications during times when the user is free. This allows notifications to be sent at the optimal timing, taking into account the user's schedule and activity status.

[0040] The notification unit can improve the delivery rate of notifications by sending notifications to appropriate users through different platforms (e.g., social media or messaging apps). For example, the notification unit sends notifications to appropriate users through social media or messaging apps, such as Facebook or LINE, thereby improving the delivery rate of notifications across different platforms.

[0041] The notification unit allows a user who receives a notification to check the notification content by voice, thereby improving convenience. The notification unit adds a function that allows a user who receives a notification to check the notification content by voice, for example, by using a voice assistant on a smartphone to read out the notification content. This allows the user to check the notification content by voice, improving convenience.

[0042] The remittance unit can add a function to visually display remittance history and detailed information so that the user can easily check the remittance details when remitting money. For example, the remittance unit adds a function to visually display remittance history and detailed information so that the user can easily check the remittance details when remitting money. For example, the remittance history is displayed in a graph or chart. This makes it easier for the user to visually check the remittance details, thereby improving user convenience.

[0043] The remittance unit allows the user to confirm the remittance details by voice when remittance is being carried out, thereby improving convenience. For example, the remittance unit adds a function that allows the user to confirm the remittance details by voice when remittance is being carried out. For example, the remittance details are read out using a smartphone voice assistant. This allows the user to confirm the remittance details by voice, improving convenience.

[0044] The remittance unit enables remittance between different electronic payment services when transferring money, thereby expanding the options available to users. For example, the remittance unit adds a function that enables remittance between different electronic payment services when transferring money. For example, it enables remittance from PayPay to LINE Pay. This enables remittance between different electronic payment services, thereby expanding the options available to users.

[0045] The remittance unit adds a function that allows the user to customize the remittance content when remittance is being carried out, thereby meeting individual needs. The remittance unit adds a function that allows the user to customize the remittance content when remittance is being carried out, for example, by allowing the user to input the reason for remittance and a message. This allows the remittance content to be customized to meet individual needs.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The request receiving unit can allow the user to easily input the details of the request using voice input when inputting the problem they are having. For example, when the user presses the voice input button in the application, the request is automatically converted into text using voice recognition technology. For example, by simply saying "I need help with shopping," the user can eliminate the need for text input. This allows the user to easily input the details of the request using voice input.

[0048] The request receiving unit can create templates for requests for specific problems, allowing users to complete the request simply by selecting from options. For example, an application can provide templates for common problems, allowing users to complete requests simply by selecting from options. For example, categories such as "shopping," "moving furniture," and "cleaning" can be selected. This allows users to easily complete requests using templates.

[0049] The request receiving unit can also allow a user to request a solution to a problem from an IoT device such as a smartwatch or a smart speaker. For example, a user can use a smartwatch to request a solution to a problem. For example, the user can use the voice assistant function of the smartwatch to request help with shopping. This allows the user to request a solution to a problem using an IoT device.

[0050] The request receiving unit can add a function that, when a user inputs a request, refers to the past request history and automatically suggests similar requests. For example, when a user inputs a request, a function is added that automatically suggests similar requests based on the past request history. For example, a suggestion such as "Please help me with the same shopping as last time" is made. This allows the user to easily input similar requests based on the past request history.

[0051] When the generation AI interprets the content of a problem, the content interpretation unit refers to the user's past request history and ratings, allowing for more accurate interpretation. For example, when the generation AI interprets the content of a problem, it refers to the user's past request history and interprets based on similar requests. For example, if a user who previously requested "help with shopping" makes the same request again, it can quickly interpret it. This allows for more accurate interpretation by referring to the user's past request history and ratings.

[0052] When interpreting the content of a problem, the content interpretation unit can analyze related images and videos to enable a more specific response. For example, when a user inputs the content of a problem request, the unit can allow the user to upload related images and videos. For example, in response to a request such as "Please help me move some furniture," a photo of the furniture can be attached. This allows the analysis of related images and videos to enable a more specific response.

