system

Abstract

An object of a system according to an embodiment is to make it easy to make a WEB application for a public service.SOLUTION: A system includes an explanation word conversion unit, a question unit, and an input unit. The descriptive wording conversion unit converts the text into a simple sentence that even a junior high school student can understand by using the generation AI. The question section specifies the URL of the input format and asks the user a question for each input portion. The input part automatically inputs the answer obtained by the question part to the input format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem with online applications for public services, where the explanatory text was complex and it was difficult to enter the required information.

[0005] The system according to the embodiment aims to make it easy to apply for public services online. [Means for solving the problem]

[0006] The system according to the embodiment includes an explanatory text conversion unit, a questioning unit, and an input unit. The explanatory text conversion unit uses a generation AI to convert text into simple sentences that even junior high school students can understand. The questioning unit specifies the URL of the input format and asks the user questions for each input field. The input unit automatically inputs the answers obtained by the questioning unit into the input format. [Effects of the Invention]

[0007] The system according to the embodiment can make it easy to apply for public services online. [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 public service application support system according to an embodiment of the present invention is a system in which a generation AI converts explanatory text into simple sentences that even a junior high school student can understand and explains it to the user when applying online for government-related applications, tax returns, etc. This significantly reduces the effort required to interpret explanatory text and input required information, allowing anyone to easily complete an application.

[0029] A public service application support system according to an embodiment includes an explanatory text conversion unit, a questioning unit, and an input unit. The explanatory text conversion unit uses a generation AI to convert explanatory text for public services, such as government-related applications and tax returns, into simple sentences that even junior high school students can understand. For example, the generation AI analyzes explanatory text for public services, such as government-related applications and tax returns, and simplifies the text, such as "Please fill in your name, address, and date of birth on this application form," to "Please write your name, address, and date of birth on this document." The generation AI also generates simplified sentences based on prompts containing instructions from the user regarding what the generation AI should do. The questioning unit specifies the URL of an input format and asks the user questions for each input field. For example, questions are posed in the form of "Please enter your name" or "Please enter your address." When the user responds, the generation AI automatically fills in the format based on the response. The input unit automatically fills in the input format based on the response obtained by the questioning unit. For example, if the user responds, "My name is Yamada Taro," the input unit enters "Yamada Taro" in the name field. As a result, the public service application support system according to the embodiment significantly reduces the effort required to interpret explanatory text and input required information, allowing anyone to easily complete an application. For example, even people who are not good at filling out application forms can apply smoothly with the support of the generation AI.

[0030] The description conversion unit can learn the user's past application history and generate individually optimized description text. For example, the description conversion unit uses a generation AI to analyze the user's past application history and learn frequently used terms and phrases. For example, it generates optimized description text based on expressions that a specific user has used many times in the past. The description conversion unit also provides more appropriate and easy-to-understand description text based on the user's past application history. For example, it generates description text that is optimal for the user based on expressions used in the past and feedback. This makes it possible to provide more appropriate and easy-to-understand description text based on the user's past application history.

[0031] The explanation conversion unit can analyze the user's real-time reactions and adjust the explanation according to the user's level of understanding. For example, the generation AI analyzes the user's facial expressions using a camera, and if it determines that the user's level of understanding is low, the explanation conversion unit simplifies the explanation. For example, if the user frowns, the explanation is re-explained in simpler terms. The explanation conversion unit also analyzes the user's tone of voice and adjusts the explanation according to the user's level of understanding. For example, if the user speaks in a confused tone, a more detailed explanation is provided. This makes it possible to dynamically adjust the explanation according to the user's level of understanding.

[0032] The explanatory text conversion unit can support different languages ​​and automatically generate explanatory text in multiple languages. For example, the generation AI in the explanatory text conversion unit supports different languages ​​and automatically generates explanatory text in multiple languages. For example, explanatory text is provided in multiple languages ​​such as Japanese, English, and Chinese. The explanatory text conversion unit also generates explanatory text in an appropriate language according to the user's language setting. For example, if the user selects English, explanatory text is provided in English. This multilingual support makes it possible to accommodate users who speak different languages.