[0053] When interpreting the content of a problem, the content interpretation unit can automatically translate request content entered in different languages, enabling multilingual support. For example, the generation AI can automatically translate request content entered in different languages ​​and interpret the content of the problem. For example, it can translate requests entered in English or Chinese into Japanese. This allows request content entered in different languages ​​to be automatically translated, enabling multilingual support.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The request receiving unit receives a request for help from a user. For example, when a user presses a dedicated button in an electronic payment service application, a screen appears where the user can enter the details of the problem. Step 2: The content interpretation unit uses the generation AI to interpret the content of the problem received by the request reception unit. For example, the generation AI understands the text entered by the user and identifies a user who can respond appropriately based on that content. The generation AI analyzes the request content using a text generation AI (e.g., LLM). The generation AI can also analyze the request content using a multimodal generation AI. Step 3: The notification unit sends notifications to appropriate users based on the content interpreted by the content interpretation unit. For example, notifications are sent to users who are good at shopping or who are nearby. Step 4: When the completion of the assistance is reported, the remittance unit remits the money from the user to the other user. For example, the remittance is automatically made through an electronic payment service.

[0056] (Example 2) The problem-solving service system according to an embodiment of the present invention is a system in which when a user presses a button through an electronic payment service, a notification is sent to nearby suitable users, and once the recipient of the notification completes the task, the system transfers money. This allows the user to easily ask for help with minor problems, and nearby suitable users can quickly respond.

[0057] A problem-solving service system according to an embodiment includes a request receiving unit, a content interpretation unit, a notification unit, and a remittance unit. The request receiving unit receives a request for a problem from a user. For example, the user presses a dedicated button in an electronic payment service application to display a screen for inputting the problem details. The content interpretation unit uses a generation AI to interpret the problem details received by the request receiving unit. For example, the generation AI understands the text entered by the user and identifies a user who can respond appropriately based on the text. The generation AI analyzes the request details using a text generation AI (e.g., LLM). The generation AI can also analyze the request details using a multimodal generation AI. The notification unit sends a notification to an appropriate user based on the content interpreted by the content interpretation unit. For example, a notification is sent to a user who is good at shopping or a user who is nearby. The remittance unit transfers money from one user to another when a user reports that a task has been completed. For example, the remittance is automatically performed via an electronic payment service. This allows the problem-solving service system to allow users to easily request help with their problems, and the appropriate user will respond quickly, allowing for smooth remittance.

[0058] The request receiving unit can allow the user to easily input the details of the request using voice input when inputting the problem they are having. For example, when the user presses a voice input button in the application, the request receiving unit automatically converts the details of the request into text using voice recognition technology. For example, by simply speaking, "I need help with shopping," the user can eliminate the need for text input. This allows the user to easily input the details of the request using voice input.

[0059] The request receiving unit can create templates for requests for problems, allowing users to complete the request simply by selecting from options. For example, the request receiving unit can prepare templates for common problems within the application, allowing users to complete the request simply by selecting from options. For example, categories such as "shopping," "moving furniture," and "cleaning" can be selected. This allows users to easily complete requests using the templates.

[0060] The request receiving unit uses the emotion estimation function to analyze the emotional state of the user, and can preferentially select users who can respond particularly quickly when stress is high. For example, when the user inputs a request, the request receiving unit uses a camera or microphone to analyze facial expressions and voice tone to estimate the emotional state. For example, when it is determined that stress is high, the request receiving unit preferentially selects users who can respond quickly. This makes it possible to select users who can respond quickly according to the user's emotional state.

[0061] The request receiving unit can also enable a user to request a solution to a problem from an IoT device such as a smart watch or a smart speaker. The request receiving unit allows a user to request a solution to a problem using, for example, a smart watch. For example, the user can use the voice assistant function of the smart watch to make a request such as "Please help me with shopping." This allows the user to request a solution to a problem using the IoT device.

[0062] The request receiving unit can add a function to automatically suggest similar requests by referring to past request history when the user inputs the request content. For example, the request receiving unit adds a function to automatically suggest similar requests based on the past request history when the user inputs the request content. For example, the request receiving unit may suggest "Please help me with the same shopping as last time." This allows the user to easily input similar requests based on the past request history.

[0063] The request receiving unit can use the emotion estimation function to analyze the emotion of the user when inputting a request in real time and make suggestions to elicit positive emotions. For example, when the user inputs a request, the request receiving unit analyzes the user's facial expression and voice tone using a camera or microphone to analyze the emotional state in real time. For example, if a negative emotion is detected, a positive message is displayed. This allows the user's emotion to be analyzed in real time and suggestions to elicit positive emotions to be made.