[0033] The explanatory text conversion unit can complement explanatory text using visual elements. For example, the generation AI complements explanatory text using visual elements. For example, it may use illustrations to show how to fill out an application form. The explanatory text conversion unit may also complement explanatory text using videos. For example, it may use videos to explain the flow of application procedures. In this way, the use of visual elements helps users understand.

[0034] The question unit can learn the user's input patterns and predict and suggest the information that should be entered next. For example, the question unit uses a generation AI to analyze the user's past input patterns and predict and suggest the information that should be entered next. For example, it suggests the next input item based on address information entered in the past. The question unit also learns the user's input patterns and predicts the information that should be entered next. For example, it suggests the next input item based on information that the user frequently enters. In this way, the efficiency of input work is improved by learning the user's input patterns and predicting and suggesting the information that should be entered next.

[0035] The question unit can analyze the user's voice input and automatically reflect data from the voice in the input format. For example, the question unit uses a generation AI to analyze the user's voice input and automatically reflect data from the voice in the input format. For example, if the user says, "My name is Yamada Taro," it will enter "Yamada Taro" in the name field. The question unit also analyzes the user's voice input using voice recognition technology. For example, it converts the voice into text data and reflects it in the input format. In this way, by analyzing the voice input and automatically reflecting the data in the input format, the effort required for input work is reduced.

[0036] The input unit is compatible with different devices and can support input on multiple devices. For example, the generation AI can be compatible with different devices and support input on multiple devices. For example, it allows input from smartphones and tablets. The input unit also synchronizes data between devices, making input work smoother. For example, input can be started on a smartphone and continued on a tablet. By supporting different devices, users can perform input work from a variety of devices.

[0037] The input unit can analyze a user's handwritten input and automatically convert the handwritten characters into digital data. For example, the input unit uses a generation AI to analyze a user's handwritten input and automatically convert the handwritten characters into digital data. For example, it converts a handwritten name into text data. The input unit also analyzes handwritten input using handwritten character recognition technology. For example, handwritten input is performed using a stylus pen and the characters are converted into digital data. In this way, the effort required for inputting is reduced by analyzing handwritten input and automatically converting it into digital data.

[0038] Furthermore, the public service application support system is equipped with a payment system that learns the user's past payment history and proposes the most appropriate payment method. The payment system, for example, analyzes the user's past payment history and proposes the most appropriate payment method. For example, the proposal is made based on payment methods used in the past. The payment system also proposes payment methods with low fees based on the user's payment history. For example, it proposes the most appropriate payment method for the user, such as credit cards or electronic money. In this way, by proposing the most appropriate payment method based on the user's past payment history, payment convenience is improved.

[0039] Furthermore, the public service application support system includes a payment system that collects user feedback in real time and reflects it in service improvements. The payment system, for example, collects user feedback in real time and reflects it in service improvements. For example, the interface is improved based on user opinions regarding the payment process. The payment system also improves the service based on user feedback. For example, user evaluation comments and survey results are collected and used to improve the service. In this way, user feedback is collected in real time and reflected in service improvements, thereby improving the user experience.

[0040] Furthermore, the public service application support system includes a payment system that supports different electronic payment services and provides a variety of payment options. The payment system supports, for example, different electronic payment services and provides a variety of payment options, such as credit card, bank transfer, and electronic money. The payment system also provides the optimal payment option according to the user's selection. For example, the system allows the user to select the payment method they desire. This makes it possible to support different electronic payment services and provide a variety of payment options to the user.