[0064] When the generation AI interprets the content of a problem, the content interpretation unit refers to the user's past request history and ratings, allowing for more accurate interpretation. For example, when the generation AI interprets the content of a problem, the content interpretation unit refers to the user's past request history and interprets based on similar requests. For example, if a user who previously requested "help with shopping" makes the same request again, the unit can quickly interpret it. This allows for more accurate interpretation by referring to the user's past request history and ratings.

[0065] When interpreting the content of a problem, the content interpretation unit can analyze related images and videos to enable a more specific response. For example, when a user inputs the content of a problem request, the content interpretation unit allows the user to upload related images and videos. For example, in response to a request such as "Please help me move some furniture," a photo of the furniture can be attached. This allows the analysis of related images and videos to enable a more specific response.

[0066] The content interpretation unit uses the emotion estimation function to analyze the user's emotions regarding the content of the problem and can provide an interpretation based on those emotions. For example, when the generation AI interprets the content of the problem, the content interpretation unit analyzes the user's emotional state and provides an interpretation based on those emotions. For example, if stress is high, it will determine that a prompt response is necessary. This allows the content interpretation unit to analyze the user's emotions and provide an interpretation based on those emotions.

[0067] When interpreting the content of a problem, the content interpretation unit can automatically translate request content entered in different languages, thereby achieving multilingual support. For example, the content interpretation unit automatically translates request content entered in different languages ​​by the generation AI and interprets the content of the problem. For example, it translates requests entered in English or Chinese into Japanese. This allows request content entered in different languages ​​to be automatically translated, enabling multilingual support.

[0068] When the generation AI interprets the content of a problem, the content interpretation unit can refer to related external databases (for example, local service information) and suggest a more appropriate response.When the generation AI interprets the content of a problem, the content interpretation unit can refer to local service information and suggest an appropriate response.For example, when the generation AI interprets the content of a problem, the content interpretation unit can refer to local service information and suggest an appropriate response.For example, it can suggest shopping help based on information about nearby supermarkets and pharmacies.This allows it to refer to related external databases and suggest a more appropriate response.

[0069] The content interpretation unit uses the emotion estimation function to analyze the user's emotions regarding the content of the problem in real time and can provide an interpretation based on emotions. For example, when the generation AI interprets the content of the problem, the content interpretation unit analyzes the user's emotional state in real time and provides an interpretation based on emotions. For example, if stress is high, it will determine that a prompt response is necessary. This allows the user's emotions to be analyzed in real time and an interpretation based on emotions to be provided.

[0070] When the generation AI selects a suitable user, the notification unit takes into consideration the user's past ratings and trustworthiness, and can prioritize the selection of highly reliable users. For example, when the generation AI selects a suitable user, the notification unit refers to the user's past ratings and prioritizes the selection of users who have received high ratings. For example, the notification unit sends notifications to users who have received high ratings in the past. This allows the generation AI to prioritize the selection of highly reliable users.

[0071] The notification unit can send notifications to the appropriate user at the optimal timing, taking into account the user's schedule and current activity status. For example, the generation AI references the user's schedule and sends notifications at the appropriate timing. For example, it sends notifications during times when the user is free. This allows notifications to be sent at the optimal timing, taking into account the user's schedule and activity status.

[0072] The notification unit uses the emotion estimation function to analyze the emotional state of the user receiving the notification and can prioritize users with positive emotions. For example, the notification unit uses a generation AI to analyze the emotional state of the user receiving the notification and prioritize users with positive emotions. For example, notifications are sent to users with high emotion scores. This allows users with positive emotions to be prioritized.

[0073] The notification unit can improve the delivery rate of notifications by sending notifications to appropriate users through different platforms (e.g., social media or messaging apps). For example, the notification unit sends notifications to appropriate users through social media or messaging apps, such as Facebook or LINE, thereby improving the delivery rate of notifications across different platforms.

[0074] The notification unit allows a user who receives a notification to check the notification content by voice, thereby improving convenience. The notification unit adds a function that allows a user who receives a notification to check the notification content by voice, for example, by using a voice assistant on a smartphone to read out the notification content. This allows the user to check the notification content by voice, improving convenience.