[0041] Furthermore, the public service application support system includes a payment system, which automatically optimizes a user's points to provide maximum convenience. The payment system, for example, automatically optimizes a user's points to provide maximum convenience, for example, by proposing the most efficient way to use the points. The payment system also proposes the optimal payment method based on the user's points, for example, by proposing a way to make a payment using the points. This makes it possible to provide maximum convenience by optimizing the user's points.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The description conversion unit can learn from a user's past application history and generate individually optimized description text. For example, the generation AI analyzes a user's past application history and learns frequently used terms and phrases. Optimized description text is generated based on expressions that a specific user has used many times in the past. The description conversion unit also provides more appropriate and easy-to-understand description text based on the user's past application history. The optimal description text for the user is generated based on expressions used in the past and feedback. This makes it possible to provide more appropriate and easy-to-understand description text based on the user's past application history.

[0044] The explanatory text conversion unit can support different languages ​​and automatically generate explanatory text in multiple languages. For example, the generation AI can support different languages ​​and automatically generate explanatory text in multiple languages. It provides explanatory text in multiple languages, such as Japanese, English, and Chinese. The explanatory text conversion unit also generates explanatory text in an appropriate language according to the user's language setting. If the user selects English, it provides explanatory text in English. This multilingual support makes it possible to accommodate users who speak different languages.

[0045] The explanatory text conversion unit can complement explanatory text using visual elements. For example, the generation AI can complement explanatory text using visual elements, such as showing diagrams of how to fill out an application form. The explanatory text conversion unit can also complement explanatory text using videos, explaining the flow of the application procedure through video. This helps users understand by using visual elements.

[0046] The question section learns the user's input patterns and can predict and suggest the information that should be entered next. For example, the generation AI analyzes the user's past input patterns and predicts and suggests the information that should be entered next. It suggests the next input item based on address information entered in the past. The question section also learns the user's input patterns and predicts the information that should be entered next. It suggests the next input item based on information that the user frequently enters. In this way, the efficiency of input work is improved by learning the user's input patterns and predicting and suggesting the information that should be entered next.

[0047] The question unit can analyze the user's voice input and automatically reflect data from the voice in the input format. For example, the generation AI analyzes the user's voice input and automatically reflects data from the voice in the input format. If the user says, "My name is Yamada Taro," it will enter "Yamada Taro" in the name field. The question unit also uses voice recognition technology to analyze the user's voice input, converting it into text data and reflecting it in the input format. This reduces the effort required for input work by analyzing voice input and automatically reflecting data in the input format.

[0048] The input unit is compatible with different devices and can support input on multiple devices. For example, the generation AI can be compatible with different devices and support input on multiple devices. It allows input from smartphones and tablets. The input unit also synchronizes data between devices, making input work smoother. Input can be started on a smartphone and continued on a tablet. This allows support for different devices, allowing users to input work from a variety of devices.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The explanatory text conversion unit uses the generation AI to convert the text into simple sentences that even junior high school students can understand. For example, the generation AI analyzes explanatory text for public services such as government applications and tax returns, simplifying the phrase "Please fill in your name, address, and date of birth on this application form" to "Please write your name, address, and date of birth on this document." The generation AI also generates simplified sentences based on prompts containing instructions on what the user wants the generation AI to do. Step 2: The question section specifies the URL of the input format and asks the user questions for each input field. For example, questions such as "Please enter your name" or "Please enter your address" are asked, and when the user responds, the generation AI automatically fills in the format based on the response. Step 3: The input unit automatically inputs the answer obtained by the question unit into the input format. For example, if the user answers "My name is Yamada Taro," the input unit inputs "Yamada Taro" into the name field.

[0051] (Example 2) The public service application support system according to an embodiment of the present invention is a system in which a generation AI converts explanatory text into simple sentences that even a junior high school student can understand and explains it to the user when applying online for government-related applications, tax returns, etc. This significantly reduces the effort required to interpret explanatory text and input required information, allowing anyone to easily complete an application.