[0075] The notification unit uses the emotion estimation function to analyze the emotional state of the user receiving the notification in real time and make suggestions to elicit positive emotions. For example, the notification unit uses a generation AI to analyze the emotional state of the user receiving the notification in real time and make suggestions to elicit positive emotions. For example, it displays an encouraging message. This allows the unit to analyze the user's emotional state in real time and make suggestions to elicit positive emotions.

[0076] The remittance unit can add a function to visually display remittance history and detailed information so that the user can easily check the remittance details when remitting money. For example, the remittance unit adds a function to visually display remittance history and detailed information so that the user can easily check the remittance details when remitting money. For example, the remittance history is displayed in a graph or chart. This makes it easier for the user to visually check the remittance details, thereby improving user convenience.

[0077] The remittance unit allows the user to confirm the remittance details by voice when remittance is being carried out, thereby improving convenience. For example, the remittance unit adds a function that allows the user to confirm the remittance details by voice when remittance is being carried out. For example, the remittance details are read out using a smartphone voice assistant. This allows the user to confirm the remittance details by voice, improving convenience.

[0078] The remittance unit can use the emotion estimation function to analyze the user's emotion when remitting money and encourage users with positive emotions to continue using the service. The remittance unit, for example, uses the emotion estimation function to analyze the user's emotion when remitting money in real time and encourages users with positive emotions to continue using the service. For example, it displays a message of gratitude. This analyzes the user's emotion and encourages users with positive emotions to continue using the service.

[0079] The remittance unit enables remittance between different electronic payment services when transferring money, thereby expanding the options available to users. For example, the remittance unit adds a function that enables remittance between different electronic payment services when transferring money. For example, it enables remittance from PayPay to LINE Pay. This enables remittance between different electronic payment services, thereby expanding the options available to users.

[0080] The remittance unit adds a function that allows the user to customize the remittance content when remittance is being carried out, thereby meeting individual needs. The remittance unit adds a function that allows the user to customize the remittance content when remittance is being carried out, for example, by allowing the user to input the reason for remittance and a message. This allows the remittance content to be customized to meet individual needs.

[0081] The remittance unit can use the emotion estimation function to analyze the user's emotion in real time when remitting money and make suggestions to elicit positive emotions. For example, the remittance unit can use the emotion estimation function to analyze the user's emotion in real time when remitting money and make suggestions to elicit positive emotions. For example, it can display a message of gratitude. This allows the user's emotion to be analyzed in real time and makes suggestions to elicit positive emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The request receiving unit can allow the user to easily input the details of the request using voice input when inputting the problem they are having. For example, when the user presses the voice input button in the application, the request is automatically converted into text using voice recognition technology. For example, by simply saying "I need help with shopping," the user can eliminate the need for text input. This allows the user to easily input the details of the request using voice input.

[0084] The request receiving unit can create templates for requests for specific problems, allowing users to complete the request simply by selecting from options. For example, an application can provide templates for common problems, allowing users to complete requests simply by selecting from options. For example, categories such as "shopping," "moving furniture," and "cleaning" can be selected. This allows users to easily complete requests using templates.

[0085] The request receiving unit uses the emotion estimation function to analyze the emotional state of the user, and can preferentially select users who can respond particularly quickly when stress is high. For example, when the user inputs a request, the emotional state is estimated by analyzing facial expressions and voice tone using a camera or microphone. For example, if it is determined that stress is high, a user who can respond quickly is preferentially selected. This makes it possible to select users who can respond quickly according to the user's emotional state.

[0086] The request receiving unit can also allow a user to request a solution to a problem from an IoT device such as a smartwatch or a smart speaker. For example, a user can use a smartwatch to request a solution to a problem. For example, the user can use the voice assistant function of the smartwatch to request help with shopping. This allows the user to request a solution to a problem using an IoT device.

[0087] The request receiving unit can add a function that, when a user inputs a request, refers to the past request history and automatically suggests similar requests. For example, when a user inputs a request, a function is added that automatically suggests similar requests based on the past request history. For example, a suggestion such as "Please help me with the same shopping as last time" is made. This allows the user to easily input similar requests based on the past request history.