[0052] A public service application support system according to an embodiment includes an explanatory text conversion unit, a questioning unit, and an input unit. The explanatory text conversion unit uses a generation AI to convert explanatory text for public services, such as government-related applications and tax returns, into simple sentences that even junior high school students can understand. For example, the generation AI analyzes explanatory text for public services, such as government-related applications and tax returns, and simplifies the text, such as "Please fill in your name, address, and date of birth on this application form," to "Please write your name, address, and date of birth on this document." The generation AI also generates simplified sentences based on prompts containing instructions from the user regarding what the generation AI should do. The questioning unit specifies the URL of an input format and asks the user questions for each input field. For example, questions are posed in the form of "Please enter your name" or "Please enter your address." When the user responds, the generation AI automatically fills in the format based on the response. The input unit automatically fills in the input format based on the response obtained by the questioning unit. For example, if the user responds, "My name is Yamada Taro," the input unit enters "Yamada Taro" in the name field. As a result, the public service application support system according to the embodiment significantly reduces the effort required to interpret explanatory text and input required information, allowing anyone to easily complete an application. For example, even people who are not good at filling out application forms can apply smoothly with the support of the generation AI.

[0053] The description conversion unit can learn the user's past application history and generate individually optimized description text. For example, the description conversion unit uses a generation AI to analyze the user's past application history and learn frequently used terms and phrases. For example, it generates optimized description text based on expressions that a specific user has used many times in the past. The description conversion unit also provides more appropriate and easy-to-understand description text based on the user's past application history. For example, it generates description text that is optimal for the user based on expressions used in the past and feedback. This makes it possible to provide more appropriate and easy-to-understand description text based on the user's past application history.

[0054] The explanation conversion unit can analyze the user's real-time reactions and adjust the explanation according to the user's level of understanding. For example, the generation AI analyzes the user's facial expressions using a camera, and if it determines that the user's level of understanding is low, the explanation conversion unit simplifies the explanation. For example, if the user frowns, the explanation is re-explained in simpler terms. The explanation conversion unit also analyzes the user's tone of voice and adjusts the explanation according to the user's level of understanding. For example, if the user speaks in a confused tone, a more detailed explanation is provided. This makes it possible to dynamically adjust the explanation according to the user's level of understanding.

[0055] The explanatory text conversion unit can use the emotion estimation function to estimate the user's emotional state and generate explanatory text that reduces stress. For example, if the user is feeling stressed, the explanatory text conversion unit uses the emotion estimation function to generate explanatory text that helps the user to relax. For example, it adds encouraging words in a gentle tone. The explanatory text conversion unit also analyzes the user's emotional state and provides explanatory text that helps to reduce stress. For example, if the user is nervous, it uses language that helps the user to relax. This makes it possible to provide explanatory text that reduces stress according to the user's emotional state.

[0056] The explanatory text conversion unit can support different languages ​​and automatically generate explanatory text in multiple languages. For example, the generation AI in the explanatory text conversion unit supports different languages ​​and automatically generates explanatory text in multiple languages. For example, explanatory text is provided in multiple languages ​​such as Japanese, English, and Chinese. The explanatory text conversion unit also generates explanatory text in an appropriate language according to the user's language setting. For example, if the user selects English, explanatory text is provided in English. This multilingual support makes it possible to accommodate users who speak different languages.

[0057] The explanatory text conversion unit can complement explanatory text using visual elements. For example, the generation AI complements explanatory text using visual elements. For example, it may use illustrations to show how to fill out an application form. The explanatory text conversion unit may also complement explanatory text using videos. For example, it may use videos to explain the flow of application procedures. In this way, the use of visual elements helps users understand.

[0058] The explanatory text conversion unit can use the emotion estimation function to present explanatory text at a timing when the user is most relaxed. The explanatory text conversion unit, for example, uses the emotion estimation function to detect a timing when the user is relaxed and presents explanatory text at that timing. For example, the explanation starts after the user takes a deep breath. The explanatory text conversion unit also analyzes the emotional state of the user and provides explanatory text at a timing when the user can relax. For example, the explanation is provided during a time period when the user is relaxed. In this way, by presenting explanatory text at a timing when the user is relaxed, the level of comprehension is improved.