[0088] The request receiving unit can use the emotion estimation function to analyze the emotion of the user when inputting a request in real time and make suggestions to elicit positive emotions. For example, when the user inputs a request, the request receiving unit can analyze the user's facial expression and voice tone using a camera or microphone to analyze the user's emotional state in real time. For example, if a negative emotion is detected, a positive message can be displayed. This allows the user's emotion to be analyzed in real time and suggestions to elicit positive emotions to be made.

[0089] When the generation AI interprets the content of a problem, the content interpretation unit refers to the user's past request history and ratings, allowing for more accurate interpretation. For example, when the generation AI interprets the content of a problem, it refers to the user's past request history and interprets based on similar requests. For example, if a user who previously requested "help with shopping" makes the same request again, it can quickly interpret it. This allows for more accurate interpretation by referring to the user's past request history and ratings.

[0090] When interpreting the content of a problem, the content interpretation unit can analyze related images and videos to enable a more specific response. For example, when a user inputs the content of a problem request, the unit can allow the user to upload related images and videos. For example, in response to a request such as "Please help me move some furniture," a photo of the furniture can be attached. This allows the analysis of related images and videos to enable a more specific response.

[0091] The content interpretation unit uses the emotion estimation function to analyze the user's emotions regarding the content of the problem and can provide an interpretation based on those emotions. For example, when the generation AI interprets the content of the problem, it analyzes the user's emotional state and provides an interpretation based on those emotions. For example, if stress is high, it will determine that a prompt response is necessary. This allows it to analyze the user's emotions and provide an interpretation based on those emotions.

[0092] When interpreting the content of a problem, the content interpretation unit can automatically translate request content entered in different languages, enabling multilingual support. For example, the generation AI can automatically translate request content entered in different languages ​​and interpret the content of the problem. For example, it can translate requests entered in English or Chinese into Japanese. This allows request content entered in different languages ​​to be automatically translated, enabling multilingual support.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The request receiving unit receives a request for help from a user. For example, when a user presses a dedicated button in an electronic payment service application, a screen appears where the user can enter the details of the problem. Step 2: The content interpretation unit uses the generation AI to interpret the content of the problem received by the request reception unit. For example, the generation AI understands the text entered by the user and identifies a user who can respond appropriately based on that content. The generation AI analyzes the request content using a text generation AI (e.g., LLM). The generation AI can also analyze the request content using a multimodal generation AI. Step 3: The notification unit sends notifications to appropriate users based on the content interpreted by the content interpretation unit. For example, notifications are sent to users who are good at shopping or who are nearby. Step 4: When the completion of the assistance is reported, the remittance unit remits the money from the user to the other user. For example, the remittance is automatically made through an electronic payment service.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. When a user presses a button through an electronic payment service, a notification is sent to a suitable nearby user, and the system has a mechanism for transferring money when the user who received the notification completes the task. The request reception department Accepting a request from the user regarding a problem; The content interpretation section interpreting the content of the problem received by the request receiving unit using a generation AI; The notification section sending the notification to the appropriate user based on the content interpreted by the content interpretation unit; The remittance department When the completion of the help is reported, the user transfers the money to the other user. A system characterized by:

2. The request receiving unit When the user inputs the problem, the user can easily input the request details using voice input.

2. The system of claim 1.

3. The request receiving unit The above-mentioned trouble requests can also be made from IoT devices such as smartwatches or smart speakers.

2. The system of claim 1.

4. The content interpretation unit When the generating AI interprets the content of the problem, it refers to the user's past request history and evaluations to make a more accurate interpretation.

2. The system of claim 1.

5. The notification unit When the generation AI selects the suitable user, it takes into consideration the user's past evaluation and reliability, and preferentially selects the user with high reliability.

2. The system of claim 1.

6. The remittance unit Add a function to visually display remittance history and detailed information so that the user can easily check the remittance details when remitting the remittance.

2. The system of claim 1.

7. The request receiving unit Analyzing the emotional state of the user, and preferentially selecting the user who will respond particularly quickly when stress is high 2. The system of claim 1.

8. The content interpretation unit Analyze the user's feelings regarding the content of the problem and perform an interpretation based on the feelings.

2. The system of claim 1.

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