[0059] The question unit can learn the user's input patterns and predict and suggest the information that should be entered next. For example, the question unit uses a generation AI to analyze the user's past input patterns and predict and suggest the information that should be entered next. For example, it suggests the next input item based on address information entered in the past. The question unit also learns the user's input patterns and predicts the information that should be entered next. For example, it suggests the next input item based on information that the user frequently enters. In this way, the efficiency of input work is improved by learning the user's input patterns and predicting and suggesting the information that should be entered next.

[0060] The question unit can analyze the user's voice input and automatically reflect data from the voice in the input format. For example, the question unit uses a generation AI to analyze the user's voice input and automatically reflect data from the voice in the input format. For example, if the user says, "My name is Yamada Taro," it will enter "Yamada Taro" in the name field. The question unit also analyzes the user's voice input using voice recognition technology. For example, it converts the voice into text data and reflects it in the input format. In this way, by analyzing the voice input and automatically reflecting the data in the input format, the effort required for input work is reduced.

[0061] The questioning unit can use the emotion estimation function to provide an interface for reducing the stress the user feels when making input. The questioning unit, for example, uses the emotion estimation function to provide an interface for reducing the stress the user feels when making input. For example, by playing relaxing music. The questioning unit also analyzes the user's emotional state and provides an interface for reducing stress. For example, by adopting an interface design that allows the user to relax. In this way, an interface that reduces the stress the user feels when making input is provided, thereby reducing the burden of input work.

[0062] The input unit is compatible with different devices and can support input on multiple devices. For example, the generation AI can be compatible with different devices and support input on multiple devices. For example, it allows input from smartphones and tablets. The input unit also synchronizes data between devices, making input work smoother. For example, input can be started on a smartphone and continued on a tablet. By supporting different devices, users can perform input work from a variety of devices.

[0063] The input unit can analyze a user's handwritten input and automatically convert the handwritten characters into digital data. For example, the input unit uses a generation AI to analyze a user's handwritten input and automatically convert the handwritten characters into digital data. For example, it converts a handwritten name into text data. The input unit also analyzes handwritten input using handwritten character recognition technology. For example, handwritten input is performed using a stylus pen and the characters are converted into digital data. In this way, the effort required for inputting is reduced by analyzing handwritten input and automatically converting it into digital data.

[0064] The input unit can use the emotion estimation function to send a reminder to encourage the user to perform input work during a time period when the user can best concentrate. For example, the input unit uses the emotion estimation function to identify a time period when the user can best concentrate, and sends a reminder to encourage the user to perform input work during that time period. For example, the input unit sends a reminder during a time period when the user is relaxed. The input unit also analyzes the user's work history to identify a time period when the user can best concentrate. For example, the input unit sends a reminder during an optimal time period based on the user's past work history. This allows the user to perform input work during a time period when the user can best concentrate, thereby achieving efficient input work.

[0065] Furthermore, the public service application support system is equipped with a payment system that learns the user's past payment history and proposes the most appropriate payment method. The payment system, for example, analyzes the user's past payment history and proposes the most appropriate payment method. For example, the proposal is made based on payment methods used in the past. The payment system also proposes payment methods with low fees based on the user's payment history. For example, it proposes the most appropriate payment method for the user, such as credit cards or electronic money. In this way, by proposing the most appropriate payment method based on the user's past payment history, payment convenience is improved.

[0066] Furthermore, the public service application support system includes a payment system that collects user feedback in real time and reflects it in service improvements. The payment system, for example, collects user feedback in real time and reflects it in service improvements. For example, the interface is improved based on user opinions regarding the payment process. The payment system also improves the service based on user feedback. For example, user evaluation comments and survey results are collected and used to improve the service. In this way, user feedback is collected in real time and reflected in service improvements, thereby improving the user experience.

[0067] Furthermore, the public service application support system includes a payment system, which uses an emotion estimation function to provide support messages to alleviate the anxiety the user feels about making a payment. The payment system, for example, uses the emotion estimation function to provide support messages to alleviate the anxiety the user feels about making a payment. For example, the payment system notifies the user of the progress of the payment procedure step by step. The payment system also analyzes the user's emotional state and provides support messages to alleviate the anxiety. For example, the payment system provides words of encouragement or specific advice. In this way, the support messages that alleviate the anxiety the user feels about making a payment are provided, thereby improving the sense of security when making a payment.

[0068] Furthermore, the public service application support system includes a payment system that supports different electronic payment services and provides a variety of payment options. The payment system supports, for example, different electronic payment services and provides a variety of payment options, such as credit card, bank transfer, and electronic money. The payment system also provides the optimal payment option according to the user's selection. For example, the system allows the user to select the payment method they desire. This makes it possible to support different electronic payment services and provide a variety of payment options to the user.

[0069] Furthermore, the public service application support system includes a payment system, which automatically optimizes a user's points to provide maximum convenience. The payment system, for example, automatically optimizes a user's points to provide maximum convenience, for example, by proposing the most efficient way to use the points. The payment system also proposes the optimal payment method based on the user's points, for example, by proposing a way to make a payment using the points. This makes it possible to provide maximum convenience by optimizing the user's points.

[0070] Furthermore, the public service application support system includes a payment system, which uses an emotion estimation function to send a payment request at a timing when the user feels most comfortable making a payment. The payment system, for example, uses the emotion estimation function to identify a timing when the user feels most comfortable making a payment and sends the payment request at that timing. For example, the request is sent during a time period when the user is relaxed. The payment system also analyzes the user's emotional state and sends the request at a timing when the user feels most comfortable making a payment. For example, the payment request is sent during a time period when the user is relaxed. This improves the user's sense of security when making a payment by sending the payment request at a timing when the user feels most comfortable making a payment.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The description conversion unit can learn from a user's past application history and generate individually optimized description text. For example, the generation AI analyzes a user's past application history and learns frequently used terms and phrases. Optimized description text is generated based on expressions that a specific user has used many times in the past. The description conversion unit also provides more appropriate and easy-to-understand description text based on the user's past application history. The optimal description text for the user is generated based on expressions used in the past and feedback. This makes it possible to provide more appropriate and easy-to-understand description text based on the user's past application history.

[0073] The explanation conversion unit can analyze the user's real-time reactions and adjust the explanation according to their level of understanding. For example, the generation AI can analyze the user's facial expressions with a camera and simplify the explanation if it determines that their level of understanding is low. If the user frowns, it will explain again in simpler terms. The explanation conversion unit also analyzes the user's tone of voice and adjusts the explanation according to their level of understanding. If the user speaks in a confused tone, it will provide a more detailed explanation. This makes it possible to dynamically adjust the explanation according to the user's level of understanding.

[0074] The explanatory text conversion unit can use the emotion estimation function to estimate the user's emotional state and generate explanatory text that reduces stress. For example, if the user is feeling stressed, the emotion estimation function can be used to generate explanatory text that helps the user to relax. Words of encouragement can be added in a gentle tone. The explanatory text conversion unit can also analyze the user's emotional state and provide explanatory text that helps the user to reduce stress. If the user is nervous, it can use language that helps the user to relax. This makes it possible to provide explanatory text that reduces stress according to the user's emotional state.

[0075] The explanatory text conversion unit can support different languages ​​and automatically generate explanatory text in multiple languages. For example, the generation AI can support different languages ​​and automatically generate explanatory text in multiple languages. It provides explanatory text in multiple languages, such as Japanese, English, and Chinese. The explanatory text conversion unit also generates explanatory text in an appropriate language according to the user's language setting. If the user selects English, it provides explanatory text in English. This multilingual support makes it possible to accommodate users who speak different languages.

[0076] The explanatory text conversion unit can complement explanatory text using visual elements. For example, the generation AI can complement explanatory text using visual elements, such as showing diagrams of how to fill out an application form. The explanatory text conversion unit can also complement explanatory text using videos, explaining the flow of the application procedure through video. This helps users understand by using visual elements.

[0077] The explanatory text conversion unit can use the emotion estimation function to present explanatory text at the timing when the user is most relaxed. For example, the emotion estimation function is used to detect when the user is relaxed and present explanatory text at that timing. The explanation begins after the user takes a deep breath. The explanatory text conversion unit also analyzes the user's emotional state and provides explanatory text at a timing when the user can relax. The explanation is provided during a time period when the user is relaxed. In this way, by presenting explanatory text at a timing when the user is relaxed, the user's level of understanding is improved.

[0078] The question section learns the user's input patterns and can predict and suggest the information that should be entered next. For example, the generation AI analyzes the user's past input patterns and predicts and suggests the information that should be entered next. It suggests the next input item based on address information entered in the past. The question section also learns the user's input patterns and predicts the information that should be entered next. It suggests the next input item based on information that the user frequently enters. In this way, the efficiency of input work is improved by learning the user's input patterns and predicting and suggesting the information that should be entered next.

[0079] The question unit can analyze the user's voice input and automatically reflect data from the voice in the input format. For example, the generation AI analyzes the user's voice input and automatically reflects data from the voice in the input format. If the user says, "My name is Yamada Taro," it will enter "Yamada Taro" in the name field. The question unit also uses voice recognition technology to analyze the user's voice input, converting it into text data and reflecting it in the input format. This reduces the effort required for input work by analyzing voice input and automatically reflecting data in the input format.

[0080] The questioning unit can use the emotion estimation function to provide an interface for reducing the stress the user feels when making input. For example, the emotion estimation function can be used to provide an interface for reducing the stress the user feels when making input. Relaxing music can be played. The questioning unit can also analyze the user's emotional state and provide an interface for reducing stress. An interface design that allows the user to relax can be adopted. This provides an interface that reduces the stress the user feels when making input, thereby reducing the burden of input work.

[0081] The input unit is compatible with different devices and can support input on multiple devices. For example, the generation AI can be compatible with different devices and support input on multiple devices. It allows input from smartphones and tablets. The input unit also synchronizes data between devices, making input work smoother. Input can be started on a smartphone and continued on a tablet. This allows support for different devices, allowing users to input work from a variety of devices.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The explanatory text conversion unit uses the generation AI to convert the text into simple sentences that even junior high school students can understand. For example, the generation AI analyzes explanatory text for public services such as government applications and tax returns, simplifying the phrase "Please fill in your name, address, and date of birth on this application form" to "Please write your name, address, and date of birth on this document." The generation AI also generates simplified sentences based on prompts containing instructions on what the user wants the generation AI to do. Step 2: The question section specifies the URL of the input format and asks the user questions for each input field. For example, questions such as "Please enter your name" or "Please enter your address" are asked, and when the user responds, the generation AI automatically fills in the format based on the response. Step 3: The input unit automatically inputs the answer obtained by the question unit into the input format. For example, if the user answers "My name is Yamada Taro," the input unit inputs "Yamada Taro" into the name field.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 explanatory text conversion part uses generative AI to convert the text into simple sentences that even junior high school students can understand, and A question section that specifies the URL of the input format and asks the user questions for each input field; an input unit that automatically inputs the answer obtained by the question unit into an input format; A system characterized by:

2. The description text conversion unit It learns the user's past application history and generates individually optimized explanations.

2. The system of claim 1.

3. The description text conversion unit Supports different languages ​​and automatically generates multilingual descriptions 2. The system of claim 1.

4. The interrogation unit Learn the user's input patterns and predict and suggest the next information to be entered.

2. The system of claim 1.

5. The input unit Supports different devices and multi-device input 2. The system of claim 1.

6. The payment system is Learn the user's past payment history and suggest the optimal payment method 2. The system of claim 1.

7. The description text conversion unit Estimating the emotional state of the user and generating explanatory text that reduces stress 2. The system of claim 1.

8. The interrogation unit To provide an interface for reducing the stress felt by the user in response to input 2. The system of claim 1.

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